model study in research

Modeling in Scientific Research: Simplifying a system to make predictions

by Anne E. Egger, Ph.D., Anthony Carpi, Ph.D.

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Did you know that scientific models can help us peer inside the tiniest atom or examine the entire universe in a single glance? Models allow scientists to study things too small to see, and begin to understand things too complex to imagine.

Modeling involves developing physical, conceptual, or computer-based representations of systems.

Scientists build models to replicate systems in the real world through simplification, to perform an experiment that cannot be done in the real world, or to assemble several known ideas into a coherent whole to build and test hypotheses.

Computer modeling is a relatively new scientific research method, but it is based on the same principles as physical and conceptual modeling.

LEGO ® bricks have been a staple of the toy world since they were first manufactured in Denmark in 1953. The interlocking plastic bricks can be assembled into an endless variety of objects (see Figure 1). Some kids (and even many adults) are interested in building the perfect model – finding the bricks of the right color, shape, and size, and assembling them into a replica of a familiar object in the real world, like a castle, the space shuttle , or London Bridge. Others focus on using the object they build – moving LEGO knights in and out of the castle shown in Figure 1, for example, or enacting a space shuttle mission to Mars. Still others may have no particular end product in mind when they start snapping bricks together and just want to see what they can do with the pieces they have.

Figure 1 : On the left, individual LEGO® bricks. On the right, a model of a NASA space center built with LEGO bricks.

On the most basic level, scientists use models in much the same way that people play with LEGO bricks. Scientific models may or may not be physical entities, but scientists build them for the same variety of reasons: to replicate systems in the real world through simplification, to perform an experiment that cannot be done in the real world, or to assemble several known ideas into a coherent whole to build and test hypotheses .

• Types of models: Physical, conceptual, mathematical

At the St. Anthony Falls Laboratory at the University of Minnesota, a group of engineers and geologists have built a room-sized physical replica of a river delta to model a real one like the Mississippi River delta in the Gulf of Mexico (Paola et al., 2001). These researchers have successfully incorporated into their model the key processes that control river deltas (like the variability of water flow, the deposition of sediments transported by the river, and the compaction and subsidence of the coastline under the pressure of constant sediment additions) in order to better understand how those processes interact. With their physical model, they can mimic the general setting of the Mississippi River delta and then do things they can't do in the real world, like take a slice through the resulting sedimentary deposits to analyze the layers within the sediments. Or they can experiment with changing parameters like sea level and sedimentary input to see how those changes affect deposition of sediments within the delta, the same way you might "experiment" with the placement of the knights in your LEGO castle.

Figure 2 : A photograph of the St. Anthony Falls lab river delta model, showing the experimental setup with pink-tinted water flowing over sediments. Image courtesy the National Center for Earth-Surface Dynamics Data Repository http://www.nced.umn.edu [accessed September, 2008]

Not all models used in scientific research are physical models. Some are conceptual, and involve assembling all of the known components of a system into a coherent whole. This is a little like building an abstract sculpture out of LEGO bricks rather than building a castle. For example, over the past several hundred years, scientists have developed a series of models for the structure of an atom . The earliest known model of the atom compared it to a billiard ball, reflecting what scientists knew at the time – they were the smallest piece of an element that maintained the properties of that element. Despite the fact that this was a purely conceptual model, it could be used to predict some of the behavior that atoms exhibit. However, it did not explain all of the properties of atoms accurately. With the discovery of subatomic particles like the proton and electron , the physicist Ernest Rutherford proposed a "solar system" model of the atom, in which electrons orbited around a nucleus that included protons (see our Atomic Theory I: The Early Days module for more information). While the Rutherford model is useful for understanding basic properties of atoms, it eventually proved insufficient to explain all of the behavior of atoms. The current quantum model of the atom depicts electrons not as pure particles, but as having the properties of both particles and waves , and these electrons are located in specific probability density clouds around the atom's nucleus.

Both physical and conceptual models continue to be important components of scientific research . In addition, many scientists now build models mathematically through computer programming. These computer-based models serve many of the same purposes as physical models, but are determined entirely by mathematical relationships between variables that are defined numerically. The mathematical relationships are kind of like individual LEGO bricks: They are basic building blocks that can be assembled in many different ways. In this case, the building blocks are fundamental concepts and theories like the mathematical description of turbulent flow in a liquid , the law of conservation of energy, or the laws of thermodynamics, which can be assembled into a wide variety of models for, say, the flow of contaminants released into a groundwater reservoir or for global climate change.

Comprehension Checkpoint

• Modeling as a scientific research method

Whether developing a conceptual model like the atomic model, a physical model like a miniature river delta , or a computer model like a global climate model, the first step is to define the system that is to be modeled and the goals for the model. "System" is a generic term that can apply to something very small (like a single atom), something very large (like the Earth's atmosphere), or something in between, like the distribution of nutrients in a local stream. So defining the system generally involves drawing the boundaries (literally or figuratively) around what you want to model, and then determining the key variables and the relationships between those variables.

Though this initial step may seem straightforward, it can be quite complicated. Inevitably, there are many more variables within a system than can be realistically included in a model , so scientists need to simplify. To do this, they make assumptions about which variables are most important. In building a physical model of a river delta , for example, the scientists made the assumption that biological processes like burrowing clams were not important to the large-scale structure of the delta, even though they are clearly a component of the real system.

Determining where simplification is appropriate takes a detailed understanding of the real system – and in fact, sometimes models are used to help determine exactly which aspects of the system can be simplified. For example, the scientists who built the model of the river delta did not incorporate burrowing clams into their model because they knew from experience that they would not affect the overall layering of sediments within the delta. On the other hand, they were aware that vegetation strongly affects the shape of the river channel (and thus the distribution of sediments), and therefore conducted an experiment to determine the nature of the relationship between vegetation density and river channel shape (Gran & Paola, 2001).

Figure 3: Dalton's ball and hook model for the atom.

Once a model is built (either in concept, physical space, or in a computer), it can be tested using a given set of conditions. The results of these tests can then be compared against reality in order to validate the model. In other words, how well does the model do at matching what we see in the real world? In the physical model of delta sediments , the scientists who built the model looked for features like the layering of sand that they have seen in the real world. If the model shows something really different than what the scientists expect, the relationships between variables may need to be redefined or the scientists may have oversimplified the system . Then the model is revised, improved, tested again, and compared to observations again in an ongoing, iterative process . For example, the conceptual "billiard ball" model of the atom used in the early 1800s worked for some aspects of the behavior of gases, but when that hypothesis was tested for chemical reactions , it didn't do a good job of explaining how they occur – billiard balls do not normally interact with one another. John Dalton envisioned a revision of the model in which he added "hooks" to the billiard ball model to account for the fact that atoms could join together in reactions , as conceptualized in Figure 3.

While conceptual and physical models have long been a component of all scientific disciplines, computer-based modeling is a more recent development, and one that is frequently misunderstood. Computer models are based on exactly the same principles as conceptual and physical models, however, and they take advantage of relatively recent advances in computing power to mimic real systems .

• The beginning of computer modeling: Numerical weather prediction

In the late 19 th century, Vilhelm Bjerknes , a Norwegian mathematician and physicist, became interested in deriving equations that govern the large-scale motion of air in the atmosphere . Importantly, he recognized that circulation was the result not just of thermodynamic properties (like the tendency of hot air to rise), but of hydrodynamic properties as well, which describe the behavior of fluid flow. Through his work, he developed an equation that described the physical processes involved in atmospheric circulation, which he published in 1897. The complexity of the equation reflected the complexity of the atmosphere, and Bjerknes was able to use it to describe why weather fronts develop and move.

• Using calculations predictively

Bjerknes had another vision for his mathematical work, however: He wanted to predict the weather. The goal of weather prediction, he realized, is not to know the paths of individual air molecules over time, but to provide the public with "average values over large areas and long periods of time." Because his equation was based on physical principles , he saw that by entering the present values of atmospheric variables like air pressure and temperature, he could solve it to predict the air pressure and temperature at some time in the future. In 1904, Bjerknes published a short paper describing what he called "the principle of predictive meteorology", (Bjerknes, 1904) (see the Research links for the entire paper). In it, he says:

Based upon the observations that have been made, the initial state of the atmosphere is represented by a number of charts which give the distribution of seven variables from level to level in the atmosphere. With these charts as the starting point, new charts of a similar kind are to be drawn, which represent the new state from hour to hour.

In other words, Bjerknes envisioned drawing a series of weather charts for the future based on using known quantities and physical principles . He proposed that solving the complex equation could be made more manageable by breaking it down into a series of smaller, sequential calculations, where the results of one calculation are used as input for the next. As a simple example, imagine predicting traffic patterns in your neighborhood. You start by drawing a map of your neighborhood showing the location, speed, and direction of every car within a square mile. Using these parameters , you then calculate where all of those cars are one minute later. Then again after a second minute. Your calculations will likely look pretty good after the first minute. After the second, third, and fourth minutes, however, they begin to become less accurate. Other factors you had not included in your calculations begin to exert an influence, like where the person driving the car wants to go, the right- or left-hand turns that they make, delays at traffic lights and stop signs, and how many new drivers have entered the roads.

Trying to include all of this information simultaneously would be mathematically difficult, so, as proposed by Bjerknes, the problem can be solved with sequential calculations. To do this, you would take the first step as described above: Use location, speed, and direction to calculate where all the cars are after one minute. Next, you would use the information on right- and left-hand turn frequency to calculate changes in direction, and then you would use information on traffic light delays and new traffic to calculate changes in speed. After these three steps are done, you would solve your first equation again for the second minute time sequence, using location, speed, and direction to calculate where the cars are after the second minute. Though it would certainly be rather tiresome to do by hand, this series of sequential calculations would provide a manageable way to estimate traffic patterns over time.

Although this method made calculations tedious, Bjerknes imagined "no intractable mathematical difficulties" with predicting the weather. The method he proposed (but never used himself) became known as numerical weather prediction, and it represents one of the first approaches towards numerical modeling of a complex, dynamic system .

• Advancing weather calculations

Bjerknes' challenge for numerical weather prediction was taken up sixteen years later in 1922 by the English scientist Lewis Fry Richardson . Richardson related seven differential equations that built on Bjerknes' atmospheric circulation equation to include additional atmospheric processes. One of Richardson's great contributions to mathematical modeling was to solve the equations for boxes within a grid; he divided the atmosphere over Germany into 25 squares that corresponded with available weather station data (see Figure 4) and then divided the atmosphere into five layers, creating a three-dimensional grid of 125 boxes. This was the first use of a technique that is now standard in many types of modeling. For each box, he calculated each of nine variables in seven equations for a single time step of three hours. This was not a simple sequential calculation, however, since the values in each box depended on the values in the adjacent boxes, in part because the air in each box does not simply stay there – it moves from box to box.

Figure 4: Data for Richardson's forecast included measurements of winds, barometric pressure and temperature. Initial data were recorded in 25 squares, each 200 kilometers on a side, but conditions were forecast only for the two central squares outlined in red.

Richardson's attempt to make a six-hour forecast took him nearly six weeks of work with pencil and paper and was considered an utter failure, as it resulted in calculated barometric pressures that exceeded any historically measured value (Dalmedico, 2001). Probably influenced by Bjerknes, Richardson attributed the failure to inaccurate input data , whose errors were magnified through successive calculations (see more about error propagation in our Uncertainty, Error, and Confidence module).

Figure 5: Norwegian stamp bearing an image of Vilhelm Bjerknes

In addition to his concerns about inaccurate input parameters , Richardson realized that weather prediction was limited in large part by the speed at which individuals could calculate by hand. He thus envisioned a "forecast factory," in which thousands of people would each complete one small part of the necessary calculations for rapid weather forecasting.

• First computer for weather prediction

Richardson's vision became reality in a sense with the birth of the computer, which was able to do calculations far faster and with fewer errors than humans. The computer used for the first one-day weather prediction in 1950, nicknamed ENIAC (Electronic Numerical Integrator and Computer), was 8 feet tall, 3 feet wide, and 100 feet long – a behemoth by modern standards, but it was so much faster than Richardson's hand calculations that by 1955, meteorologists were using it to make forecasts twice a day (Weart, 2003). Over time, the accuracy of the forecasts increased as better data became available over the entire globe through radar technology and, eventually, satellites.

The process of numerical weather prediction developed by Bjerknes and Richardson laid the foundation not only for modern meteorology , but for computer-based mathematical modeling as we know it today. In fact, after Bjerknes died in 1951, the Norwegian government recognized the importance of his contributions to the science of meteorology by issuing a stamp bearing his portrait in 1962 (Figure 5).

• Modeling in practice: The development of global climate models

The desire to model Earth's climate on a long-term, global scale grew naturally out of numerical weather prediction. The goal was to use equations to describe atmospheric circulation in order to understand not just tomorrow's weather, but large-scale patterns in global climate, including dynamic features like the jet stream and major climatic shifts over time like ice ages. Initially, scientists were hindered in the development of valid models by three things: a lack of data from the more inaccessible components of the system like the upper atmosphere , the sheer complexity of a system that involved so many interacting components, and limited computing powers. Unexpectedly, World War II helped solve one problem as the newly-developed technology of high altitude aircraft offered a window into the upper atmosphere (see our Technology module for more information on the development of aircraft). The jet stream, now a familiar feature of the weather broadcast on the news, was in fact first documented by American bombers flying westward to Japan.

As a result, global atmospheric models began to feel more within reach. In the early 1950s, Norman Phillips, a meteorologist at Princeton University, built a mathematical model of the atmosphere based on fundamental thermodynamic equations (Phillips, 1956). He defined 26 variables related through 47 equations, which described things like evaporation from Earth's surface , the rotation of the Earth, and the change in air pressure with temperature. In the model, each of the 26 variables was calculated in each square of a 16 x 17 grid that represented a piece of the northern hemisphere. The grid represented an extremely simple landscape – it had no continents or oceans, no mountain ranges or topography at all. This was not because Phillips thought it was an accurate representation of reality, but because it simplified the calculations. He started his model with the atmosphere "at rest," with no predetermined air movement, and with yearly averages of input parameters like air temperature.

Phillips ran the model through 26 simulated day-night cycles by using the same kind of sequential calculations Bjerknes proposed. Within only one "day," a pattern in atmospheric pressure developed that strongly resembled the typical weather systems of the portion of the northern hemisphere he was modeling (see Figure 6). In other words, despite the simplicity of the model, Phillips was able to reproduce key features of atmospheric circulation , showing that the topography of the Earth was not of primary importance in atmospheric circulation. His work laid the foundation for an entire subdiscipline within climate science: development and refinement of General Circulation Models (GCMs).

Figure 6: A model result from Phillips' 1956 paper. The box in the lower right shows the size of a grid cell. The solid lines represent the elevation of the 1000 millibar pressure, so the H and L represent areas of high and low pressure, respectively. The dashed lines represent lines of constant temperature, indicating a decreasing temperature at higher latitudes. This is the 23 rd simulated day.

By the 1980s, computing power had increased to the point where modelers could incorporate the distribution of oceans and continents into their models . In 1991, the eruption of Mt. Pinatubo in the Philippines provided a natural experiment: How would the addition of a significant volume of sulfuric acid , carbon dioxide, and volcanic ash affect global climate? In the aftermath of the eruption, descriptive methods (see our Description in Scientific Research module) were used to document its effect on global climate: Worldwide measurements of sulfuric acid and other components were taken, along with the usual air temperature measurements. Scientists could see that the large eruption had affected climate , and they quantified the extent to which it had done so. This provided a perfect test for the GCMs . Given the inputs from the eruption, could they accurately reproduce the effects that descriptive research had shown? Within a few years, scientists had demonstrated that GCMs could indeed reproduce the climatic effects induced by the eruption, and confidence in the abilities of GCMs to provide reasonable scenarios for future climate change grew. The validity of these models has been further substantiated by their ability to simulate past events, like ice ages, and the agreement of many different models on the range of possibilities for warming in the future, one of which is shown in Figure 7.

Figure 7: Projected change in annual mean surface air temperature from the late 20th century (1971-2000 average) to the middle 21st century (2051-2060 average). Image courtesy NOAA Geophysical Fluid Dynamics Laboratory.

• Limitations and misconceptions of models

The widespread use of modeling has also led to widespread misconceptions about models , particularly with respect to their ability to predict. Some models are widely used for prediction, such as weather and streamflow forecasts, yet we know that weather forecasts are often wrong. Modeling still cannot predict exactly what will happen to the Earth's climate , but it can help us see the range of possibilities with a given set of changes. For example, many scientists have modeled what might happen to average global temperatures if the concentration of carbon dioxide (CO 2 ) in the atmosphere is doubled from pre-industrial levels (pre-1950); though individual models differ in exact output, they all fall in the range of an increase of 2-6° C (IPCC, 2007).

All models are also limited by the availability of data from the real system . As the amount of data from a system increases, so will the accuracy of the model. For climate modeling, that is why scientists continue to gather data about climate in the geologic past and monitor things like ocean temperatures with satellites – all those data help define parameters within the model. The same is true of physical and conceptual models, too, well-illustrated by the evolution of our model of the atom as our knowledge about subatomic particles increased.

• Modeling in modern practice

The various types of modeling play important roles in virtually every scientific discipline, from ecology to analytical chemistry and from population dynamics to geology. Physical models such as the river delta take advantage of cutting edge technology to integrate multiple large-scale processes. As computer processing speed and power have increased, so has the ability to run models on them. From the room-sized ENIAC in the 1950s to the closet-sized Cray supercomputer in the 1980s to today's laptop, processing speed has increased over a million-fold, allowing scientists to run models on their own computers rather than booking time on one of only a few supercomputers in the world. Our conceptual models continue to evolve, and one of the more recent theories in theoretical physics digs even deeper into the structure of the atom to propose that what we once thought were the most fundamental particles – quarks – are in fact composed of vibrating filaments, or strings. String theory is a complex conceptual model that may help explain gravitational force in a way that has not been done before. Modeling has also moved out of the realm of science into recreation, and many computer games like SimCity® involve both conceptual modeling (answering the question, "What would it be like to run a city?") and computer modeling, using the same kinds of equations that are used model traffic flow patterns in real cities. The accessibility of modeling as a research method allows it to be easily combined with other scientific research methods, and scientists often incorporate modeling into experimental, descriptive, and comparative studies.

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• Correspondence
• Open access
• Published: 07 November 2012

Reporting guidelines for modelling studies

• Carol Bennett 1 , 2 &
• Douglas G Manuel 1 , 2 , 3 , 4 , 5 , 6  

BMC Medical Research Methodology volume  12 , Article number:  168 ( 2012 ) Cite this article

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Modelling studies are used widely to help inform decisions about health care and policy and their use is increasing. However, in order for modelling to gain strength as a tool for health policy, it is critical that key model factors are transparent so that users of models can have a clear understanding of the model and its limitations.Reporting guidelines are evidence-based tools that specify minimum criteria for authors to report their research such that readers can both critically appraise and interpret study findings. This study was conducted to determine whether there is an unmet need for population modelling reporting guidelines.

We conducted a review of the literature to identify: 1) guidance for reporting population modelling studies; and, 2) evidence on the quality of reporting of population modelling studies. Guidance for reporting was analysed using a thematic approach and the data was summarised as frequencies. Evidence on the quality of reporting was reviewed and summarized descriptively.

There were no guidelines that specifically addressed the reporting of population modelling studies. We identified a number of reporting guidelines for economic evaluation studies, some of which had sections that were relevant population modelling studies. Amongst seven relevant records, we identified 69 quality criteria that have distinct reporting characteristics. We identified two papers that addressed reporting practices of modelling studies. Overall, with the exception of describing the data used for calibration, there was little consistency in reporting.

Conclusions

While numerous guidelines exist for developing and evaluating health technology assessment and economic evaluation models, which by extension could be applicable to population modelling studies, there is variation in their comprehensiveness and in the consistency of reporting these methods. Population modelling studies may be an area which would benefit from the development of a reporting guideline.

Peer Review reports

Introduction

Modelling studies are used widely to help inform decisions about health care and policy and their use is increasing [ 1 , 2 ]. A model is “an analytical methodology that accounts for events over time and across populations, that is based on data drawn from primary or secondary sources…” and in the context of health care-evaluation “…whose purpose is to estimate the effects of an intervention on valued health consequences and costs” [ 3 ]. Its value lies not only in its results, but also in its ability to reveal the connections between its data and assumptions and model outputs [ 3 ]. But, as pointed out by Garrison, models don’t have to be mathematically sophisticated to be hard to follow [ 4 ]. For these reasons, a model should not be a “black box” for the end-user but be as transparent as possible [ 3 ].

To address the problem of poorly reported research, multiple reporting guidelines have been developed and validated for use with a number of study designs. Reporting guidelines are evidence-based tools that employ expert consensus to specify minimum criteria for authors to report their research such that readers can both critically appraise and interpret study findings [ 5 , 6 ]. The EQUATOR Network, an international initiative whose aim is to improve the reliability of medical research by promoting transparent and accurate reporting of research studies, indexes more than 100 reporting guidelines on their Web site ( http://www.equator-network.org ).

The growth in the number and range of reporting guidelines has prompted guidance on how to develop one using a well-structured development process [ 6 ]. This study addresses the needs assessment-that is, to determine whether there is a need for population modelling reporting guidelines. More specifically, the objectives of our study were: to locate and assess any existing reporting guidelines for population modelling studies; to identify key quality criteria for the reporting of population modelling studies; and to determine if and how these criteria are being reported in the literature.

We began this process with a search of the MEDLINE electronic database (MEDLINE (1950 – February 2011) via Ovid. Our electronic search strategy (see appendix), developed in consultation with a library scientist, was pragmatically designed to avoid being overwhelmed with irrelevant records. We hand-searched the reference lists and used the related articles feature in PubMED for all papers meeting our eligibility criteria. In addition, we reviewed relevant textbooks and Web sites. One reviewer screened the titles and abstracts of all unique citations to identify papers that met our inclusion criteria—that is, English language papers that provided explicit guidance on the reporting of population modelling studies or provided evidence on the quality of reporting of population modelling studies in the health science literature. The full-text report of each record passing title/abstract screening was retrieved and reviewed by the research team and its inclusion/exclusion status was established.

For records that provided explicit guidance on reporting of population modelling studies, the list of criteria identified was analysed using a thematic approach and the data was summarised as frequencies. For those papers that presented evidence on the quality of reporting of population modelling studies, we identified the aspects of reporting that were assessed and summarised the results descriptively.

Results and discussion

We identified 806 unique records through our search strategy, 30 full-text articles were reviewed to determine eligibility (Figure 1 ).

Flow diagram of records – guidelines for reporting modelling studies and evidence on the quality of reporting of modelling studies.

Existence of guidelines for modelling studies

There were no guidelines that specifically addressed the reporting of population modelling studies. However, there were a number of reporting guidelines for economic evaluation studies: one of which was related to modelling [ 7 ] and one included a section which focused on the generalisability of modelling studies [ 8 ]. Additionally, we identified one paper that provided reporting guidance for a specific aspect of simulation modelling methodology – calibration [ 9 ].

Numerous guidelines have been published defining good practice for the conduct of economic evaluations in general and model-based evaluations in particular. We identified two papers that provided guidance for assessing the quality of decision-analytic modelling studies [ 3 , 10 ] and one paper that provided guidance for assessing validation of population-based disease simulation models [ 2 ].

Identification of key reporting items

Amongst the relevant records that were analysed, we identified 69 quality criteria that have distinct reporting characteristics (Table 1 ).

We identified 22 items relating to the structure of the model and broadly classified them into 10 domains: 1) statement of decision problem/objective; 2) statement of scope/perspective; 3) rationale for structure; 4) structural assumptions; 5) strategies/comparators; 6) model type; 7) time horizon; 8) disease states/pathways; 9) cycle length; and, 10) parsimony.

We identified 28 items related to data issues and broadly classified them into 11 domains: 1) data identification; 2) data modelling; 3) baseline data; 4) treatment effects; 5) risk factors; 6) data incorporation; 7) assessment of uncertainty; 8) methodological; 9) structural; 10) heterogeneity; and, 11) parameter.

We identified 14 items related to consistency (internal and external) and validity (output plausibility and predictive validity). The final five items fell under computer implementation, transparency or funding.

The items are not mutually exclusive, and there is overlap if one takes into account implicit and explicit considerations. Even considering this, the records differed in terms of their comprehensiveness and the areas of model quality they considered. No item was identified by all of the resources, one item appeared in five lists, four items appeared in four lists, three items appeared in 17 lists and the remainder of the items appeared in only one or two lists (Table 1 ).

Quality of reporting

We identified two papers that addressed reporting practices of modelling studies, the first of which was a systematic review of coronary heart disease policy models [ 11 ].

The authors evaluated 75 papers on the basis of whether a sensitivity analysis was carried out, the validity was checked, data quality was reported, illustrative examples were provided, if the model was potentially available to the reader (transparency), and if potential limitations were specified or discussed. This evaluation was based on authors reporting on the specific item in the articles.

Relatively few papers included in the review reported on quality issues: sensitivity analysis and assessment of validity were reported in very few models, 33% provided illustrative examples, working versions of the model were available in 10%, and 19% reported on limitations of their methodology,

The second paper examining the reporting practices of modelling studies looked more specifically at the reporting of calibration methods in 154 cancer simulation models [ 9 ]. Data elements abstracted included whether model validation was mentioned (52%) and if a description of the calibration protocol was provided (66%). The authors further characterized calibration protocols by five components. A description of the data used as calibration targets was reported by 95% of the studies and goodness-of-fit metrics were reported in 54% of the studies. However, the search algorithm used for selected alternative parameter values, the criteria for identifying parameter sets that provide an acceptable model fit, and the stopping criteria were not well reported (quantitative values not provided).

Few studies were identified that addressed the quality of reporting of population modelling studies. Overall, with the exception of describing the data used for calibration, there is little consistency in the reporting of items that have been identified as key quality items.

Population modelling studies can fill an important role for policy makers. Their ability to synthesize data from multiple sources and estimate the effects of interventions can be invaluable, especially in areas where primary data collection may be infeasible. However, in order for modelling to gain strength as a tool for health policy, it is critical that key model factors are made transparent so that users of models have a clear understanding of the model and its limitations.

While numerous guidelines exist for developing and evaluating health technology assessment and economic evaluation models, which by extension can be applicable to population modelling studies, there is variation in their comprehensiveness and in the consistency of reporting these methods. There is evidence to suggest that key items are under-reported.

In other areas where reporting guidelines have been developed, there has been a favourable impact on the transparency and accuracy of reporting [ 12 – 15 ]. Population modelling studies may be another area which would benefit from the development of a reporting guideline. Moher and colleagues have outlined the importance of a structured approach to the development of reporting guidelines [ 6 ]. This paper provides results from initial steps in this structure approach. Future work should focus on identifying key information related to potential sources of bias in population modelling studies and identifying a multidisciplinary expert panel to steer the guideline development process.

Search strategy

"Reproducibility of Results"

Quality control/

((valid$ or reliab$ or quality or accura$) adj2 (result$ or report$ or data)).tw.

(good adj1 practice$).tw.

Guidelines as Topic/

(guideline$ or checklist$).tw.

(model$ adj3 (stud$ or method$ or process$ or simulation)).tw.

(modelling or modeling).tw.

Research design/

Decision Support Techniques/

published literature.tw.

16 7 and 10 and 15

Weinstein MC, Toy EL, Sandberg EA, Neumann PJ, Evans JS, Kuntz KM, Graham JD, Hammitt JK: Modeling for health care and other policy decisions: uses, roles, and validity. Value Health. 2001, 4: 348-361. 10.1046/j.1524-4733.2001.45061.x.

Article   CAS   PubMed   Google Scholar  

Kopec JA, Fines P, Manuel DG, Buckeridge DL, Flanagan WM, Oderkirk J, Abrahamowicz M, Harper S, Sharif B, Okhmatovskaia A, Sayre EC, Rahman MM, Wolfson MC: Validation of population-based disease simulation models: a review of concepts and methods. [Review]. BMC Publ Health. 2010, 10: 710-10.1186/1471-2458-10-710.

Article   Google Scholar  

Weinstein MC, O’Brien B, Hornberger J, Jackson J, Johannesson M, McCabe C, Luce BR, ISPOR Task Force on Good Research Practices: Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices–Modeling Studies. Value Health. 2003, 6: 9-17. 10.1046/j.1524-4733.2003.00234.x.

Article   PubMed   Google Scholar  

Garrison LP: The ISPOR Good Practice Modeling Principles–a sensible approach: be transparent, be reasonable. Value Health. 2003, 6: 6-8. 10.1046/j.1524-4733.2003.00003.x.

Enhancing the Quality and Transparency of Health Research (Equator Network) An introduction to reporting guidelines. http://www.equator-network.org Accessed 2012-03-01.

Moher D, Schulz KF, Simera I, Altman DG: Guidance for developers of health research reporting guidelines. PLoS Medicine / Public Library of Science. 2010, 7: e1000217-

Google Scholar  

Nuijten MJ, Pronk MH, Brorens MJ, Hekster YA, Lockefeer JH, de Smet PA, Bonsel G, van der Kuy A: Reporting format for economic evaluation. Part II: Focus on modelling studies. Pharmacoeconomics. 1998, 14: 259-268. 10.2165/00019053-199814030-00003.

Drummond M, Manca A, Sculpher M: Increasing the generalizability of economic evaluations: recommendations for the design, analysis, and reporting of studies. Int J Technol Assess Health Care. 2005, 21: 165-171.

PubMed   Google Scholar  

Stout NK, Knudsen AB, Kong CY, McMahon PM, Gazelle GS: Calibration methods used in cancer simulation models and suggested reporting guidelines. [Review] [180 refs]. PharmacoEconomics. 2009, 27: 533-545. 10.2165/11314830-000000000-00000.

Article   PubMed   PubMed Central   Google Scholar  

Philips Z, Ginnelly L, Sculpher M, Claxton K, Golder S, Riemsma R, Woolacoot N, Glanville J: Review of guidelines for good practice in decision-analytic modelling in health technology assessment. [Review] [62 refs]. 2001, Winchester, England: Health Technology Assessment, 8: iii-iiv.

Unal B, Capewell S, Critchley JA: Coronary heart disease policy models: a systematic review. [Review] [51 refs]. BMC Publ Health. 2006, 6: 213-10.1186/1471-2458-6-213.

Smidt N, Rutjes AW, van der Windt DA, Ostelo RW, Bossuyt PM, Reitsma JB, Bouter LM, de Vet HC: The quality of diagnostic accuracy studies since the STARD statement: has it improved?. Neurology. 2006, 67: 792-797. 10.1212/01.wnl.0000238386.41398.30.

Plint AC, Moher D, Morrison A, Schulz K, Altman DG, Hill C, Gaboury I: Does the CONSORT checklist improve the quality of reports of randomised controlled trials? A systematic review. Med J Aust. 2006, 185: 263-267.

Smith BA, Lee HJ, Lee JH, Choi M, Jones DE, Bausell RB, Broome ME: Quality of reporting randomized controlled trials (RCTs) in the nursing literature: application of the consolidated standards of reporting trials (CONSORT). Nurs Outlook. 2008, 56: 31-37. 10.1016/j.outlook.2007.09.002.

Prady SL, Richmond SJ, Morton VM, Macpherson H: A systematic evaluation of the impact of STRICTA and CONSORT recommendations on quality of reporting for acupuncture trials. PLoS One. 2008, 3: e1577-10.1371/journal.pone.0001577. Electronic Resource.

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Acknowledgments

We thank Sascha Davis, MLIS (Librarian, The Ottawa Hospital) for her assistance with designing the electronic search strategy used in this study.

Funding support: Canadian Institutes of Health Research STAR Emerging Team Grant.

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Concept and design (CB, DM); acquisition of data (CB); analysis and interpretation of data (CB, DM); drafting of the manuscript (CB); critical revision of the manuscript for important intellectual content (CB, DM); and final approval of the version to be published (CB, DM). Both authors read and approved the final manuscript.

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What is a Model? 5 Essential Components

In the research and statistics context, what does the term model mean? This article defines what is a model, poses guide questions on how to create one, lists steps on how to construct a model and provides simple examples to clarify points arising from those questions.

One of the interesting things that I particularly like in statistics is the prospect of being able to predict an outcome (referred to as the independent variable) from a set of factors (referred to as the independent variables). A multiple regression equation or a model derived from a set of interrelated variables achieves this end.

The usefulness of a model is determined by how well it can predict the behavior of dependent variables from a set of independent variables.

To clarify the concept, I will describe here an example of a research activity that aimed to develop a multiple regression model from both secondary and primary data sources.

What is a Model?

Before going into a detailed discussion on what is a model, it is always good practice to define what we mean here by a model.

A model, in research and statistics, is a representation of reality using variables that somehow relate with each other. I italicize the word “somehow” here being reminded of the possibility of a correlation between variables when in fact there is no logical connection between them.

A Classic Example of Nonsensical Correlation

A classic example given to illustrate nonsensical correlation is the high correlation between length of hair and height. They found out in a study that if a person has short hair, that person is tall and vice versa.

Actually, the conclusion of that study is spurious because there is no real correlation between length of hair and height. It so happened that men usually have short hair while women have long hair. Men are taller than women. The true variable behind what really determines height is the sex or gender of the individual, not the length of hair.

The model is only an approximation of the likely outcome of things because there will always be errors involved in building it. This is the reason scientists adopt a five percent error (p=0.05) as a standard in making conclusions from statistical computations. There is no such thing as absolute certainty in predicting the probability of a phenomenon.

Things Needed to Construct A Model

In developing a multiple regression model which will be fully described here, you will need to have a clear idea of:

• What is your intention or reason in constructing the model?
• What is the time frame and unit of your analysis?
• What has been done so far in line with the model that you intend to construct?
• What variables would you like to include in your model?
• How would you ensure your model has predictive value?

These questions will guide you towards developing a model that will help you achieve your goal. I explain the expected answers to the above questions. I provide examples to further clarify the points.

1. Purpose in Constructing the Model

Why would you like to have a model in the first place? What would you like to get from it? The objectives of your research, therefore, should be clear enough so that you can derive full benefit from it.

Here, I sought to develop a model. The main purpose is to determine the predictors of the number of published papers produced by the faculty in the university. The major question, therefore, is:

“What are the crucial factors that will motivate the faculty members to engage in research and publish research papers?”

Once the research director of the university, I figured out that the best way to increase the number of research publications is to zero in on those variables that really matter. There are so many variables that will influence the turnout of publications, but which ones do really matter?

A certain number of research publications is required each year, so what should the interventions be to reach those targets? There is a need to identify the reasons for the failure of the faculty members to publish research papers to rectify the problem.

2. Time Frame and Unit of Analysis

You should have a specific time frame on which you should base your analysis from.

There are many considerations in selecting the time frame of the analysis but of foremost importance is the availability of data. For established universities with consistent data collection fields, this poses no problem. But for struggling universities without an established database, it will be much more challenging.

Why do I say consistent data collection fields? If you want to see trends, then the same data must be collected in a series through time.

What do I mean by this?

In the particular case I mentioned, i.e., number of publications, one of the suspected predictors is the time spent by the faculty in administrative work. In a 40-hour work week, how much time do they spend in designated posts such as unit head, department head, or dean? This variable which is a unit of analysis , therefore, should be consistently monitored every semester, for many years for correlation with the number of publications.

How many years should these data be collected?

From what I collect, peer-reviewed publications can be produced normally from two to three years. Hence, the study must cover at least three years of data to log the number of publications produced. That is, if no systematic data collection ensued to supply the study’s data needs.

If data was systematically collected, you can backtrack and get data for as long as you want. It is even possible to compare publication performance before and after implementation of a research policy in the university.

3. Literature Review

You might be guilty of “reinventing the wheel” if you did not take time to review published literature on your specific research concern. Reinventing the wheel means you duplicate the work of others. It is possible that other researchers have already satisfactorily studied the area you are trying to clarify issues on. For this reason, an exhaustive review of literature will enhance the quality and predictive value of your model.

For the model I attempted to make on the number of publications made by the faculty, I bumped on a summary of the predictors made by Bland et al . [1] based on a considerable number of published papers. Below is the model they prepared to sum up the findings.

Bland and colleagues found that three major areas determine research productivity namely,

1) the individual’s characteristics,

2) institutional characteristics, and

3) leadership characteristics.

This just means that you cannot just threaten the faculty with the so-called publish and perish policy if the required institutional resources are absent and/or leadership quality is poor.

4. Select the Variables for Study

The model given by Bland and colleagues in the figure above is still too general to allow statistical analysis to take place.

For example, in individual characteristics, how can socialization as a variable be measured? How about motivation ?

This requires you to further delve on literature on how to properly measure socialization and motivation, among other variables you are interested in. The dependent variable I reflected productivity in a recent study I conducted with students is the number of total publications , whether these are peer-reviewed.

5. Ensuring the Predictive Value of the Model

The predictive value of a model depends on influence of a set of predictor variables on the dependent variable. How do you determine influence of these variables?

In Bland’s model, we may include all the variables associated with those concepts identified in analyzing data. But of course, this will be costly and time-consuming as there are a lot of variables to consider. Besides, the greater the number of variables you included in your analysis, the more samples you will need to get a good correlation between the predictor variables and the dependent variable .

Stevens [2] recommends a nominal number of 15 cases for one predictor variable. This means that if you want to study 10 variables, you will need at least 150 cases to make your multiple regression model valid in some sense. But of course, the more samples you have, the greater the certainty in predicting outcomes.

Once you have decided on the number of variables you intend to incorporate in your multiple regression model, you will then be able to input your data on a spreadsheet or a statistical software such as SPSS, Statistica, or related software applications. The software application will automatically produce the results for you.

The next concern is how to interpret the results of a model such as the results of a multiple regression analysis . I will consider this topic in my upcoming posts.

A model is only as good as the data used to create it. You must therefore make sure that your data is accurate and reliable for better predictive outcomes.

• Bland, C.J., Center, B.A., Finstad, D.A., Risbey, K.R., and J. G. Staples. (2005). A Theoretical, Practical, Predictive Model of Faculty and Department Research Productivity.  Academic Medicine , Vol. 80, No. 3, 225-237.
• Stevens, J. 2002. Applied multivariate statistics for the social sciences, 3rd ed . New Jersey: Lawrence Erlbaum Publishers. p. 72.

Updated May 6, 2022 © P. A. Regoniel

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Home » Conceptual Framework – Types, Methodology and Examples

Conceptual Framework – Types, Methodology and Examples

Table of Contents

Conceptual Framework

Definition:

A conceptual framework is a structured approach to organizing and understanding complex ideas, theories, or concepts. It provides a systematic and coherent way of thinking about a problem or topic, and helps to guide research or analysis in a particular field.

A conceptual framework typically includes a set of assumptions, concepts, and propositions that form a theoretical framework for understanding a particular phenomenon. It can be used to develop hypotheses, guide empirical research, or provide a framework for evaluating and interpreting data.

Conceptual Framework in Research

In research, a conceptual framework is a theoretical structure that provides a framework for understanding a particular phenomenon or problem. It is a key component of any research project and helps to guide the research process from start to finish.

A conceptual framework provides a clear understanding of the variables, relationships, and assumptions that underpin a research study. It outlines the key concepts that the study is investigating and how they are related to each other. It also defines the scope of the study and sets out the research questions or hypotheses.

Types of Conceptual Framework

Types of Conceptual Framework are as follows:

Theoretical Framework

A theoretical framework is an overarching set of concepts, ideas, and assumptions that help to explain and interpret a phenomenon. It provides a theoretical perspective on the phenomenon being studied and helps researchers to identify the relationships between different concepts. For example, a theoretical framework for a study on the impact of social media on mental health might draw on theories of communication, social influence, and psychological well-being.

Conceptual Model

A conceptual model is a visual or written representation of a complex system or phenomenon. It helps to identify the main components of the system and the relationships between them. For example, a conceptual model for a study on the factors that influence employee turnover might include factors such as job satisfaction, salary, work-life balance, and job security, and the relationships between them.

Empirical Framework

An empirical framework is based on empirical data and helps to explain a particular phenomenon. It involves collecting data, analyzing it, and developing a framework to explain the results. For example, an empirical framework for a study on the impact of a new health intervention might involve collecting data on the intervention’s effectiveness, cost, and acceptability to patients.

Descriptive Framework

A descriptive framework is used to describe a particular phenomenon. It helps to identify the main characteristics of the phenomenon and to develop a vocabulary to describe it. For example, a descriptive framework for a study on different types of musical genres might include descriptions of the instruments used, the rhythms and beats, the vocal styles, and the cultural contexts of each genre.

Analytical Framework

An analytical framework is used to analyze a particular phenomenon. It involves breaking down the phenomenon into its constituent parts and analyzing them separately. This type of framework is often used in social science research. For example, an analytical framework for a study on the impact of race on police brutality might involve analyzing the historical and cultural factors that contribute to racial bias, the organizational factors that influence police behavior, and the psychological factors that influence individual officers’ behavior.

Conceptual Framework for Policy Analysis

A conceptual framework for policy analysis is used to guide the development of policies or programs. It helps policymakers to identify the key issues and to develop strategies to address them. For example, a conceptual framework for a policy analysis on climate change might involve identifying the key stakeholders, assessing their interests and concerns, and developing policy options to mitigate the impacts of climate change.

Logical Frameworks

Logical frameworks are used to plan and evaluate projects and programs. They provide a structured approach to identifying project goals, objectives, and outcomes, and help to ensure that all stakeholders are aligned and working towards the same objectives.

Conceptual Frameworks for Program Evaluation

These frameworks are used to evaluate the effectiveness of programs or interventions. They provide a structure for identifying program goals, objectives, and outcomes, and help to measure the impact of the program on its intended beneficiaries.

Conceptual Frameworks for Organizational Analysis

These frameworks are used to analyze and evaluate organizational structures, processes, and performance. They provide a structured approach to understanding the relationships between different departments, functions, and stakeholders within an organization.

Conceptual Frameworks for Strategic Planning

These frameworks are used to develop and implement strategic plans for organizations or businesses. They help to identify the key factors and stakeholders that will impact the success of the plan, and provide a structure for setting goals, developing strategies, and monitoring progress.

Components of Conceptual Framework

The components of a conceptual framework typically include:

• Research question or problem statement : This component defines the problem or question that the conceptual framework seeks to address. It sets the stage for the development of the framework and guides the selection of the relevant concepts and constructs.
• Concepts : These are the general ideas, principles, or categories that are used to describe and explain the phenomenon or problem under investigation. Concepts provide the building blocks of the framework and help to establish a common language for discussing the issue.
• Constructs : Constructs are the specific variables or concepts that are used to operationalize the general concepts. They are measurable or observable and serve as indicators of the underlying concept.
• Propositions or hypotheses : These are statements that describe the relationships between the concepts or constructs in the framework. They provide a basis for testing the validity of the framework and for generating new insights or theories.
• Assumptions : These are the underlying beliefs or values that shape the framework. They may be explicit or implicit and may influence the selection and interpretation of the concepts and constructs.
• Boundaries : These are the limits or scope of the framework. They define the focus of the investigation and help to clarify what is included and excluded from the analysis.
• Context : This component refers to the broader social, cultural, and historical factors that shape the phenomenon or problem under investigation. It helps to situate the framework within a larger theoretical or empirical context and to identify the relevant variables and factors that may affect the phenomenon.
• Relationships and connections: These are the connections and interrelationships between the different components of the conceptual framework. They describe how the concepts and constructs are linked and how they contribute to the overall understanding of the phenomenon or problem.
• Variables : These are the factors that are being measured or observed in the study. They are often operationalized as constructs and are used to test the propositions or hypotheses.
• Methodology : This component describes the research methods and techniques that will be used to collect and analyze data. It includes the sampling strategy, data collection methods, data analysis techniques, and ethical considerations.
• Literature review : This component provides an overview of the existing research and theories related to the phenomenon or problem under investigation. It helps to identify the gaps in the literature and to situate the framework within the broader theoretical and empirical context.
• Outcomes and implications: These are the expected outcomes or implications of the study. They describe the potential contributions of the study to the theoretical and empirical knowledge in the field and the practical implications for policy and practice.

Conceptual Framework Methodology

Conceptual Framework Methodology is a research method that is commonly used in academic and scientific research to develop a theoretical framework for a study. It is a systematic approach that helps researchers to organize their thoughts and ideas, identify the variables that are relevant to their study, and establish the relationships between these variables.

Here are the steps involved in the conceptual framework methodology:

Identify the Research Problem

The first step is to identify the research problem or question that the study aims to answer. This involves identifying the gaps in the existing literature and determining what specific issue the study aims to address.

Conduct a Literature Review

The second step involves conducting a thorough literature review to identify the existing theories, models, and frameworks that are relevant to the research question. This will help the researcher to identify the key concepts and variables that need to be considered in the study.

Define key Concepts and Variables

The next step is to define the key concepts and variables that are relevant to the study. This involves clearly defining the terms used in the study, and identifying the factors that will be measured or observed in the study.

Develop a Theoretical Framework

Once the key concepts and variables have been identified, the researcher can develop a theoretical framework. This involves establishing the relationships between the key concepts and variables, and creating a visual representation of these relationships.

Test the Framework

The final step is to test the theoretical framework using empirical data. This involves collecting and analyzing data to determine whether the relationships between the key concepts and variables that were identified in the framework are accurate and valid.

Examples of Conceptual Framework

Some realtime Examples of Conceptual Framework are as follows:

• In economics , the concept of supply and demand is a well-known conceptual framework. It provides a structure for understanding how prices are set in a market, based on the interplay of the quantity of goods supplied by producers and the quantity of goods demanded by consumers.
• In psychology , the cognitive-behavioral framework is a widely used conceptual framework for understanding mental health and illness. It emphasizes the role of thoughts and behaviors in shaping emotions and the importance of cognitive restructuring and behavior change in treatment.
• In sociology , the social determinants of health framework provides a way of understanding how social and economic factors such as income, education, and race influence health outcomes. This framework is widely used in public health research and policy.
• In environmental science , the ecosystem services framework is a way of understanding the benefits that humans derive from natural ecosystems, such as clean air and water, pollination, and carbon storage. This framework is used to guide conservation and land-use decisions.
• In education, the constructivist framework is a way of understanding how learners construct knowledge through active engagement with their environment. This framework is used to guide instructional design and teaching strategies.

Applications of Conceptual Framework

Some of the applications of Conceptual Frameworks are as follows:

• Research : Conceptual frameworks are used in research to guide the design, implementation, and interpretation of studies. Researchers use conceptual frameworks to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data.
• Policy: Conceptual frameworks are used in policy-making to guide the development of policies and programs. Policymakers use conceptual frameworks to identify key factors that influence a particular problem or issue, and to develop strategies for addressing them.
• Education : Conceptual frameworks are used in education to guide the design and implementation of instructional strategies and curriculum. Educators use conceptual frameworks to identify learning objectives, select appropriate teaching methods, and assess student learning.
• Management : Conceptual frameworks are used in management to guide decision-making and strategy development. Managers use conceptual frameworks to understand the internal and external factors that influence their organizations, and to develop strategies for achieving their goals.
• Evaluation : Conceptual frameworks are used in evaluation to guide the development of evaluation plans and to interpret evaluation results. Evaluators use conceptual frameworks to identify key outcomes, indicators, and measures, and to develop a logic model for their evaluation.

Purpose of Conceptual Framework

The purpose of a conceptual framework is to provide a theoretical foundation for understanding and analyzing complex phenomena. Conceptual frameworks help to:

• Guide research : Conceptual frameworks provide a framework for researchers to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data. By providing a theoretical foundation for research, conceptual frameworks help to ensure that research is rigorous, systematic, and valid.
• Provide clarity: Conceptual frameworks help to provide clarity and structure to complex phenomena by identifying key concepts, relationships, and processes. By providing a clear and systematic understanding of a phenomenon, conceptual frameworks help to ensure that researchers, policymakers, and practitioners are all on the same page when it comes to understanding the issue at hand.
• Inform decision-making : Conceptual frameworks can be used to inform decision-making and strategy development by identifying key factors that influence a particular problem or issue. By understanding the complex interplay of factors that contribute to a particular issue, decision-makers can develop more effective strategies for addressing the problem.
• Facilitate communication : Conceptual frameworks provide a common language and conceptual framework for researchers, policymakers, and practitioners to communicate and collaborate on complex issues. By providing a shared understanding of a phenomenon, conceptual frameworks help to ensure that everyone is working towards the same goal.

When to use Conceptual Framework

There are several situations when it is appropriate to use a conceptual framework:

• To guide the research : A conceptual framework can be used to guide the research process by providing a clear roadmap for the research project. It can help researchers identify key variables and relationships, and develop hypotheses or research questions.
• To clarify concepts : A conceptual framework can be used to clarify and define key concepts and terms used in a research project. It can help ensure that all researchers are using the same language and have a shared understanding of the concepts being studied.
• To provide a theoretical basis: A conceptual framework can provide a theoretical basis for a research project by linking it to existing theories or conceptual models. This can help researchers build on previous research and contribute to the development of a field.
• To identify gaps in knowledge : A conceptual framework can help identify gaps in existing knowledge by highlighting areas that require further research or investigation.
• To communicate findings : A conceptual framework can be used to communicate research findings by providing a clear and concise summary of the key variables, relationships, and assumptions that underpin the research project.

Characteristics of Conceptual Framework

key characteristics of a conceptual framework are:

• Clear definition of key concepts : A conceptual framework should clearly define the key concepts and terms being used in a research project. This ensures that all researchers have a shared understanding of the concepts being studied.
• Identification of key variables: A conceptual framework should identify the key variables that are being studied and how they are related to each other. This helps to organize the research project and provides a clear focus for the study.
• Logical structure: A conceptual framework should have a logical structure that connects the key concepts and variables being studied. This helps to ensure that the research project is coherent and consistent.
• Based on existing theory : A conceptual framework should be based on existing theory or conceptual models. This helps to ensure that the research project is grounded in existing knowledge and builds on previous research.
• Testable hypotheses or research questions: A conceptual framework should include testable hypotheses or research questions that can be answered through empirical research. This helps to ensure that the research project is rigorous and scientifically valid.
• Flexibility : A conceptual framework should be flexible enough to allow for modifications as new information is gathered during the research process. This helps to ensure that the research project is responsive to new findings and is able to adapt to changing circumstances.

Advantages of Conceptual Framework

Advantages of the Conceptual Framework are as follows:

• Clarity : A conceptual framework provides clarity to researchers by outlining the key concepts and variables that are relevant to the research project. This clarity helps researchers to focus on the most important aspects of the research problem and develop a clear plan for investigating it.
• Direction : A conceptual framework provides direction to researchers by helping them to develop hypotheses or research questions that are grounded in existing theory or conceptual models. This direction ensures that the research project is relevant and contributes to the development of the field.
• Efficiency : A conceptual framework can increase efficiency in the research process by providing a structure for organizing ideas and data. This structure can help researchers to avoid redundancies and inconsistencies in their work, saving time and effort.
• Rigor : A conceptual framework can help to ensure the rigor of a research project by providing a theoretical basis for the investigation. This rigor is essential for ensuring that the research project is scientifically valid and produces meaningful results.
• Communication : A conceptual framework can facilitate communication between researchers by providing a shared language and understanding of the key concepts and variables being studied. This communication is essential for collaboration and the advancement of knowledge in the field.
• Generalization : A conceptual framework can help to generalize research findings beyond the specific study by providing a theoretical basis for the investigation. This generalization is essential for the development of knowledge in the field and for informing future research.

Limitations of Conceptual Framework

Limitations of Conceptual Framework are as follows:

• Limited applicability: Conceptual frameworks are often based on existing theory or conceptual models, which may not be applicable to all research problems or contexts. This can limit the usefulness of a conceptual framework in certain situations.
• Lack of empirical support : While a conceptual framework can provide a theoretical basis for a research project, it may not be supported by empirical evidence. This can limit the usefulness of a conceptual framework in guiding empirical research.
• Narrow focus: A conceptual framework can provide a clear focus for a research project, but it may also limit the scope of the investigation. This can make it difficult to address broader research questions or to consider alternative perspectives.
• Over-simplification: A conceptual framework can help to organize and structure research ideas, but it may also over-simplify complex phenomena. This can limit the depth of the investigation and the richness of the data collected.
• Inflexibility : A conceptual framework can provide a structure for organizing research ideas, but it may also be inflexible in the face of new data or unexpected findings. This can limit the ability of researchers to adapt their research project to new information or changing circumstances.
• Difficulty in development : Developing a conceptual framework can be a challenging and time-consuming process. It requires a thorough understanding of existing theory or conceptual models, and may require collaboration with other researchers.

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• Open access
• Published: 19 April 2024

A scoping review of continuous quality improvement in healthcare system: conceptualization, models and tools, barriers and facilitators, and impact

• Aklilu Endalamaw 1 , 2 ,
• Resham B Khatri 1 , 3 ,
• Tesfaye Setegn Mengistu 1 , 2 ,
• Daniel Erku 1 , 4 , 5 ,
• Eskinder Wolka 6 ,
• Anteneh Zewdie 6 &
• Yibeltal Assefa 1  

BMC Health Services Research volume  24 , Article number:  487 ( 2024 ) Cite this article

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Metrics details

The growing adoption of continuous quality improvement (CQI) initiatives in healthcare has generated a surge in research interest to gain a deeper understanding of CQI. However, comprehensive evidence regarding the diverse facets of CQI in healthcare has been limited. Our review sought to comprehensively grasp the conceptualization and principles of CQI, explore existing models and tools, analyze barriers and facilitators, and investigate its overall impacts.

This qualitative scoping review was conducted using Arksey and O’Malley’s methodological framework. We searched articles in PubMed, Web of Science, Scopus, and EMBASE databases. In addition, we accessed articles from Google Scholar. We used mixed-method analysis, including qualitative content analysis and quantitative descriptive for quantitative findings to summarize findings and PRISMA extension for scoping reviews (PRISMA-ScR) framework to report the overall works.

A total of 87 articles, which covered 14 CQI models, were included in the review. While 19 tools were used for CQI models and initiatives, Plan-Do-Study/Check-Act cycle was the commonly employed model to understand the CQI implementation process. The main reported purposes of using CQI, as its positive impact, are to improve the structure of the health system (e.g., leadership, health workforce, health technology use, supplies, and costs), enhance healthcare delivery processes and outputs (e.g., care coordination and linkages, satisfaction, accessibility, continuity of care, safety, and efficiency), and improve treatment outcome (reduce morbidity and mortality). The implementation of CQI is not without challenges. There are cultural (i.e., resistance/reluctance to quality-focused culture and fear of blame or punishment), technical, structural (related to organizational structure, processes, and systems), and strategic (inadequate planning and inappropriate goals) related barriers that were commonly reported during the implementation of CQI.

Conclusions

Implementing CQI initiatives necessitates thoroughly comprehending key principles such as teamwork and timeline. To effectively address challenges, it’s crucial to identify obstacles and implement optimal interventions proactively. Healthcare professionals and leaders need to be mentally equipped and cognizant of the significant role CQI initiatives play in achieving purposes for quality of care.

Peer Review reports

Continuous quality improvement (CQI) initiative is a crucial initiative aimed at enhancing quality in the health system that has gradually been adopted in the healthcare industry. In the early 20th century, Shewhart laid the foundation for quality improvement by describing three essential steps for process improvement: specification, production, and inspection [ 1 , 2 ]. Then, Deming expanded Shewhart’s three-step model into ‘plan, do, study/check, and act’ (PDSA or PDCA) cycle, which was applied to management practices in Japan in the 1950s [ 3 ] and was gradually translated into the health system. In 1991, Kuperman applied a CQI approach to healthcare, comprising selecting a process to be improved, assembling a team of expert clinicians that understands the process and the outcomes, determining key steps in the process and expected outcomes, collecting data that measure the key process steps and outcomes, and providing data feedback to the practitioners [ 4 ]. These philosophies have served as the baseline for the foundation of principles for continuous improvement [ 5 ].

Continuous quality improvement fosters a culture of continuous learning, innovation, and improvement. It encourages proactive identification and resolution of problems, promotes employee engagement and empowerment, encourages trust and respect, and aims for better quality of care [ 6 , 7 ]. These characteristics drive the interaction of CQI with other quality improvement projects, such as quality assurance and total quality management [ 8 ]. Quality assurance primarily focuses on identifying deviations or errors through inspections, audits, and formal reviews, often settling for what is considered ‘good enough’, rather than pursuing the highest possible standards [ 9 , 10 ], while total quality management is implemented as the management philosophy and system to improve all aspects of an organization continuously [ 11 ].

Continuous quality improvement has been implemented to provide quality care. However, providing effective healthcare is a complicated and complex task in achieving the desired health outcomes and the overall well-being of individuals and populations. It necessitates tackling issues, including access, patient safety, medical advances, care coordination, patient-centered care, and quality monitoring [ 12 , 13 ], rooted long ago. It is assumed that the history of quality improvement in healthcare started in 1854 when Florence Nightingale introduced quality improvement documentation [ 14 ]. Over the passing decades, Donabedian introduced structure, processes, and outcomes as quality of care components in 1966 [ 15 ]. More comprehensively, the Institute of Medicine in the United States of America (USA) has identified effectiveness, efficiency, equity, patient-centredness, safety, and timeliness as the components of quality of care [ 16 ]. Moreover, quality of care has recently been considered an integral part of universal health coverage (UHC) [ 17 ], which requires initiatives to mobilise essential inputs [ 18 ].

While the overall objective of CQI in health system is to enhance the quality of care, it is important to note that the purposes and principles of CQI can vary across different contexts [ 19 , 20 ]. This variation has sparked growing research interest. For instance, a review of CQI approaches for capacity building addressed its role in health workforce development [ 21 ]. Another systematic review, based on random-controlled design studies, assessed the effectiveness of CQI using training as an intervention and the PDSA model [ 22 ]. As a research gap, the former review was not directly related to the comprehensive elements of quality of care, while the latter focused solely on the impact of training using the PDSA model, among other potential models. Additionally, a review conducted in 2015 aimed to identify barriers and facilitators of CQI in Canadian contexts [ 23 ]. However, all these reviews presented different perspectives and investigated distinct outcomes. This suggests that there is still much to explore in terms of comprehensively understanding the various aspects of CQI initiatives in healthcare.

As a result, we conducted a scoping review to address several aspects of CQI. Scoping reviews serve as a valuable tool for systematically mapping the existing literature on a specific topic. They are instrumental when dealing with heterogeneous or complex bodies of research. Scoping reviews provide a comprehensive overview by summarizing and disseminating findings across multiple studies, even when evidence varies significantly [ 24 ]. In our specific scoping review, we included various types of literature, including systematic reviews, to enhance our understanding of CQI.

This scoping review examined how CQI is conceptualized and measured and investigated models and tools for its application while identifying implementation challenges and facilitators. It also analyzed the purposes and impact of CQI on the health systems, providing valuable insights for enhancing healthcare quality.

Protocol registration and results reporting

Protocol registration for this scoping review was not conducted. Arksey and O’Malley’s methodological framework was utilized to conduct this scoping review [ 25 ]. The scoping review procedures start by defining the research questions, identifying relevant literature, selecting articles, extracting data, and summarizing the results. The review findings are reported using the PRISMA extension for a scoping review (PRISMA-ScR) [ 26 ]. McGowan and colleagues also advised researchers to report findings from scoping reviews using PRISMA-ScR [ 27 ].

Defining the research problems

This review aims to comprehensively explore the conceptualization, models, tools, barriers, facilitators, and impacts of CQI within the healthcare system worldwide. Specifically, we address the following research questions: (1) How has CQI been defined across various contexts? (2) What are the diverse approaches to implementing CQI in healthcare settings? (3) Which tools are commonly employed for CQI implementation ? (4) What barriers hinder and facilitators support successful CQI initiatives? and (5) What effects CQI initiatives have on the overall care quality?

Information source and search strategy

We conducted the search in PubMed, Web of Science, Scopus, and EMBASE databases, and the Google Scholar search engine. The search terms were selected based on three main distinct concepts. One group was CQI-related terms. The second group included terms related to the purpose for which CQI has been implemented, and the third group included processes and impact. These terms were selected based on the Donabedian framework of structure, process, and outcome [ 28 ]. Additionally, the detailed keywords were recruited from the primary health framework, which has described lists of dimensions under process, output, outcome, and health system goals of any intervention for health [ 29 ]. The detailed search strategy is presented in the Supplementary file 1 (Search strategy). The search for articles was initiated on August 12, 2023, and the last search was conducted on September 01, 2023.

Eligibility criteria and article selection

Based on the scoping review’s population, concept, and context frameworks [ 30 ], the population included any patients or clients. Additionally, the concepts explored in the review encompassed definitions, implementation, models, tools, barriers, facilitators, and impacts of CQI. Furthermore, the review considered contexts at any level of health systems. We included articles if they reported results of qualitative or quantitative empirical study, case studies, analytic or descriptive synthesis, any review, and other written documents, were published in peer-reviewed journals, and were designed to address at least one of the identified research questions or one of the identified implementation outcomes or their synonymous taxonomy as described in the search strategy. Based on additional contexts, we included articles published in English without geographic and time limitations. We excluded articles with abstracts only, conference abstracts, letters to editors, commentators, and corrections.

We exported all citations to EndNote x20 to remove duplicates and screen relevant articles. The article selection process includes automatic duplicate removal by using EndNote x20, unmatched title and abstract removal, citation and abstract-only materials removal, and full-text assessment. The article selection process was mainly conducted by the first author (AE) and reported to the team during the weekly meetings. The first author encountered papers that caused confusion regarding whether to include or exclude them and discussed them with the last author (YA). Then, decisions were ultimately made. Whenever disagreements happened, they were resolved by discussion and reconsideration of the review questions in relation to the written documents of the article. Further statistical analysis, such as calculating Kappa, was not performed to determine article inclusion or exclusion.

Data extraction and data items

We extracted first author, publication year, country, settings, health problem, the purpose of the study, study design, types of intervention if applicable, CQI approaches/steps if applicable, CQI tools and procedures if applicable, and main findings using a customized Microsoft Excel form.

Summarizing and reporting the results

The main findings were summarized and described based on the main themes, including concepts under conceptualizing, principles, teams, timelines, models, tools, barriers, facilitators, and impacts of CQI. Results-based convergent synthesis, achieved through mixed-method analysis, involved content analysis to identify the thematic presentation of findings. Additionally, a narrative description was used for quantitative findings, aligning them with the appropriate theme. The authors meticulously reviewed the primary findings from each included material and contextualized these findings concerning the main themes1. This approach provides a comprehensive understanding of complex interventions and health systems, acknowledging quantitative and qualitative evidence.

Search results

A total of 11,251 documents were identified from various databases: SCOPUS ( n  = 4,339), PubMed ( n  = 2,893), Web of Science ( n  = 225), EMBASE ( n  = 3,651), and Google Scholar ( n  = 143). After removing duplicates ( n  = 5,061), 6,190 articles were evaluated by title and abstract. Subsequently, 208 articles were assessed for full-text eligibility. Following the eligibility criteria, 121 articles were excluded, leaving 87 included in the current review (Fig.  1 ).

Article selection process

Operationalizing continuous quality improvement

Continuous Quality Improvement (CQI) is operationalized as a cyclic process that requires commitment to implementation, teamwork, time allocation, and celebrating successes and failures.

CQI is a cyclic ongoing process that is followed reflexive, analytical and iterative steps, including identifying gaps, generating data, developing and implementing action plans, evaluating performance, providing feedback to implementers and leaders, and proposing necessary adjustments [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ].

CQI requires committing to the philosophy, involving continuous improvement [ 19 , 38 ], establishing a mission statement [ 37 ], and understanding quality definition [ 19 ].

CQI involves a wide range of patient-oriented measures and performance indicators, specifically satisfying internal and external customers, developing quality assurance, adopting common quality measures, and selecting process measures [ 8 , 19 , 35 , 36 , 37 , 39 , 40 ].

CQI requires celebrating success and failure without personalization, leading each team member to develop error-free attitudes [ 19 ]. Success and failure are related to underlying organizational processes and systems as causes of failure rather than blaming individuals [ 8 ] because CQI is process-focused based on collaborative, data-driven, responsive, rigorous and problem-solving statistical analysis [ 8 , 19 , 38 ]. Furthermore, a gap or failure opens another opportunity for establishing a data-driven learning organization [ 41 ].

CQI cannot be implemented without a CQI team [ 8 , 19 , 37 , 39 , 42 , 43 , 44 , 45 , 46 ]. A CQI team comprises individuals from various disciplines, often comprising a team leader, a subject matter expert (physician or other healthcare provider), a data analyst, a facilitator, frontline staff, and stakeholders [ 39 , 43 , 47 , 48 , 49 ]. It is also important to note that inviting stakeholders or partners as part of the CQI support intervention is crucial [ 19 , 38 , 48 ].

The timeline is another distinct feature of CQI because the results of CQI vary based on the implementation duration of each cycle [ 35 ]. There is no specific time limit for CQI implementation, although there is a general consensus that a cycle of CQI should be relatively short [ 35 ]. For instance, a CQI implementation took 2 months [ 42 ], 4 months [ 50 ], 9 months [ 51 , 52 ], 12 months [ 53 , 54 , 55 ], and one year and 5 months [ 49 ] duration to achieve the desired positive outcome, while bi-weekly [ 47 ] and monthly data reviews and analyses [ 44 , 48 , 56 ], and activities over 3 months [ 57 ] have also resulted in a positive outcome.

Continuous quality improvement models and tools

There have been several models are utilized. The Plan-Do-Study/Check-Act cycle is a stepwise process involving project initiation, situation analysis, root cause identification, solution generation and selection, implementation, result evaluation, standardization, and future planning [ 7 , 36 , 37 , 45 , 47 , 48 , 49 , 50 , 51 , 53 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ]. The FOCUS-PDCA cycle enhances the PDCA process by adding steps to find and improve a process (F), organize a knowledgeable team (O), clarify the process (C), understand variations (U), and select improvements (S) [ 55 , 71 , 72 , 73 ]. The FADE cycle involves identifying a problem (Focus), understanding it through data analysis (Analyze), devising solutions (Develop), and implementing the plan (Execute) [ 74 ]. The Logic Framework involves brainstorming to identify improvement areas, conducting root cause analysis to develop a problem tree, logically reasoning to create an objective tree, formulating the framework, and executing improvement projects [ 75 ]. Breakthrough series approach requires CQI teams to meet in quarterly collaborative learning sessions, share learning experiences, and continue discussion by telephone and cross-site visits to strengthen learning and idea exchange [ 47 ]. Another CQI model is the Lean approach, which has been conducted with Kaizen principles [ 52 ], 5 S principles, and the Six Sigma model. The 5 S (Sort, Set/Straighten, Shine, Standardize, Sustain) systematically organises and improves the workplace, focusing on sorting, setting order, shining, standardizing, and sustaining the improvement [ 54 , 76 ]. Kaizen principles guide CQI by advocating for continuous improvement, valuing all ideas, solving problems, focusing on practical, low-cost improvements, using data to drive change, acknowledging process defects, reducing variability and waste, recognizing every interaction as a customer-supplier relationship, empowering workers, responding to all ideas, and maintaining a disciplined workplace [ 77 ]. Lean Six Sigma, a CQI model, applies the DMAIC methodology, which involves defining (D) and measuring the problem (M), analyzing root causes (A), improving by finding solutions (I), and controlling by assessing process stability (C) [ 78 , 79 ]. The 5 C-cyclic model (consultation, collection, consideration, collaboration, and celebration), the first CQI framework for volunteer dental services in Aboriginal communities, ensures quality care based on community needs [ 80 ]. One study used meetings involving activities such as reviewing objectives, assigning roles, discussing the agenda, completing tasks, retaining key outputs, planning future steps, and evaluating the meeting’s effectiveness [ 81 ].

Various tools are involved in the implementation or evaluation of CQI initiatives: checklists [ 53 , 82 ], flowcharts [ 81 , 82 , 83 ], cause-and-effect diagrams (fishbone or Ishikawa diagrams) [ 60 , 62 , 79 , 81 , 82 ], fuzzy Pareto diagram [ 82 ], process maps [ 60 ], time series charts [ 48 ], why-why analysis [ 79 ], affinity diagrams and multivoting [ 81 ], and run chart [ 47 , 48 , 51 , 60 , 84 ], and others mentioned in the table (Table  1 ).

Barriers and facilitators of continuous quality improvement implementation

Implementing CQI initiatives is determined by various barriers and facilitators, which can be thematized into four dimensions. These dimensions are cultural, technical, structural, and strategic dimensions.

Continuous quality improvement initiatives face various cultural, strategic, technical, and structural barriers. Cultural dimension barriers involve resistance to change (e.g., not accepting online technology), lack of quality-focused culture, staff reporting apprehensiveness, and fear of blame or punishment [ 36 , 41 , 85 , 86 ]. The technical dimension barriers of CQI can include various factors that hinder the effective implementation and execution of CQI processes [ 36 , 86 , 87 , 88 , 89 ]. Structural dimension barriers of CQI arise from the organization structure, process, and systems that can impede the effective implementation and sustainability of CQI [ 36 , 85 , 86 , 87 , 88 ]. Strategic dimension barriers are, for example, the inability to select proper CQI goals and failure to integrate CQI into organizational planning and goals [ 36 , 85 , 86 , 87 , 88 , 90 ].

Facilitators are also grouped to cultural, structural, technical, and strategic dimensions to provide solutions to CQI barriers. Cultural challenges were addressed by developing a group culture to CQI and other rewards [ 39 , 41 , 80 , 85 , 86 , 87 , 90 , 91 , 92 ]. Technical facilitators are pivotal to improving technical barriers [ 39 , 42 , 53 , 69 , 86 , 90 , 91 ]. Structural-related facilitators are related to improving communication, infrastructure, and systems [ 86 , 92 , 93 ]. Strategic dimension facilitators include strengthening leadership and improving decision-making skills [ 43 , 53 , 67 , 86 , 87 , 92 , 94 , 95 ] (Table  2 ).

Impact of continuous quality improvement

Continuous quality improvement initiatives can significantly impact the quality of healthcare in a wide range of health areas, focusing on improving structure, the health service delivery process and improving client wellbeing and reducing mortality.

Structure components

These are health leadership, financing, workforce, technology, and equipment and supplies. CQI has improved planning, monitoring and evaluation [ 48 , 53 ], and leadership and planning [ 48 ], indicating improvement in leadership perspectives. Implementing CQI in primary health care (PHC) settings has shown potential for maintaining or reducing operation costs [ 67 ]. Findings from another study indicate that the costs associated with implementing CQI interventions per facility ranged from approximately $2,000 to $10,500 per year, with an average cost of approximately $10 to $60 per admitted client [ 57 ]. However, based on model predictions, the average cost savings after implementing CQI were estimated to be $5430 [ 31 ]. CQI can also be applied to health workforce development [ 32 ]. CQI in the institutional system improved medical education [ 66 , 96 , 97 ], human resources management [ 53 ], motivated staffs [ 76 ], and increased staff health awareness [ 69 ], while concerns raised about CQI impartiality, independence, and public accountability [ 96 ]. Regarding health technology, CQI also improved registration and documentation [ 48 , 53 , 98 ]. Furthermore, the CQI initiatives increased cleanliness [ 54 ] and improved logistics, supplies, and equipment [ 48 , 53 , 68 ].

Process and output components

The process component focuses on the activities and actions involved in delivering healthcare services.

Service delivery

CQI interventions improved service delivery [ 53 , 56 , 99 ], particularly a significant 18% increase in the overall quality of service performance [ 48 ], improved patient counselling, adherence to appropriate procedures, and infection prevention [ 48 , 68 ], and optimised workflow [ 52 ].

Coordination and collaboration

CQI initiatives improved coordination and collaboration through collecting and analysing data, onsite technical support, training, supportive supervision [ 53 ] and facilitating linkages between work processes and a quality control group [ 65 ].

Patient satisfaction

The CQI initiatives increased patient satisfaction and improved quality of life by optimizing care quality management, improving the quality of clinical nursing, reducing nursing defects and enhancing the wellbeing of clients [ 54 , 76 , 100 ], although CQI was not associated with changes in adolescent and young adults’ satisfaction [ 51 ].

CQI initiatives reduced medication error reports from 16 to 6 [ 101 ], and it significantly reduced the administration of inappropriate prophylactic antibiotics [ 44 ], decreased errors in inpatient care [ 52 ], decreased the overall episiotomy rate from 44.5 to 33.3% [ 83 ], reduced the overall incidence of unplanned endotracheal extubation [ 102 ], improving appropriate use of computed tomography angiography [ 103 ], and appropriate diagnosis and treatment selection [ 47 ].

Continuity of care

CQI initiatives effectively improve continuity of care by improving client and physician interaction. For instance, provider continuity levels showed a 64% increase [ 55 ]. Modifying electronic medical record templates, scheduling, staff and parental education, standardization of work processes, and birth to 1-year age-specific incentives in post-natal follow-up care increased continuity of care to 74% in 2018 compared to baseline 13% in 2012 [ 84 ].

The CQI initiative yielded enhanced efficiency in the cardiac catheterization laboratory, as evidenced by improved punctuality in procedure starts and increased efficiency in manual sheath-pulls inside [ 78 ].

Accessibility

CQI initiatives were effective in improving accessibility in terms of increasing service coverage and utilization rate. For instance, screening for cigarettes, nutrition counselling, folate prescription, maternal care, immunization coverage [ 53 , 81 , 104 , 105 ], reducing the percentage of non-attending patients to surgery to 0.9% from the baseline 3.9% [ 43 ], increasing Chlamydia screening rates from 29 to 60% [ 45 ], increasing HIV care continuum coverage [ 51 , 59 , 60 ], increasing in the uptake of postpartum long-acting reversible contraceptive use from 6.9% at the baseline to 25.4% [ 42 ], increasing post-caesarean section prophylaxis from 36 to 89% [ 62 ], a 31% increase of kangaroo care practice [ 50 ], and increased follow-up [ 65 ]. Similarly, the QI intervention increased the quality of antenatal care by 29.3%, correct partograph use by 51.7%, and correct active third-stage labour management, a 19.6% improvement from the baseline, but not significantly associated with improvement in contraceptive service uptake [ 61 ].

Timely access

CQI interventions improved the time care provision [ 52 ], and reduced waiting time [ 62 , 74 , 76 , 106 ]. For instance, the discharge process waiting time in the emergency department decreased from 76 min to 22 min [ 79 ]. It also reduced mean postprocedural length of stay from 2.8 days to 2.0 days [ 31 ].

Acceptability

Acceptability of CQI by healthcare providers was satisfactory. For instance, 88% of the faculty, 64% of the residents, and 82% of the staff believed CQI to be useful in the healthcare clinic [ 107 ].

Outcome components

Morbidity and mortality.

CQI efforts have demonstrated better management outcomes among diabetic patients [ 40 ], patients with oral mucositis [ 71 ], and anaemic patients [ 72 ]. It has also reduced infection rate in post-caesarean Sect. [ 62 ], reduced post-peritoneal dialysis peritonitis [ 49 , 108 ], and prevented pressure ulcers [ 70 ]. It is explained by peritonitis incidence from once every 40.1 patient months at baseline to once every 70.8 patient months after CQI [ 49 ] and a 63% reduction in pressure ulcer prevalence within 2 years from 2008 to 2010 [ 70 ]. Furthermore, CQI initiatives significantly reduced in-hospital deaths [ 31 ] and increased patient survival rates [ 108 ]. Figure  2 displays the overall process of the CQI implementations.

The overall mechanisms of continuous quality improvement implementation

In this review, we examined the fundamental concepts and principles underlying CQI, the factors that either hinder or assist in its successful application and implementation, and the purpose of CQI in enhancing quality of care across various health issues.

Our findings have brought attention to the application and implementation of CQI, emphasizing its underlying concepts and principles, as evident in the existing literature [ 31 , 32 , 33 , 34 , 35 , 36 , 39 , 40 , 43 , 45 , 46 ]. Continuous quality improvement has shared with the principles of continuous improvement, such as a customer-driven focus, effective leadership, active participation of individuals, a process-oriented approach, systematic implementation, emphasis on design improvement and prevention, evidence-based decision-making, and fostering partnership [ 5 ]. Moreover, Deming’s 14 principles laid the foundation for CQI principles [ 109 ]. These principles have been adapted and put into practice in various ways: ten [ 19 ] and five [ 38 ] principles in hospitals, five principles for capacity building [ 38 ], and two principles for medication error prevention [ 41 ]. As a principle, the application of CQI can be process-focused [ 8 , 19 ] or impact-focused [ 38 ]. Impact-focused CQI focuses on achieving specific outcomes or impacts, whereas process-focused CQI prioritizes and improves the underlying processes and systems. These principles complement each other and can be utilized based on the objectives of quality improvement initiatives in healthcare settings. Overall, CQI is an ongoing educational process that requires top management’s involvement, demands coordination across departments, encourages the incorporation of views beyond clinical area, and provides non-judgemental evidence based on objective data [ 110 ].

The current review recognized that it was not easy to implement CQI. It requires reasonable utilization of various models and tools. The application of each tool can be varied based on the studied health problem and the purpose of CQI initiative [ 111 ], varied in context, content, structure, and usability [ 112 ]. Additionally, overcoming the cultural, technical, structural, and strategic-related barriers. These barriers have emerged from clinical staff, managers, and health systems perspectives. Of the cultural obstacles, staff non-involvement, resistance to change, and reluctance to report error were staff-related. In contrast, others, such as the absence of celebration for success and hierarchical and rational culture, may require staff and manager involvement. Staff members may exhibit reluctance in reporting errors due to various cultural factors, including lack of trust, hierarchical structures, fear of retribution, and a blame-oriented culture. These challenges pose obstacles to implementing standardized CQI practices, as observed, for instance, in community pharmacy settings [ 85 ]. The hierarchical culture, characterized by clearly defined levels of power, authority, and decision-making, posed challenges to implementing CQI initiatives in public health [ 41 , 86 ]. Although rational culture, a type of organizational culture, emphasizes logical thinking and rational decision-making, it can also create challenges for CQI implementation [ 41 , 86 ] because hierarchical and rational cultures, which emphasize bureaucratic norms and narrow definitions of achievement, were found to act as barriers to the implementation of CQI [ 86 ]. These could be solved by developing a shared mindset and collective commitment, establishing a shared purpose, developing group norms, and cultivating psychological preparedness among staff, managers, and clients to implement and sustain CQI initiatives. Furthermore, reversing cultural-related barriers necessitates cultural-related solutions: development of a culture and group culture to CQI [ 41 , 86 ], positive comprehensive perception [ 91 ], commitment [ 85 ], involving patients, families, leaders, and staff [ 39 , 92 ], collaborating for a common goal [ 80 , 86 ], effective teamwork [ 86 , 87 ], and rewarding and celebrating successes [ 80 , 90 ].

The technical dimension barriers of CQI can include inadequate capitalization of a project and insufficient support for CQI facilitators and data entry managers [ 36 ], immature electronic medical records or poor information systems [ 36 , 86 ], and the lack of training and skills [ 86 , 87 , 88 ]. These challenges may cause the CQI team to rely on outdated information and technologies. The presence of barriers on the technical dimension may challenge the solid foundation of CQI expertise among staff, the ability to recognize opportunities for improvement, a comprehensive understanding of how services are produced and delivered, and routine use of expertise in daily work. Addressing these technical barriers requires knowledge creation activities (training, seminar, and education) [ 39 , 42 , 53 , 69 , 86 , 90 , 91 ], availability of quality data [ 86 ], reliable information [ 92 ], and a manual-online hybrid reporting system [ 85 ].

Structural dimension barriers of CQI include inadequate communication channels and lack of standardized process, specifically weak physician-to-physician synergies [ 36 ], lack of mechanisms for disseminating knowledge and limited use of communication mechanisms [ 86 ]. Lack of communication mechanism endangers sharing ideas and feedback among CQI teams, leading to misunderstandings, limited participation and misinterpretations, and a lack of learning [ 113 ]. Knowledge translation facilitates the co-production of research, subsequent diffusion of knowledge, and the developing stakeholder’s capacity and skills [ 114 ]. Thus, the absence of a knowledge translation mechanism may cause missed opportunities for learning, inefficient problem-solving, and limited creativity. To overcome these challenges, organizations should establish effective communication and information systems [ 86 , 93 ] and learning systems [ 92 ]. Though CQI and knowledge translation have interacted with each other, it is essential to recognize that they are distinct. CQI focuses on process improvement within health care systems, aiming to optimize existing processes, reduce errors, and enhance efficiency.

In contrast, knowledge translation bridges the gap between research evidence and clinical practice, translating research findings into actionable knowledge for practitioners. While both CQI and knowledge translation aim to enhance health care quality and patient outcomes, they employ different strategies: CQI utilizes tools like Plan-Do-Study-Act cycles and statistical process control, while knowledge translation involves knowledge synthesis and dissemination. Additionally, knowledge translation can also serve as a strategy to enhance CQI. Both concepts share the same principle: continuous improvement is essential for both. Therefore, effective strategies on the structural dimension may build efficient and effective steering councils, information systems, and structures to diffuse learning throughout the organization.

Strategic factors, such as goals, planning, funds, and resources, determine the overall purpose of CQI initiatives. Specific barriers were improper goals and poor planning [ 36 , 86 , 88 ], fragmentation of quality assurance policies [ 87 ], inadequate reinforcement to staff [ 36 , 90 ], time constraints [ 85 , 86 ], resource inadequacy [ 86 ], and work overload [ 86 ]. These barriers can be addressed through strengthening leadership [ 86 , 87 ], CQI-based mentoring [ 94 ], periodic monitoring, supportive supervision and coaching [ 43 , 53 , 87 , 92 , 95 ], participation, empowerment, and accountability [ 67 ], involving all stakeholders in decision-making [ 86 , 87 ], a provider-payer partnership [ 64 ], and compensating staff for after-hours meetings on CQI [ 85 ]. The strategic dimension, characterized by a strategic plan and integrated CQI efforts, is devoted to processes that are central to achieving strategic priorities. Roles and responsibilities are defined in terms of integrated strategic and quality-related goals [ 115 ].

The utmost goal of CQI has been to improve the quality of care, which is usually revealed by structure, process, and outcome. After resolving challenges and effectively using tools and running models, the goal of CQI reflects the ultimate reason and purpose of its implementation. First, effectively implemented CQI initiatives can improve leadership, health financing, health workforce development, health information technology, and availability of supplies as the building blocks of a health system [ 31 , 48 , 53 , 68 , 98 ]. Second, effectively implemented CQI initiatives improved care delivery process (counselling, adherence with standards, coordination, collaboration, and linkages) [ 48 , 53 , 65 , 68 ]. Third, the CQI can improve outputs of healthcare delivery, such as satisfaction, accessibility (timely access, utilization), continuity of care, safety, efficiency, and acceptability [ 52 , 54 , 55 , 76 , 78 ]. Finally, the effectiveness of the CQI initiatives has been tested in enhancing responses related to key aspects of the HIV response, maternal and child health, non-communicable disease control, and others (e.g., surgery and peritonitis). However, it is worth noting that CQI initiative has not always been effective. For instance, CQI using a two- to nine-times audit cycle model through systems assessment tools did not bring significant change to increase syphilis testing performance [ 116 ]. This study was conducted within the context of Aboriginal and Torres Strait Islander people’s primary health care settings. Notably, ‘the clinics may not have consistently prioritized syphilis testing performance in their improvement strategies, as facilitated by the CQI program’ [ 116 ]. Additionally, by applying CQI-based mentoring, uptake of facility-based interventions was not significantly improved, though it was effective in increasing community health worker visits during pregnancy and the postnatal period, knowledge about maternal and child health and exclusive breastfeeding practice, and HIV disclosure status [ 117 ]. The study conducted in South Africa revealed no significant association between the coverage of facility-based interventions and Continuous Quality Improvement (CQI) implementation. This lack of association was attributed to the already high antenatal and postnatal attendance rates in both control and intervention groups at baseline, leaving little room for improvement. Additionally, the coverage of HIV interventions remained consistently high throughout the study period [ 117 ].

Regarding health care and policy implications, CQI has played a vital role in advancing PHC and fostering the realization of UHC goals worldwide. The indicators found in Donabedian’s framework that are positively influenced by CQI efforts are comparable to those included in the PHC performance initiative’s conceptual framework [ 29 , 118 , 119 ]. It is clearly explained that PHC serves as the roadmap to realizing the vision of UHC [ 120 , 121 ]. Given these circumstances, implementing CQI can contribute to the achievement of PHC principles and the objectives of UHC. For instance, by implementing CQI methods, countries have enhanced the accessibility, affordability, and quality of PHC services, leading to better health outcomes for their populations. CQI has facilitated identifying and resolving healthcare gaps and inefficiencies, enabling countries to optimize resource allocation and deliver more effective and patient-centered care. However, it is crucial to recognize that the successful implementation of Continuous Quality Improvement (CQI) necessitates optimizing the duration of each cycle, understanding challenges and barriers that extend beyond the health system and settings, and acknowledging that its effectiveness may be compromised if these challenges are not adequately addressed.

Despite abundant literature, there are still gaps regarding the relationship between CQI and other dimensions within the healthcare system. No studies have examined the impact of CQI initiatives on catastrophic health expenditure, effective service coverage, patient-centredness, comprehensiveness, equity, health security, and responsiveness.

Limitations

In conducting this review, it has some limitations to consider. Firstly, only articles published in English were included, which may introduce the exclusion of relevant non-English articles. Additionally, as this review follows a scoping methodology, the focus is on synthesising available evidence rather than critically evaluating or scoring the quality of the included articles.

Continuous quality improvement is investigated as a continuous and ongoing intervention, where the implementation time can vary across different cycles. The CQI team and implementation timelines were critical elements of CQI in different models. Among the commonly used approaches, the PDSA or PDCA is frequently employed. In most CQI models, a wide range of tools, nineteen tools, are commonly utilized to support the improvement process. Cultural, technical, structural, and strategic barriers and facilitators are significant in implementing CQI initiatives. Implementing the CQI initiative aims to improve health system blocks, enhance health service delivery process and output, and ultimately prevent morbidity and reduce mortality. For future researchers, considering that CQI is context-dependent approach, conducting scale-up implementation research about catastrophic health expenditure, effective service coverage, patient-centredness, comprehensiveness, equity, health security, and responsiveness across various settings and health issues would be valuable.

Availability of data and materials

The data used and/or analyzed during the current study are available in this manuscript and/or the supplementary file.

Shewhart WA, Deming WE. Memoriam: Walter A. Shewhart, 1891–1967. Am Stat. 1967;21(2):39–40.

Article   Google Scholar  

Shewhart WA. Statistical method from the viewpoint of quality control. New York: Dover; 1986. ISBN 978-0486652320. OCLC 13822053. Reprint. Originally published: Washington, DC: Graduate School of the Department of Agriculture, 1939.

Moen R, editor Foundation and History of the PDSA Cycle. Asian network for quality conference Tokyo. https://www.deming.org/sites/default/files/pdf/2015/PDSA_History_Ron_MoenPdf . 2009.

Kuperman G, James B, Jacobsen J, Gardner RM. Continuous quality improvement applied to medical care: experiences at LDS hospital. Med Decis Making. 1991;11(4suppl):S60–65.

Article   CAS   PubMed   Google Scholar  

Singh J, Singh H. Continuous improvement philosophy–literature review and directions. Benchmarking: An International Journal. 2015;22(1):75–119.

Goldstone J. Presidential address: Sony, Porsche, and vascular surgery in the 21st century. J Vasc Surg. 1997;25(2):201–10.

Radawski D. Continuous quality improvement: origins, concepts, problems, and applications. J Physician Assistant Educ. 1999;10(1):12–6.

Shortell SM, O’Brien JL, Carman JM, Foster RW, Hughes E, Boerstler H, et al. Assessing the impact of continuous quality improvement/total quality management: concept versus implementation. Health Serv Res. 1995;30(2):377.

CAS   PubMed   PubMed Central   Google Scholar  

Lohr K. Quality of health care: an introduction to critical definitions, concepts, principles, and practicalities. Striving for quality in health care. 1991.

Berwick DM. The clinical process and the quality process. Qual Manage Healthc. 1992;1(1):1–8.

Article   CAS   Google Scholar  

Gift B. On the road to TQM. Food Manage. 1992;27(4):88–9.

CAS   PubMed   Google Scholar  

Greiner A, Knebel E. The core competencies needed for health care professionals. health professions education: A bridge to quality. 2003:45–73.

McCalman J, Bailie R, Bainbridge R, McPhail-Bell K, Percival N, Askew D et al. Continuous quality improvement and comprehensive primary health care: a systems framework to improve service quality and health outcomes. Front Public Health. 2018:6 (76):1–6.

Sheingold BH, Hahn JA. The history of healthcare quality: the first 100 years 1860–1960. Int J Afr Nurs Sci. 2014;1:18–22.

Google Scholar  

Donabedian A. Evaluating the quality of medical care. Milbank Q. 1966;44(3):166–206.

Institute of Medicine (US) Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington (DC): National Academies Press (US). 2001. 2, Improving the 21st-century Health Care System. Available from: https://www.ncbi.nlm.nih.gov/books/NBK222265/ .

Rubinstein A, Barani M, Lopez AS. Quality first for effective universal health coverage in low-income and middle-income countries. Lancet Global Health. 2018;6(11):e1142–1143.

Article   PubMed   Google Scholar  

Agency for Healthcare Reserach and Quality. Quality Improvement and monitoring at your fingertips USA,: Agency for Healthcare Reserach and Quality. 2022. Available from: https://qualityindicators.ahrq.gov/ .

Anderson CA, Cassidy B, Rivenburgh P. Implementing continuous quality improvement (CQI) in hospitals: lessons learned from the International Quality Study. Qual Assur Health Care. 1991;3(3):141–6.

Gardner K, Mazza D. Quality in general practice - definitions and frameworks. Aust Fam Physician. 2012;41(3):151–4.

PubMed   Google Scholar  

Loper AC, Jensen TM, Farley AB, Morgan JD, Metz AJ. A systematic review of approaches for continuous quality improvement capacity-building. J Public Health Manage Pract. 2022;28(2):E354.

Hill JE, Stephani A-M, Sapple P, Clegg AJ. The effectiveness of continuous quality improvement for developing professional practice and improving health care outcomes: a systematic review. Implement Sci. 2020;15(1):1–14.

Candas B, Jobin G, Dubé C, Tousignant M, Abdeljelil AB, Grenier S, et al. Barriers and facilitators to implementing continuous quality improvement programs in colonoscopy services: a mixed methods systematic review. Endoscopy Int Open. 2016;4(02):E118–133.

Peters MD, Marnie C, Colquhoun H, Garritty CM, Hempel S, Horsley T, et al. Scoping reviews: reinforcing and advancing the methodology and application. Syst Reviews. 2021;10(1):1–6.

Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32.

Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73.

McGowan J, Straus S, Moher D, Langlois EV, O’Brien KK, Horsley T, et al. Reporting scoping reviews—PRISMA ScR extension. J Clin Epidemiol. 2020;123:177–9.

Donabedian A. Explorations in quality assessment and monitoring: the definition of quality and approaches to its assessment. Health Administration Press, Ann Arbor. 1980;1.

World Health Organization. Operational framework for primary health care: transforming vision into action. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF); 2020 [updated 14 December 2020; cited 2023 Nov Oct 17]. Available from: https://www.who.int/publications/i/item/9789240017832 .

The Joanna Briggs Institute. The Joanna Briggs Institute Reviewers’ Manual :2014 edition. Australia: The Joanna Briggs Institute. 2014:88–91.

Rihal CS, Kamath CC, Holmes DR Jr, Reller MK, Anderson SS, McMurtry EK, et al. Economic and clinical outcomes of a physician-led continuous quality improvement intervention in the delivery of percutaneous coronary intervention. Am J Manag Care. 2006;12(8):445–52.

Ade-Oshifogun JB, Dufelmeier T. Prevention and Management of Do not return notices: a quality improvement process for Supplemental staffing nursing agencies. Nurs Forum. 2012;47(2):106–12.

Rubenstein L, Khodyakov D, Hempel S, Danz M, Salem-Schatz S, Foy R, et al. How can we recognize continuous quality improvement? Int J Qual Health Care. 2014;26(1):6–15.

O’Neill SM, Hempel S, Lim YW, Danz MS, Foy R, Suttorp MJ, et al. Identifying continuous quality improvement publications: what makes an improvement intervention ‘CQI’? BMJ Qual Saf. 2011;20(12):1011–9.

Article   PubMed   PubMed Central   Google Scholar  

Sibthorpe B, Gardner K, McAullay D. Furthering the quality agenda in Aboriginal community controlled health services: understanding the relationship between accreditation, continuous quality improvement and national key performance indicator reporting. Aust J Prim Health. 2016;22(4):270–5.

Bennett CL, Crane JM. Quality improvement efforts in oncology: are we ready to begin? Cancer Invest. 2001;19(1):86–95.

VanValkenburgh DA. Implementing continuous quality improvement at the facility level. Adv Ren Replace Ther. 2001;8(2):104–13.

Loper AC, Jensen TM, Farley AB, Morgan JD, Metz AJ. A systematic review of approaches for continuous quality improvement capacity-building. J Public Health Manage Practice. 2022;28(2):E354–361.

Ryan M. Achieving and sustaining quality in healthcare. Front Health Serv Manag. 2004;20(3):3–11.

Nicolucci A, Allotta G, Allegra G, Cordaro G, D’Agati F, Di Benedetto A, et al. Five-year impact of a continuous quality improvement effort implemented by a network of diabetes outpatient clinics. Diabetes Care. 2008;31(1):57–62.

Wakefield BJ, Blegen MA, Uden-Holman T, Vaughn T, Chrischilles E, Wakefield DS. Organizational culture, continuous quality improvement, and medication administration error reporting. Am J Med Qual. 2001;16(4):128–34.

Sori DA, Debelew GT, Degefa LS, Asefa Z. Continuous quality improvement strategy for increasing immediate postpartum long-acting reversible contraceptive use at Jimma University Medical Center, Jimma, Ethiopia. BMJ Open Qual. 2023;12(1):e002051.

Roche B, Robin C, Deleaval PJ, Marti MC. Continuous quality improvement in ambulatory surgery: the non-attending patient. Ambul Surg. 1998;6(2):97–100.

O’Connor JB, Sondhi SS, Mullen KD, McCullough AJ. A continuous quality improvement initiative reduces inappropriate prescribing of prophylactic antibiotics for endoscopic procedures. Am J Gastroenterol. 1999;94(8):2115–21.

Ursu A, Greenberg G, McKee M. Continuous quality improvement methodology: a case study on multidisciplinary collaboration to improve chlamydia screening. Fam Med Community Health. 2019;7(2):e000085.

Quick B, Nordstrom S, Johnson K. Using continuous quality improvement to implement evidence-based medicine. Lippincotts Case Manag. 2006;11(6):305–15 ( quiz 16 – 7 ).

Oyeledun B, Phillips A, Oronsaye F, Alo OD, Shaffer N, Osibo B, et al. The effect of a continuous quality improvement intervention on retention-in-care at 6 months postpartum in a PMTCT Program in Northern Nigeria: results of a cluster randomized controlled study. J Acquir Immune Defic Syndr. 2017;75(Suppl 2):S156–164.

Nyengerai T, Phohole M, Iqaba N, Kinge CW, Gori E, Moyo K, et al. Quality of service and continuous quality improvement in voluntary medical male circumcision programme across four provinces in South Africa: longitudinal and cross-sectional programme data. PLoS ONE. 2021;16(8):e0254850.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Wang J, Zhang H, Liu J, Zhang K, Yi B, Liu Y, et al. Implementation of a continuous quality improvement program reduces the occurrence of peritonitis in PD. Ren Fail. 2014;36(7):1029–32.

Stikes R, Barbier D. Applying the plan-do-study-act model to increase the use of kangaroo care. J Nurs Manag. 2013;21(1):70–8.

Wagner AD, Mugo C, Bluemer-Miroite S, Mutiti PM, Wamalwa DC, Bukusi D, et al. Continuous quality improvement intervention for adolescent and young adult HIV testing services in Kenya improves HIV knowledge. AIDS. 2017;31(Suppl 3):S243–252.

Le RD, Melanson SE, Santos KS, Paredes JD, Baum JM, Goonan EM, et al. Using lean principles to optimise inpatient phlebotomy services. J Clin Pathol. 2014;67(8):724–30.

Manyazewal T, Mekonnen A, Demelew T, Mengestu S, Abdu Y, Mammo D, et al. Improving immunization capacity in Ethiopia through continuous quality improvement interventions: a prospective quasi-experimental study. Infect Dis Poverty. 2018;7:7.

Kamiya Y, Ishijma H, Hagiwara A, Takahashi S, Ngonyani HAM, Samky E. Evaluating the impact of continuous quality improvement methods at hospitals in Tanzania: a cluster-randomized trial. Int J Qual Health Care. 2017;29(1):32–9.

Kibbe DC, Bentz E, McLaughlin CP. Continuous quality improvement for continuity of care. J Fam Pract. 1993;36(3):304–8.

Adrawa N, Ongiro S, Lotee K, Seret J, Adeke M, Izudi J. Use of a context-specific package to increase sputum smear monitoring among people with pulmonary tuberculosis in Uganda: a quality improvement study. BMJ Open Qual. 2023;12(3):1–6.

Hunt P, Hunter SB, Levan D. Continuous quality improvement in substance abuse treatment facilities: how much does it cost? J Subst Abuse Treat. 2017;77:133–40.

Azadeh A, Ameli M, Alisoltani N, Motevali Haghighi S. A unique fuzzy multi-control approach for continuous quality improvement in a radio therapy department. Qual Quantity. 2016;50(6):2469–93.

Memiah P, Tlale J, Shimabale M, Nzyoka S, Komba P, Sebeza J, et al. Continuous quality improvement (CQI) institutionalization to reach 95:95:95 HIV targets: a multicountry experience from the Global South. BMC Health Serv Res. 2021;21(1):711.

Yapa HM, De Neve JW, Chetty T, Herbst C, Post FA, Jiamsakul A, et al. The impact of continuous quality improvement on coverage of antenatal HIV care tests in rural South Africa: results of a stepped-wedge cluster-randomised controlled implementation trial. PLoS Med. 2020;17(10):e1003150.

Dadi TL, Abebo TA, Yeshitla A, Abera Y, Tadesse D, Tsegaye S, et al. Impact of quality improvement interventions on facility readiness, quality and uptake of maternal and child health services in developing regions of Ethiopia: a secondary analysis of programme data. BMJ Open Qual. 2023;12(4):e002140.

Weinberg M, Fuentes JM, Ruiz AI, Lozano FW, Angel E, Gaitan H, et al. Reducing infections among women undergoing cesarean section in Colombia by means of continuous quality improvement methods. Arch Intern Med. 2001;161(19):2357–65.

Andreoni V, Bilak Y, Bukumira M, Halfer D, Lynch-Stapleton P, Perez C. Project management: putting continuous quality improvement theory into practice. J Nurs Care Qual. 1995;9(3):29–37.

Balfour ME, Zinn TE, Cason K, Fox J, Morales M, Berdeja C, et al. Provider-payer partnerships as an engine for continuous quality improvement. Psychiatric Serv. 2018;69(6):623–5.

Agurto I, Sandoval J, De La Rosa M, Guardado ME. Improving cervical cancer prevention in a developing country. Int J Qual Health Care. 2006;18(2):81–6.

Anderson CI, Basson MD, Ali M, Davis AT, Osmer RL, McLeod MK, et al. Comprehensive multicenter graduate surgical education initiative incorporating entrustable professional activities, continuous quality improvement cycles, and a web-based platform to enhance teaching and learning. J Am Coll Surg. 2018;227(1):64–76.

Benjamin S, Seaman M. Applying continuous quality improvement and human performance technology to primary health care in Bahrain. Health Care Superv. 1998;17(1):62–71.

Byabagambi J, Marks P, Megere H, Karamagi E, Byakika S, Opio A, et al. Improving the quality of voluntary medical male circumcision through use of the continuous quality improvement approach: a pilot in 30 PEPFAR-Supported sites in Uganda. PLoS ONE. 2015;10(7):e0133369.

Hogg S, Roe Y, Mills R. Implementing evidence-based continuous quality improvement strategies in an urban Aboriginal Community Controlled Health Service in South East Queensland: a best practice implementation pilot. JBI Database Syst Rev Implement Rep. 2017;15(1):178–87.

Hopper MB, Morgan S. Continuous quality improvement initiative for pressure ulcer prevention. J Wound Ostomy Cont Nurs. 2014;41(2):178–80.

Ji J, Jiang DD, Xu Z, Yang YQ, Qian KY, Zhang MX. Continuous quality improvement of nutrition management during radiotherapy in patients with nasopharyngeal carcinoma. Nurs Open. 2021;8(6):3261–70.

Chen M, Deng JH, Zhou FD, Wang M, Wang HY. Improving the management of anemia in hemodialysis patients by implementing the continuous quality improvement program. Blood Purif. 2006;24(3):282–6.

Reeves S, Matney K, Crane V. Continuous quality improvement as an ideal in hospital practice. Health Care Superv. 1995;13(4):1–12.

Barton AJ, Danek G, Johns P, Coons M. Improving patient outcomes through CQI: vascular access planning. J Nurs Care Qual. 1998;13(2):77–85.

Buttigieg SC, Gauci D, Dey P. Continuous quality improvement in a Maltese hospital using logical framework analysis. J Health Organ Manag. 2016;30(7):1026–46.

Take N, Byakika S, Tasei H, Yoshikawa T. The effect of 5S-continuous quality improvement-total quality management approach on staff motivation, patients’ waiting time and patient satisfaction with services at hospitals in Uganda. J Public Health Afr. 2015;6(1):486.

PubMed   PubMed Central   Google Scholar  

Jacobson GH, McCoin NS, Lescallette R, Russ S, Slovis CM. Kaizen: a method of process improvement in the emergency department. Acad Emerg Med. 2009;16(12):1341–9.

Agarwal S, Gallo J, Parashar A, Agarwal K, Ellis S, Khot U, et al. Impact of lean six sigma process improvement methodology on cardiac catheterization laboratory efficiency. Catheter Cardiovasc Interv. 2015;85:S119.

Rahul G, Samanta AK, Varaprasad G A Lean Six Sigma approach to reduce overcrowding of patients and improving the discharge process in a super-specialty hospital. In 2020 International Conference on System, Computation, Automation and Networking (ICSCAN) 2020 July 3 (pp. 1-6). IEEE

Patel J, Nattabi B, Long R, Durey A, Naoum S, Kruger E, et al. The 5 C model: A proposed continuous quality improvement framework for volunteer dental services in remote Australian Aboriginal communities. Community Dent Oral Epidemiol. 2023;51(6):1150–8.

Van Acker B, McIntosh G, Gudes M. Continuous quality improvement techniques enhance HMO members’ immunization rates. J Healthc Qual. 1998;20(2):36–41.

Horine PD, Pohjala ED, Luecke RW. Healthcare financial managers and CQI. Healthc Financ Manage. 1993;47(9):34.

Reynolds JL. Reducing the frequency of episiotomies through a continuous quality improvement program. CMAJ. 1995;153(3):275–82.

Bunik M, Galloway K, Maughlin M, Hyman D. First five quality improvement program increases adherence and continuity with well-child care. Pediatr Qual Saf. 2021;6(6):e484.

Boyle TA, MacKinnon NJ, Mahaffey T, Duggan K, Dow N. Challenges of standardized continuous quality improvement programs in community pharmacies: the case of SafetyNET-Rx. Res Social Adm Pharm. 2012;8(6):499–508.

Price A, Schwartz R, Cohen J, Manson H, Scott F. Assessing continuous quality improvement in public health: adapting lessons from healthcare. Healthc Policy. 2017;12(3):34–49.

Gage AD, Gotsadze T, Seid E, Mutasa R, Friedman J. The influence of continuous quality improvement on healthcare quality: a mixed-methods study from Zimbabwe. Soc Sci Med. 2022;298:114831.

Chan YC, Ho SJ. Continuous quality improvement: a survey of American and Canadian healthcare executives. Hosp Health Serv Adm. 1997;42(4):525–44.

Balas EA, Puryear J, Mitchell JA, Barter B. How to structure clinical practice guidelines for continuous quality improvement? J Med Syst. 1994;18(5):289–97.

ElChamaa R, Seely AJE, Jeong D, Kitto S. Barriers and facilitators to the implementation and adoption of a continuous quality improvement program in surgery: a case study. J Contin Educ Health Prof. 2022;42(4):227–35.

Candas B, Jobin G, Dubé C, Tousignant M, Abdeljelil A, Grenier S, et al. Barriers and facilitators to implementing continuous quality improvement programs in colonoscopy services: a mixed methods systematic review. Endoscopy Int Open. 2016;4(2):E118–133.

Brandrud AS, Schreiner A, Hjortdahl P, Helljesen GS, Nyen B, Nelson EC. Three success factors for continual improvement in healthcare: an analysis of the reports of improvement team members. BMJ Qual Saf. 2011;20(3):251–9.

Lee S, Choi KS, Kang HY, Cho W, Chae YM. Assessing the factors influencing continuous quality improvement implementation: experience in Korean hospitals. Int J Qual Health Care. 2002;14(5):383–91.

Horwood C, Butler L, Barker P, Phakathi S, Haskins L, Grant M, et al. A continuous quality improvement intervention to improve the effectiveness of community health workers providing care to mothers and children: a cluster randomised controlled trial in South Africa. Hum Resour Health. 2017;15(1):39.

Hyrkäs K, Lehti K. Continuous quality improvement through team supervision supported by continuous self-monitoring of work and systematic patient feedback. J Nurs Manag. 2003;11(3):177–88.

Akdemir N, Peterson LN, Campbell CM, Scheele F. Evaluation of continuous quality improvement in accreditation for medical education. BMC Med Educ. 2020;20(Suppl 1):308.

Barzansky B, Hunt D, Moineau G, Ahn D, Lai CW, Humphrey H, et al. Continuous quality improvement in an accreditation system for undergraduate medical education: benefits and challenges. Med Teach. 2015;37(11):1032–8.

Gaylis F, Nasseri R, Salmasi A, Anderson C, Mohedin S, Prime R, et al. Implementing continuous quality improvement in an integrated community urology practice: lessons learned. Urology. 2021;153:139–46.

Gaga S, Mqoqi N, Chimatira R, Moko S, Igumbor JO. Continuous quality improvement in HIV and TB services at selected healthcare facilities in South Africa. South Afr J HIV Med. 2021;22(1):1202.

Wang F, Yao D. Application effect of continuous quality improvement measures on patient satisfaction and quality of life in gynecological nursing. Am J Transl Res. 2021;13(6):6391–8.

Lee SB, Lee LL, Yeung RS, Chan J. A continuous quality improvement project to reduce medication error in the emergency department. World J Emerg Med. 2013;4(3):179–82.

Chiang AA, Lee KC, Lee JC, Wei CH. Effectiveness of a continuous quality improvement program aiming to reduce unplanned extubation: a prospective study. Intensive Care Med. 1996;22(11):1269–71.

Chinnaiyan K, Al-Mallah M, Goraya T, Patel S, Kazerooni E, Poopat C, et al. Impact of a continuous quality improvement initiative on appropriate use of coronary CT angiography: results from a multicenter, statewide registry, the advanced cardiovascular imaging consortium (ACIC). J Cardiovasc Comput Tomogr. 2011;5(4):S29–30.

Gibson-Helm M, Rumbold A, Teede H, Ranasinha S, Bailie R, Boyle J. A continuous quality improvement initiative: improving the provision of pregnancy care for Aboriginal and Torres Strait Islander women. BJOG: Int J Obstet Gynecol. 2015;122:400–1.

Bennett IM, Coco A, Anderson J, Horst M, Gambler AS, Barr WB, et al. Improving maternal care with a continuous quality improvement strategy: a report from the interventions to minimize preterm and low birth weight infants through continuous improvement techniques (IMPLICIT) network. J Am Board Fam Med. 2009;22(4):380–6.

Krall SP, Iv CLR, Donahue L. Effect of continuous quality improvement methods on reducing triage to thrombolytic interval for Acute myocardial infarction. Acad Emerg Med. 1995;2(7):603–9.

Swanson TK, Eilers GM. Physician and staff acceptance of continuous quality improvement. Fam Med. 1994;26(9):583–6.

Yu Y, Zhou Y, Wang H, Zhou T, Li Q, Li T, et al. Impact of continuous quality improvement initiatives on clinical outcomes in peritoneal dialysis. Perit Dial Int. 2014;34(Suppl 2):S43–48.

Schiff GD, Goldfield NI. Deming meets Braverman: toward a progressive analysis of the continuous quality improvement paradigm. Int J Health Serv. 1994;24(4):655–73.

American Hospital Association Division of Quality Resources Chicago, IL: The role of hospital leadership in the continuous improvement of patient care quality. American Hospital Association. J Healthc Qual. 1992;14(5):8–14,22.

Scriven M. The Logic and Methodology of checklists [dissertation]. Western Michigan University; 2000.

Hales B, Terblanche M, Fowler R, Sibbald W. Development of medical checklists for improved quality of patient care. Int J Qual Health Care. 2008;20(1):22–30.

Vermeir P, Vandijck D, Degroote S, Peleman R, Verhaeghe R, Mortier E, et al. Communication in healthcare: a narrative review of the literature and practical recommendations. Int J Clin Pract. 2015;69(11):1257–67.

Eljiz K, Greenfield D, Hogden A, Taylor R, Siddiqui N, Agaliotis M, et al. Improving knowledge translation for increased engagement and impact in healthcare. BMJ open Qual. 2020;9(3):e000983.

O’Brien JL, Shortell SM, Hughes EF, Foster RW, Carman JM, Boerstler H, et al. An integrative model for organization-wide quality improvement: lessons from the field. Qual Manage Healthc. 1995;3(4):19–30.

Adily A, Girgis S, D’Este C, Matthews V, Ward JE. Syphilis testing performance in Aboriginal primary health care: exploring impact of continuous quality improvement over time. Aust J Prim Health. 2020;26(2):178–83.

Horwood C, Butler L, Barker P, Phakathi S, Haskins L, Grant M, et al. A continuous quality improvement intervention to improve the effectiveness of community health workers providing care to mothers and children: a cluster randomised controlled trial in South Africa. Hum Resour Health. 2017;15:1–11.

Veillard J, Cowling K, Bitton A, Ratcliffe H, Kimball M, Barkley S, et al. Better measurement for performance improvement in low- and middle-income countries: the primary Health Care Performance Initiative (PHCPI) experience of conceptual framework development and indicator selection. Milbank Q. 2017;95(4):836–83.

Barbazza E, Kringos D, Kruse I, Klazinga NS, Tello JE. Creating performance intelligence for primary health care strengthening in Europe. BMC Health Serv Res. 2019;19(1):1006.

Assefa Y, Hill PS, Gilks CF, Admassu M, Tesfaye D, Van Damme W. Primary health care contributions to universal health coverage. Ethiopia Bull World Health Organ. 2020;98(12):894.

Van Weel C, Kidd MR. Why strengthening primary health care is essential to achieving universal health coverage. CMAJ. 2018;190(15):E463–466.

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Endalamaw, A., Khatri, R.B., Mengistu, T.S. et al. A scoping review of continuous quality improvement in healthcare system: conceptualization, models and tools, barriers and facilitators, and impact. BMC Health Serv Res 24 , 487 (2024). https://doi.org/10.1186/s12913-024-10828-0

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A New Model for Studying Social Isolation and Health in People with Serious Mental Illnesses

Researchers have developed a promising new framework for studying the link between social disconnection and poor physical health in people living with serious mental illnesses (SMI). Drawing on published research from animal models and data from the general population, this framework builds on existing social isolation and loneliness models by integrating insights from evolutionary and cognitive theories. This research was supported by the Office of Behavioral and Social Sciences Research and the National Institute of Mental Health.

What were the researchers studying and why?

One of the most challenging aspects of living with SMI is difficulties with social perception, motivation, and social behaviors. These difficulties can lead to social withdrawal and loneliness, outcomes that can contribute to poor heart health and early death. However, researchers have an incomplete understanding of how differences in the brain functions in people living with SMIs impact the connection between their social perception and self-reported, lived experience of social withdrawal, isolation, or loneliness.

How did the researchers conduct the study?

Researchers from Boston University and Harvard Medical School conducted a selective narrative review of studies addressing social withdrawal, isolation, loneliness, and health in SMI.

Their review highlighted evidence indicating differences in brain activity between people experiencing loneliness and those who are not, particularly in regions associated with social cognitive processes. Additionally, neuroimaging studies have shown increased activation in brain areas responsible for risk assessment among lonely individuals.

Furthermore, the researchers discussed findings suggesting that individuals experiencing loneliness, who perceive others negatively and exhibit signs of psychopathology, may misinterpret social cues, leading to social disconnection. Over time, this social disconnection can prompt a defensive response to social situations, further reducing motivation for social interaction.

What did the study results show?

Based on a synthesis of recent findings that indicate a causal relationship between loneliness and nervous system responses in the human body that cause inflammation and reduce immunity, the authors developed a testable model of the psychological and neural mechanisms of social disconnection in SMI. They hypothesize that people living with SMI are more likely to experience high levels of chronic psychological stress and therefore, more likely to experience persistently high levels of physiological inflammation. Stress and inflammation biomarkers can serve as indicators of an unmet need for social connection. Health providers and caregivers could use these indicators to provide social support and connection to those experiencing this need.

What is the potential impact of these findings?

The authors suggest that once their hypothesis has been rigorously tested and verified, new methods to improve health outcomes for people living with SMI may be developed, including potential “just-in-time” digital interventions through mobile devices. The authors also suggest that people living with SMI and experiencing loneliness can receive interventions that address any potential negative beliefs they hold about rejection, thus interrupting the cycle of social isolation.

Citation: Fulford D, Holt DJ. Social Withdrawal, Loneliness, and Health in Schizophrenia: Psychological and Neural Mechanisms . Schizophr Bull. 2023 Sep 7;49(5):1138-1149. doi: 10.1093/schbul/sbad099. PMID: 37419082; PMCID: PMC10483452.

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AI for Earth: How NASA’s Artificial Intelligence and Open Science Efforts Combat Climate Change

As extreme weather events increase around the world due to climate change, the need for further research into our warming planet has increased as well. For NASA, climate research involves not only conducting studies of these events, but also empowering outside researchers to do the same. The artificial intelligence (AI) efforts spearheaded by the agency offer a powerful tool to accomplish these goals.

In 2023, NASA teamed up with IBM Research to create an AI geospatial foundation model. Trained on vast amounts of NASA’s widely used Harmonized Landsat and Sentinel-2 (HLS) data, the model provides a base for a variety of AI-powered studies to tackle environmental challenges. In keeping with open science principles, the model is freely available for anyone to access .

Foundation models serve as a baseline from which scientists can develop a diverse set of applications, enabling powerful and efficient solutions. “Foundation models only know what things are represented in the data,” explained Manil Maskey, the data science lead at NASA’s Office of the Chief Science Data Officer (OCSDO). “It’s like a Swiss Army Knife—it can be used for multiple different things.”

Once a foundation model is created, it can be trained on a small amount of data to perform a specific task. To date, the Interagency Implementation and Advanced Concept Team (IMPACT) along with collaborators have demonstrated the geospatial foundation model’s capabilities by fine-tuning it to detect burn scars, to delineate flood water, and to classify crop and other land use categories.

Because of the computational resources required to create the initial foundation model, a partnership was necessary for success. In this case, NASA brought the data and scientific knowledge, while IBM brought the computing power and AI algorithm optimization expertise. The team’s shared commitment to making their research accessible through open science principles ensures that their model can be useful to as many researchers as possible.

“To build a foundation model at scale, we realized early on that it's not feasible for one institution to build it,” Maskey said. “Everything we have done on our foundation models has been open to the public, all the way from pre-training data, code, best practices, model weights, fine-tuning training data, and publications. There’s transparency, so researchers can trace why certain things were used in terms of data or model architecture.”

Following on from the success of their geospatial foundation model, NASA and IBM Research are continuing their partnership to create a new, similar model for weather and climate studies. They are collaborating with Oak Ridge National Laboratory (ORNL), NVIDIA, and several universities to bring this model to life.

This time, the main dataset will be the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) , a huge collection of atmospheric reanalysis data that spans from 1980 to the present day. Like the geospatial foundation model, the weather and climate model is being developed with an open science approach, and will be available to the public in the near future.

Covering all aspects of Earth science would take several foundation models trained on different types of datasets. However, Maskey believes those future models might someday be combined into one comprehensive model, leading to a “digital twin” of the Earth that would provide unparalleled analysis and predictions for all kinds of climate and environmental events.

Whatever innovations the future holds, NASA and IBM’s geospatial and climate foundation models will enable leaps in Earth science like never before. Though powerful AI tools will enhance researchers’ work, the team’s dedication to open science supercharges the possibilities for discovery by allowing anyone to put those tools into practice and pave the way for groundbreaking research to help better care for the planet.

For more information about open science at NASA, visit: https://science.nasa.gov/open-science/

By Lauren Leese Web Content Strategist for the Office of the Chief Science Data Officer

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Original research

Evidence-based practice models and frameworks in the healthcare setting: a scoping review, jarrod dusin.

1 Department of Evidence Based Practice, Children’s Mercy Hospitals and Clinics, Kansas City, Missouri, USA

2 Therapeutic Science, The University of Kansas Medical Center, Kansas City, Kansas, USA

Andrea Melanson

Lisa mische-lawson, associated data.

bmjopen-2022-071188supp001.pdf

bmjopen-2022-071188supp002.pdf

No data are available.

The aim of this scoping review was to identify and review current evidence-based practice (EBP) models and frameworks. Specifically, how EBP models and frameworks used in healthcare settings align with the original model of (1) asking the question, (2) acquiring the best evidence, (3) appraising the evidence, (4) applying the findings to clinical practice and (5) evaluating the outcomes of change, along with patient values and preferences and clinical skills.

A Scoping review.

Included sources and articles

Published articles were identified through searches within electronic databases (MEDLINE, EMBASE, Scopus) from January 1990 to April 2022. The English language EBP models and frameworks included in the review all included the five main steps of EBP. Excluded were models and frameworks focused on one domain or strategy (eg, frameworks focused on applying findings).

Of the 20 097 articles found by our search, 19 models and frameworks met our inclusion criteria. The results showed a diverse collection of models and frameworks. Many models and frameworks were well developed and widely used, with supporting validation and updates. Some models and frameworks provided many tools and contextual instruction, while others provided only general process instruction. The models and frameworks reviewed demonstrated that the user must possess EBP expertise and knowledge for the step of assessing evidence. The models and frameworks varied greatly in the level of instruction to assess the evidence. Only seven models and frameworks integrated patient values and preferences into their processes.

Many EBP models and frameworks currently exist that provide diverse instructions on the best way to use EBP. However, the inclusion of patient values and preferences needs to be better integrated into EBP models and frameworks. Also, the issues of EBP expertise and knowledge to assess evidence must be considered when choosing a model or framework.

STRENGTHS AND LIMITATIONS OF THIS STUDY

• Currently, no comprehensive review exists of evidence-based practice (EBP) models and frameworks.
• Well-developed models and frameworks may have been excluded for not including all five steps of original model for EBP.
• This review did not measure the quality of the models and frameworks based on validated studies.

Introduction

Evidence-based practice (EBP) grew from evidence-based medicine (EBM) to provide a process to review, translate and implement research with practice to improve patient care, treatment and outcomes. Guyatt 1 coined the term EBM in the early 1990s. Over the last 25 years, the field of EBM has continued to evolve and is now a cornerstone of healthcare and a core competency for all medical professionals. 2 3 At first, the term EBM was used only in medicine. However, the term EBP now applies to the principles of other health professions. This expansion of the concept of EBM increases its complexity. 4 The term EBP is used for this paper because it is universal across professions.

Early in the development of EBP, Sackett 5 created an innovative five-step model. This foundational medical model provided a concise overview of the process of EBP. The five steps are (1) asking the question, (2) acquiring the best evidence, (3) appraising the evidence, (4) applying the findings to clinical practice and (5) evaluating the outcomes of change. Other critical components of Sackett’s model are considering patient value and preferences and clinical skills with the best available evidence. 5 The influence of this model has led to its integration and adaption into every field of healthcare. Historically, the foundation of EBP has focused on asking the question, acquiring the literature and appraising the evidence but has had difficulty integrating evidence into practice. 6 Although the five steps appear simple, each area includes a vast number of ways to review the literature (eg, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), Newcastle-Ottawa Scale) and entire fields of study, such as implementation science, a field dedicated to implementing EBP. 7 8 Implementation science can be traced to the 1960s with Everett Rogers’ Diffusion of Innovation Theory and has grown alongside EBP over the last 25 years. 7 9

One way to manage the complexity of EBP in healthcare is by developing EBP models and frameworks that establish strategies to determine resource needs, identify barriers and facilitators, and guide processes. 10 EBP models and frameworks provide insight into the complexity of transforming evidence into clinical practice. 11 They also allow organisations to determine readiness, willingness and potential outcomes for a hospital system. 12 EBP can differ from implementation science, as EBP models include all five of Sackett’s steps of EBP, while the non-process models of implementation science typically focus on the final two steps. 5 10 There are published scoping reviews of implementation science, 13 however, no comprehensive review of EBP models and frameworks currently exists. Although there is overlap of EBP, implementation science and knowledge translation models and frameworks 10 14 the purpose of the scoping review was to explore how EBP models and frameworks used in healthcare settings align with the original EBP five-step model.

A scoping review synthesises findings across various study types and provides a broad overview of the selected topic. 15 The Arksey and O’Malley method and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR) procedures guided this review (see online supplemental PRISMA-ScR checklist ). 15 16 The primary author established the research question and inclusion and exclusion criteria before conducting the review. An a priori protocol was not pre-registered. One research question guided the review: Which EBP models and frameworks align with Sackett’s original model?

Supplementary data

Eligibility criteria.

To be included in the review, English language published EBP models and frameworks needed to include the five main steps of EBP (asking the question, acquiring the best evidence, appraising the evidence, applying the findings to clinical practice and assessing the outcomes of change) based on Sackett’s model. 5 If the models or frameworks involved identifying problems or measured readiness for change, the criteria of ‘asking the question’ was met. Exclusions included models or frameworks focused on one domain or strategy (eg, frameworks focused on applying findings). Also, non-peer-reviewed abstracts, letters, editorials, opinion articles, and dissertations were excluded.

Search and selection

To identify potential studies, a medical librarian searched the databases from January 1990 to April 2022 in MEDLINE, EMBASE and Scopus in collaboration with the primary author. The search was limited to 1990 because the term EBP was coined in the early 90s. The search strategy employed the following keywords: ‘Evidence-Based Practice’ OR ‘evidence based medicine’ OR ‘evidence-based medicine’ OR ‘evidence based nursing’ OR ‘evidence-based nursing’ OR ‘evidence based practice’ OR ‘evidence-based practice’ OR ‘evidence based medicine’ OR ‘evidence-based medicine’ OR ‘evidence based nursing’ OR ‘evidence-based nursing’ OR ‘evidence based practice’ OR ‘evidence-based practice’ AND ‘Hospitals’ OR ‘Hospital Medicine’ OR ‘Nursing’ OR ‘Advanced Practice Nursing’ OR ‘Academic Medical Centers’ OR ‘healthcare’ OR ‘hospital’ OR ‘healthcare’ OR ‘hospital’ AND ‘Models, Organizational’ OR ‘Models, Nursing’ OR ‘framework’ OR ‘theory’ OR ‘theories’ OR ‘model’ OR ‘framework’ OR ‘theory’ OR ‘theories’ OR ‘model’. Additionally, reference lists in publications included for full-text review were screened to identify eligible models and frameworks (see online supplemental appendix A for searches).

Selection of sources of evidence

Two authors (JD and AM) independently screened titles and abstracts and selected studies for potential inclusion in the study, applying the predefined inclusion and exclusion criteria. Both authors then read the full texts of these articles to assess eligibility for final inclusion. Disagreement between the authors regarding eligibility was resolved by consensus between the three authors (JD, AM and LM-L). During the selection process, many models and frameworks were found more than once. Once a model or framework article was identified, the seminal article was reviewed for inclusion. If models or frameworks had been changed or updated since the publication of their seminal article, the most current iteration published was reviewed for inclusion. Once a model or framework was identified and verified for inclusion, all other articles listing the model or framework were excluded. This scoping review intended to identify model or framework aligned with Sackett’s model; therefore, analysing every article that used the included model or framework was unnecessary (see online supplemental appendix B for tracking form).

Data extraction and analysis

Data were collected on the following study characteristics: (1) authors, (2) publication year, (3) model or framework and (4) area(s) of focus in reference to Sackett’s five-step model. After initial selection, models and frameworks were analysed for key features and alignment to the five-step EBP process. A data analysis form was developed to map detailed information (see online supplemental appendix C for full data capture form). Data analysis focused on identifying (1) the general themes of the model or frameworks, and (2) any knowledge gaps. Data extraction and analysis were done by the primary author (JD) and verified by one other author (AM). 15

Patient and public involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

The search identified 6523 potentially relevant references (see figure 1 ). Following a review of the titles and abstracts, the primary author completed a more detailed screening of 37 full papers. From these, 19 models and frameworks were included. Table 1 summarises the 19 models and frameworks. Of the 19 models and frameworks assessed and mapped, 15 had broad target audiences, including healthcare or public health organisations or health systems. Only five models and frameworks included a target audience of individual clinicians (eg, physicians and nurses). 17–22

Retrieval and selection process.

Models and frameworks organised by integration of patient preferences and values

EBP, evidence-based practice.

Asking the question

All 19 models and frameworks included a process for asking questions. Most focused on identifying problems that needed to be addressed on an organisational or hospital level. Five used the PICO (population, intervention, comparator, outcome) format to ask specific questions related to patient care. 19–25

Acquiring the evidence

The models and frameworks gave basic instructions on acquiring literature, such as ‘conduct systematic search’ or ‘acquire resource’. 20 Four recommended sources from previously generated evidence, such as guidelines and systematic reviews. 6 21 22 26 Although most models and frameworks did not provide specifics, others suggested this work be done through EBP mentors/experts. 20 21 25 27 Seven models included qualitative evidence in the use of evidence, 6 19 21 24 27–29 while only four models considered the use of patient preference and values as evidence. 21 22 24 27 Six models recommended internal data be used in acquiring information. 17 20–22 24 27

Assessing the evidence

The models and frameworks varied greatly in the level of instruction provided in assessing the best evidence. All provided a general overview in assessing and grading the evidence. Four recommended this work be done by EBP mentors and experts. 20 25 27 30 Seven models developed specific tools to be used to assess the levels of evidence. 6 17 21 22 24 25 27

Applying the evidence

The application of evidence also varied greatly for the different models and frameworks. Seven models recommended pilot programmes to implement change. 6 21–25 31 Five recommended the use of EBP mentors and experts to assist in the implementation of evidence and quality improvement as a strategy of the models and frameworks. 20 24 25 27 Thirteen models and frameworks discussed patient values and preferences, 6 17–19 21–27 31 32 but only seven incorporated this topic into the model or framework, 21–27 and only five included tools and instructions. 21–25 Twelve of the 20 models discussed using clinical skill, but specifics of how this was incorporated was lacking in models and frameworks. 6 17–19 21–27 31

Evaluating the outcomes of change

Evaluation varied among the models and frameworks, but most involved using implementation outcome measures to determine the project’s success. Five models and frameworks provide tools and in-depth instruction for evaluation. 21 22 24–26 Monash Partners Learning Health Systems provided detailed instruction on using internal institutional data to determine success of application. 26 This framework uses internal and external data along with evidence in decision making as a benchmark for successful implementation.

EBP models and frameworks provide a process for transforming evidence into clinical practice and allow organisations to determine readiness and willingness for change in a complex hospital system. 12 The large number of models and frameworks complicates the process by confusing what the best tool is for healthcare organisations. This review examined many models and frameworks and assessed the characteristics and gaps that can better assist healthcare organisations to determine the right tool for themselves. This review identified 19 EBP models and frameworks that included the five main steps of EBP as described by Sackett. 5 The results showed that the themes of the models and frameworks are as diverse as the models and frameworks themselves. Some are well developed and widely used, with supporting validation and updates. 21 22 24 27 One such model, the Iowa EBP model, has received over 3900 requests for permission to use it and has been updated from its initial development and publication. 24 Other models provided tools and contextual instruction such as the Johns Hopkin’s model which includes a large number of supporting tools for developing PICOs, instructions for grading literature and project implementation. 17 21 22 24 27 By contrast, the ACE Star model and the An Evidence Implementation Model for Public Health Systems only provide high level overview and general instructions compared with other models and frameworks. 19 29 33

Gaps in the evidence

A consistent finding in research of clinician experience with EBP is the lack of expertise that is needed to assess the literature. 24 34 35 The models and frameworks reviewed demonstrated that the user must possess the knowledge and related skills for this step in the process. The models and frameworks varied greatly in the level of instruction to assess the evidence. Most provided a general overview in assessing and grading the evidence, though a few recommended that this work be done by EBP mentors and experts. 20 25 27 ARCC, JBI and Johns Hopkins provided robust tools and resources that would require administrative time and financial support. 21 22 27 Some models and frameworks offered vital resources or pointed to other resources for assessing evidence, 24 but most did not. While a few used mentors and experts to assist with assessing the literature, a majority did not address this persistent issue.

Sackett’s five-step model included another important consideration when implementing EBP: patient values and preferences. One criticism of EBP is that it ignores patient values and preferences. 36 Over half of the models and frameworks reported the need to include patient values and preferences, but the tools, instruction or resources for including them were limited. The ARCC model integrates patient preferences and values into the model, but it is up to the EBP mentor to accomplish this task. 37 There are many tools for assessing evidence, but few models and frameworks provide this level of guidance for incorporating patient preference and values. The inclusion of patient and family values and preferences can be misunderstood, insincere, and even tokenistic but without it there is reduced chance of success of implementation of EBP. 38 39

Strengths and limitations

Similar to other well-designed scoping reviews, the strengths of this review include a rigorous search conducted by a skilled librarian, literature evaluation by more than one person, and the utilisation of an established methodological framework (PRISMA-ScR). 14 15 Additionally, utilising the EBP five-step models as a point of alignment allows for a more comprehensive breakdown and established reference points for the reviewed models and frameworks. While scoping reviews have been completed on implementation science and knowledge translation models and framework, to our knowledge, this is the first scoping review of EBP models and frameworks. 13 14 Limitations of the study include that well-developed models and frameworks may have been excluded for not including all five steps. 40 For example, the Promoting Action on Research Implementation in Health Services (PARIHS) framework is a well-developed and validated implementation framework but did not include all five steps of an EBP model. 40 Also, some models and frameworks have been studied and validated over many years. It was beyond the scope of the review to measure the quality of the models and frameworks based on these other validated studies.

Implications and future research

Healthcare organisations can support EBP by choosing a model or framework that best suits their environment and providing clear guidance for implementing the best evidence. Some organisations may find the best fit with the ARCC and the Clinical Scholars Model because of the emphasis on mentors or the Johns Hopkins model for its tools for grading the level of evidence. 21 25 27 In contrast, other organisations may find the Iowa model useful with its feedback loops throughout its process. 24

Another implication of this study is the opportunity to better define and develop robust tools for patient and family values and preferences within EBP models and frameworks. Patient experiences are complex and require thorough exploration, so it is not overlooked, which is often the case. 39 41 The utilisation of EBP models and frameworks provide an opportunity to explore this area and provide the resources and understanding that are often lacking. 38 Though varying, models such as the Iowa Model, JBI and Johns Hopkins developed tools to incorporate patient and family values and preferences, but a majority of the models and frameworks did not. 21 22 24 An opportunity exists to create broad tools that can incorporate patient and family values and preferences into EBP to a similar extent as many of the models and frameworks used for developing tools for literature assessment and implementation. 21–25

Future research should consider appraising the quality and use of the different EBP models and frameworks to determine success. Additionally, greater clarification on what is considered patient and family values and preferences and how they can be integrated into the different models and frameworks is needed.

This scoping review of 19 models and frameworks shows considerable variation regarding how the EBP models and frameworks integrate the five steps of EBP. Most of the included models and frameworks provided a narrow description of the steps needed to assess and implement EBP, while a few provided robust instruction and tools. The reviewed models and frameworks provided diverse instructions on the best way to use EBP. However, the inclusion of patient values and preferences needs to be better integrated into EBP models. Also, the issues of EBP expertise to assess evidence must be considered when selecting a model or framework.

Supplementary Material

Acknowledgments.

We thank Keri Swaggart for completing the database searches and the Medical Writing Center at Children's Mercy Kansas City for editing this manuscript.

Contributors: All authors have read and approved the final manuscript. JD conceptualised the study design, screened the articles for eligibility, extracted data from included studies and contributed to the writing and revision of the manuscript. LM-L conceptualised the study design, provided critical feedback on the manuscript and revised the manuscript. AM screened the articles for eligibility, extracted data from the studies, provided critical feedback on the manuscript and revised the manuscript. JD is the guarantor of this work.

Funding: The article processing charges related to the publication of this article were supported by The University of Kansas (KU) One University Open Access Author Fund sponsored jointly by the KU Provost, KU Vice Chancellor for Research, and KUMC Vice Chancellor for Research and managed jointly by the Libraries at the Medical Center and KU - Lawrence

Disclaimer: No funding agencies had input into the content of this manuscript.

Competing interests: None declared.

Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review: Not commissioned; externally peer reviewed.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Data availability statement

Ethics statements, patient consent for publication.

Not applicable.

Groundwater salinization risk assessment using combined artificial intelligence models

• Research Article
• Published: 28 April 2024

Cite this article

• Oussama Dhaoui 1 , 2 ,
• Isabel Margarida Antunes 2 ,
• Ines Benhenda 1 ,
• Belgacem Agoubi 1 &
• Adel Kharroubi 1  

Assessing the risk of groundwater contamination is of crucial importance for the management of water resources, particularly in arid regions such as Menzel Habib (south-eastern Tunisia). The aim of this research is to create and validate artificial intelligence models based on the original DRASTIC vulnerability methodology to explain groundwater salinization risk (GSR). To this end, several algorithms, such as artificial neural networks (ANN), support vector regression (SVR), and multiple linear regression (MLR), were applied to the Menzel Habib aquifer system. The results obtained indicate that the DRASTIC Vulnerability Index (VI) ranges from 91 to 141 and is classified into two categories: low and moderate vulnerability. However, the correlation between groundwater total dissolved solids (TDS) and the Vulnerability Index is relatively weak ( r < 0.5). Indeed, the original DRASTIC index needs some improvements. To improve it, some adjustments are required, notably by incorporating the TDS-groundwater salinization risk (GSR) indicator. The seven parameters of the original DRASTIC model were used as inputs for the artificial intelligence models, while the GSR values were used as outputs. Performance indicators, such as the correlation coefficient ( r ) and the Willmott Agreement Index ( d ), showed that the ANN model outperformed the SVR and MLR models. Indeed, during the training phase, the ANN model obtained r values equal to 0.89 and d values of 0.4, demonstrating the superiority, robustness, and accuracy of ANN-based methodologies over the original DRASTIC model. The findings could provide valuable information to guide management of groundwater contamination risks, especially in arid regions.

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Data availability

Geochemical data were generated at the Applied Hydrosciences Laboratory, Higher Institute of Water Sciences and Techniques of Gabès, Tunisia. Derived data supporting the findings of this study are available from the corresponding author on request.

Abduljaleel Y, Amiri M, Amen EM, Salem A, Ali ZF, Awd A, Lóczy D, Ghzal M (2024) Enhancing groundwater vulnerability assessment for improved environmental management: addressing a critical environmental concern. Environ Sci Pollut Res 31:19185–19205. https://doi.org/10.1007/s11356-024-32305-1

Article   CAS   Google Scholar  

Abu M, Akurugu BA, Egbueri JC (2024b) Understanding groundwater mineralization controls and the implications on its quality (Southwestern Ghana): insights from hydrochemistry, multivariate statistics, and multi-linear regression models. Acta Geophys. https://doi.org/10.1007/s11600-023-01271-6

Abu M, Musah R, Zango MS (2024a) A combination of multivariate statistics and machine learning techniques in groundwater characterization and quality forecasting. Geosyst Geoenviron 3(2):100261. https://doi.org/10.1016/j.geogeo.2024.100261

Article   Google Scholar  

Albuquerque MTD, Roque N, Rodrigues J, Antunes IMHR, Silva C (2021) DRASTICAI, a new index for groundwater vulnerability assessment - a Portuguese case study. Geosciences 11(6):228. https://doi.org/10.3390/geosciences11060228

Aller L, Bennet T, Lehr HJ, Petty RJ, Hackett G (1987) DRASTIC: a standardized system for evaluating groundwater pollution potential using hydrogeological settings. Report EPA–600/2–87–035. Environmental Research Laboratory, United States Environmental Protection Agency, Corvallis, p 622

Google Scholar  

Al-Ruzouq R, Shanableh A, Jena R, Mukherjee S, Khalil MA, Gibril MBA, Pradhan B, Hammouri NA (2024) Hybrid deep learning and remote sensing for the delineation of artificial groundwater recharge zones. Egypt J Remote Sens Space Sci 27(2):178–191. https://doi.org/10.1016/j.ejrs.2024.02.006

Amiri V, Li P, Bhattacharya P, Nakhaei M (2021) Mercury pollution in the coastal Urmia aquifer in northwestern Iran: potential sources, mobility, and toxicity. Environ Sci Pollut Res 28(14):17546–17562. https://doi.org/10.1007/s11356-020-11865-y

Antunes IMHR, Albuquerque MTD (2013) Using indicator kriging for the evaluation of arsenic potential contamination in an abandoned mining area (Portugal). Sci Total Environ 442:545–552. https://doi.org/10.1016/j.scitotenv.2012.10.010

Ashrafzadeh A, Roshandel F, Khaledian M, Vazifedoust M, Rezaei M (2016) Assessment of groundwater salinity risk using kriging methods: a case study in northern Iran. Agric Water Manag 178:215–224. https://doi.org/10.1016/j.agwat.2016.09.028

Ayadi M (1987) Etude de la nappe phréatique de Segui El Hamma-Menzel Habib. DGRE. p 30

Barzegar R, Moghaddam AA, Deo R, Fijani E, Tziritis E (2018) Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms. Sci Total Environ 621:697–712. https://doi.org/10.1016/j.scitotenv.2017.11.185

Ben Cheikh N (2013) Etude des relations hydrodynamiques entre la nappe profonde de Sfax et les systèmes aquifères méridionaux (Menzel Habib et Gabès Nord): origines et mécanismes de minéralisation des eaux souterraines. Unpublished PhD. Thesis, University of Sfax, Tunisia, p 161

Bertrand G, Petelet-Giraud E, Cary L, Hirata R, Montenegro S, Paiva A, Mahlknecht J, Coelho V, Almeida C (2021) Delineating groundwater contamination risks in southern coastal metropoles through implementation of geochemical and socio-environmental data in decision-tree and geographical information system. Water Res 209:117877. https://doi.org/10.1016/j.watres.2021.117877

Cao H, Xie X, Shi J, Wang Y (2022) Evaluating the validity of class balancing algorithms-based machine learning models for geogenic contaminated groundwaters prediction. J Hydrol 610:127933. https://doi.org/10.1016/j.jhydrol.2022.127933

Chachadi A.G., Lobo-Ferreira, J.P. 2001. Sea water intrusion vulnerability mapping of aquifers isung GALDIT method. Proc. Workshop on modeling in hydrogeology, Anna University, Chennai, pp.143-156, and in COASTIN A Coastal Policy Research Newsletter, Number 4, March 2001. New Delhi, TERI, pp. 7-9, (cf. http://www.teriin.org/teri-wr/coastin/newslett/coastin4.pdf )

Civita M (1994) Aquifer vulnerability maps to pollution. Pitagora Ed, Bologna

Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1007/BF00994018

Das R, Saha S (2022) Spatial mapping of groundwater potentiality applying ensemble of computational intelligence and machine learning approaches. Groundw Sustain Dev 18:100778. https://doi.org/10.1016/j.gsd.2022.100778

Dhaoui O, Antunes IMHR, Agoubi B (2021a) Sustainability and management of the Menzel Habib aquifer system, southeastern Tunisia. In: Abrunhosa M, Chambel A, Peppoloni S, Chaminé HI (eds) Advances in geoethics and groundwater management: theory and practice for a sustainable development. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-59320-9_110

Chapter   Google Scholar  

Dhaoui O, Antunes IMHR, Agoubi B, Kharroubi A (2021b) Geochemical processes of groundwater salinization in an arid area, southeastern Tunisia. Arab J Geosci 14:1721. https://doi.org/10.1007/s12517-021-08155-3

Dhaoui O, Antunes IMHR, Agoubi B, Kharroubi A (2022) Integration of water contamination indicators and vulnerability indices on groundwater management in Menzel Habib area, south-eastern Tunisia. Environ Res 205:112491. https://doi.org/10.1016/j.envres.2021.112491

Dhaoui O, Antunes IMHR, Agoubi B, Tlig L, Kharroubi A (2023a) Groundwater quality for irrigation in an arid region—application of fuzzy logic techniques. Environ Sci Pollut Res 30:29773–29789. https://doi.org/10.1007/s11356-022-24334-5

Dhaoui O, Antunes IMHR, Boente C, Agoubi B, Kharroubi A (2023b) Hydrogeochemical processes on inland aquifer systems: a combined multivariate statistical technique and isotopic approach. Groundw Sustain Dev 20:100887. https://doi.org/10.1016/j.gsd.2022.100887

Di Nunno F, Granata F (2020) Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network. Environ Res 190:110062. https://doi.org/10.1016/j.envres.2020.110062

Doerfliger N, Zwahlen F (1997) EPIK: A New method for outlining of protection areas in karstic environment. IntSymp on Karst Water and Environmental Impacts, Antalya, Turkey. Balkema, Rotterdam, pp 117–123

Durango-Cordero J, Saqalli M, Ferrant S, Bonilla S, Maurice L, Arellano P, Elger A (2022) Risk assessment of unlined oil pits leaking into groundwater in the Ecuadorian Amazon: a modified GIS-DRASTIC approach. Appl Geogr 139:102628. https://doi.org/10.1016/j.apgeog.2021.102628

Ehteram M, Ahmed AN, Kumar P, Sherif M, El-Shafie A (2021) Predicting freshwater production and energy consumption in a seawater greenhouse based on ensemble frameworks using optimized multi-layer perceptron. Energy Rep 7:6308–6326. https://doi.org/10.1016/j.egyr.2021.09.079

El Fadili H, Ben Ali M, El Mahi M, Cooray AT, ElMostapha L (2022) A comprehensive health risk assessment and groundwater quality for irrigation and drinking purposes around municipal solid waste sanitary landfill: a case study in Morocco. Environ Nanotechnol Monit Manag 18:100698. https://doi.org/10.1016/j.enmm.2022.100698

Elzain HE, Chung SY, Senapathi V, Sekar S, Lee SY, Roy PD, Hassan A, Sabarathinam C (2022) Comparative study of machine learning models for evaluating groundwater vulnerability to nitrate contamination. Ecotoxicol Environ Saf 229:113061. https://doi.org/10.1016/j.ecoenv.2021.113061

Faryabi M, Rahimi MH (2024) Factors controlling groundwater quality and salinization near the salt playa of Kavir-e Daranjir, central part of Iran. Sustain Water Resour Manag 10:99. https://doi.org/10.1007/s40899-024-01078-3

Gani A, Singh M, Pathak S, Hussain A (2024) Groundwater Quality Index development using the ANN model of Delhi Metropolitan City. Environmental Science Pollution Research, India. https://doi.org/10.1007/s11356-023-31584-4

Book   Google Scholar  

Ganwer S, Sinha MK, Multaniya AP, Ghodichore N (2024) Introducing reverse multi influencing factor technique in DRASTIC model for groundwater vulnerability assessment. Groundw Sustain Dev 25:101106. https://doi.org/10.1016/j.gsd.2024.101106

Gautam A, Rai SC, Rai SP, Ram K, Sanny (2022) Impact of anthropogenic and geological factors on groundwater hydrochemistry in the unconfined aquifers of Indo-Gangetic Plain. Phys Chem Earth 126:103109. https://doi.org/10.1016/j.pce.2022.103109

Gholami V, Booij MJ (2022) Use of machine learning and geographical information system to predict nitrate concentration in an unconfined aquifer in Iran. J Clean Prod 360:131847. https://doi.org/10.1016/j.jclepro.2022.131847

Ghouili N, Horriche FJ, Azaza FH, Zaghrarni MF, Ribeiro L, Zammouri M (2021) Groundwater vulnerability mapping using the Susceptibility Index (SI) method: Case study of Takelsa aquifer, Northeastern Tunisia. J Af Earth Sci 173:104035. https://doi.org/10.1016/j.jafrearsci.2020.104035

Gomes LA, Barbosa NS, Debruyne D, Barbosa N, Moitinho DER, Peixoto R, Santos CB, Peixinho MAL (2023) Hydrogeochemical processes and groundwater evolution of the São Sebastião-Marizal aquifer system in the Tucano Central Basin, Bahia, Brazil. J South Am Earth Sci 127:104413. https://doi.org/10.1016/j.jsames.2023.104413

Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press

Gueddari H, Akodad M, Baghour M, Moumen A, Skalli A, ElYousfi Y, Ismail A, Chahban M, Azizi G, Ait Hmeid H, Zahid M (2022) The salinity origin and hydrogeochemical evolution of groundwater in the Oued Kert basin, north-eastern of Morocco. Sci Afr 16:e01226. https://doi.org/10.1016/j.sciaf.2022.e01226

Guenther N, Schonlau M (2016) Support vector machines. Stata J 16(4):917–937. https://doi.org/10.1177/1536867X1601600407

IRA - Institut des Régions Arides, (2011). Etat de référence environnemental 2010 dans l’observatoire de Menzel Habib, gouvernorat de Gabès, sud-est de la Tunisie.

Jain S, Rathee S, Kumar A, Sambasivam A, Boadh R, Choudhary T, Kumar P, Singh PK (2022) Prediction of temperature for various pressure levels using ANN and multiple linear regression techniques: a case study. Mater Today Proc 56(1):194–199. https://doi.org/10.1016/j.matpr.2022.01.067

Jaishi HP, Singh S, Tiwari RP, Tiwari RC (2024) Comparing wavelet-based artificial neural network, multiple linear regression, and ARIMA models for detecting genuine radon anomalies associated with seismic events. Proc Indian Natl Sci Acad. https://doi.org/10.1007/s43538-024-00239-4

Jang CS (2023) Geostatistical estimates of groundwater nitrate-nitrogen concentrations with spatial auxiliary information on DRASTIC-LU-based aquifer contamination vulnerability. Environ Sci Pollut Res 30:81113–81130. https://doi.org/10.1007/s11356-023-28208-2

Jia Z, Bian J, Wang Y, Wana H, Sun X, Li Q (2019) Assessment and validation of groundwater vulnerability to nitrate in porous aquifers based on a DRASTIC method modified by projection pursuit dynamic clustering model. J Contam Hydrol 226:103522. https://doi.org/10.1016/j.jconhyd.2019.103522

Jiang Q, Liu Q, Liu Y, Chai H, Zhu J (2024) Groundwater chemical characteristic analysis and water source identification model study in Gubei coal mine, Northern Anhui Province, China. Heliyon 10(5):e26925. https://doi.org/10.1016/j.heliyon.2024.e26925

Kassem Y, Gökçekuş H, Mosbah AAS (2023) Prediction of monthly precipitation using various artificial models and comparison with mathematical models. Environ Sci Pollut Res 30:41209–41235. https://doi.org/10.1007/s11356-022-24912-7

Kavzoglu T, Colkesen I, Sahin EK (2019) Machine learning techniques in landslide susceptibility mapping: a survey and a case study. In: Pradhan S, Vishal V, Singh T (eds) Landslides: theory, practice and modelling. Advances in Natural and Technological Hazards Research, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-77377-3_13

Keita S, Zhonghua T (2017) The assessment of processes controlling the spatial distribution of hydrogeochemical groundwater types in Mali using multivariate statistics. J Afr Earth Sci 134:573–589. https://doi.org/10.1016/j.jafrearsci.2017.07.023

Khosravi K, Sartaj M, Tsai FTC, Singh VP, Kazakis N, Melesse AM, Prakash I, Bui DT, Pham BT (2018) A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment. Sci Total Environ 642:1032–1049. https://doi.org/10.1016/j.scitotenv.2018.06.130

Kumar P, Sharma R, Bhaumik S (2022) MCDA techniques used in optimization of weights and ratings of DRASTIC model for groundwater vulnerability assessment. Data Sci Manag 5(1):28–41. https://doi.org/10.1016/j.dsm.2022.03.004

Laghrib F, Bahaj T, El Kasmi S, Hilali M, Kacimi I, Nouayti N, Dakak H, Bouzekraoui M, El Fatni O, Hammani O (2024) Hydrogeochemical study of groundwater in arid and semi-arid regions of the Infracenomanian aquifers (Cretaceous Errachidia basin, Southeastern Morocco), using hydrochemical modeling and multivariate statistical analysis. J Afr Earth Sci 209:105132. https://doi.org/10.1016/j.jafrearsci.2023.105132

Li W, Bao L, Yao G, Wang F, Guo Q, Zhu J, Zhu J, Wang Z, Bi J, Zhu C, Zhong Y, Lu S (2024) The analysis on groundwater storage variations from GRACE/GRACE-FO in recent 20 years driven by influencing factors and prediction in Shandong Province, China. Sci Rep 14:5819. https://doi.org/10.1038/s41598-024-55588-3

Li X, Zhang Y, Li Z, Wang R (2021) Response of the groundwater environment to rapid urbanization in Hohhot, the provincial capital of western China. J Hydrol 603, Part C:127033. https://doi.org/10.1016/j.jhydrol.2021.127033

Liu D, Zhang W, Tang Y, Xie B, Shi Q, Cao K (2024) Evolving support vector regression based on improved grey wolf optimization for predicting settlement during construction of high-filled roadbed. Transport Geotech 45:101233. https://doi.org/10.1016/j.trgeo.2024.101233

Liu J, Meng X, Ma Y, Liu X (2020) Introduce canopy temperature to evaluate actual evapotranspiration of green peppers using optimized ENN models. J Hydrol 590:125437. https://doi.org/10.1016/j.jhydrol.2020.125437

Luo D, Ma C, Qiu Y, Zhang Z, Wang L (2023) Groundwater vulnerability assessment using AHP-DRASTIC-GALDIT comprehensive model: a case study of Binhai New Area, Tianjin, China. Environ Monit Assess 195:268. https://doi.org/10.1007/s10661-022-10894-z

Luo M, Zhang Y, Li H, Hu W, Xiao K, Yu S, Zheng C, Wang X (2021) Pollution assessment and sources of dissolved heavy metals in coastal water of a highly urbanized coastal area: the role of groundwater discharge. Sci Total Environ 807, Part 3:151070. https://doi.org/10.1016/j.scitotenv.2021.151070

Mejri S, Chekirbene A, Tsujimura M, Boughdiri M, Mlayah A (2018) Tracing groundwater salinization processes in an inland aquifer: a hydrogeochemical and isotopic approach in Sminja aquifer (Zaghouan, northeast of Tunisia). J Afr Earth Sci 147:511–522. https://doi.org/10.1016/j.jafrearsci.2018.07.009

Meng J, Hu K, Wang S, Wang Y, Chen Z, Gao C, Mao D (2024) A framework for risk assessment of groundwater contamination integrating hydrochemical, hydrogeological, and electrical resistivity tomography method. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-024-33030-5

Mishra D, Chakrabortty R, Sen K, Pal SC, Mondal NK (2023) Groundwater vulnerability assessment of elevated arsenic in Gangetic Plain of West Bengal, India; using primary information, lithological transport, state-of-the-art approaches. J Contam Hydrol 256:104195. https://doi.org/10.1016/j.jconhyd.2023.104195

Motlagh ZK, Derakhshani R, Sayadi MH (2023) Groundwater vulnerability assessment in central Iran: integration of GIS-based DRASTIC model and a machine learning approach. Groundw Sustain Dev 101037. https://doi.org/10.1016/j.gsd.2023.101037

Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogrammetry Remote Sensing 66(3):247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001

Mousavi M, Qaderi F, Ahmadi A (2023) Spatial prediction of temporary and permanent hardness concentrations in groundwater based on chemistry parameters by artificial intelligence. Int J Environ Sci Technol 20:6665–6684. https://doi.org/10.1007/s13762-023-04934-5

Msengi CM, Mjemah IC, Makoba EE, Mussa KR (2024) Hydrogeochemical characterization and assessment of factors controlling groundwater salinity in the Chamwino granitic complex, central Tanzania. Helyion 10(7):e28187. https://doi.org/10.1016/j.heliyon.2024.e28187

Muhammad A, Danbatta SJ, Muhammad IY, Nasidi II (2024) Exploring soil radon (Rn) concentrations and their connection to geological and meteorological factors. Environ Sci Pollut Res 31:565–578. https://doi.org/10.1007/s11356-023-31237-6

NASA 2018. POWER project - prediction of worldwide energy resources. https://power.larc.nasa.gov/data-access-viewer/ , accessed on July 2020.

Nguyen AH, Hong Tat VM, Hoang TTT (2024a) Assessing groundwater vulnerability and addressing salinization in the coastal region of Ba Ria–Vung Tau province, Vietnam: an enhanced DRASTIC model approach. Environ Earth Sci 83:53. https://doi.org/10.1007/s12665-023-11343-x

Nguyen HD, Nguyen QH, Dang DK, Nguyen TG, Truong QH, Nguyen VH, Bretcan P, Șerban G, Bui QT, Petrisor AI (2024b) Integrated machine learning and remote sensing for groundwater potential mapping in the Mekong Delta in Vietnam. Acta Geophys. https://doi.org/10.1007/s11600-024-01331-5

Nourani V, Fard MS (2012) Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes. Adv Eng Softw 47(1):127–146. https://doi.org/10.1016/j.advengsoft.2011.12.014

Nourani V, Maleki S, Najafi H, Baghanam AH (2023) A fuzzy logic-based approach for groundwater vulnerability assessment. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-023-26236-6

Oliveira GAB, Cardoso RA, Júnior RCSF, Doca T, Araújo JA (2023) On the generalization capability of artificial neural networks used to estimate fretting fatigue life. Tribol Int 192:109222. https://doi.org/10.1016/j.triboint.2023.109222

Ouled Ghrib A, Slimane MF (1994) Nouvelles données géologiques sur l’Atlas méridional de la Tunisie: mise en évidence du Trias dans la chaîne de Gafsa. Notes de service géologique de Tunisie 60:5–10

Ourarhi, S., Barkaoui, AE., Zarhloule, Y., Kadiri, M., Bouiss, H., 2023. Groundwater vulnerability assessment in the Triffa Plain based on GIS combined with DRASTIC, SINTACS, and GOD models. Model Earth Syst Environ https://doi.org/10.1007/s40808-023-01801-7

Ozegin KO, Ilugbo SO, Adebo B (2024) Spatial evaluation of groundwater vulnerability using the DRASTIC-L model with the analytic hierarchy process (AHP) and GIS approaches in Edo State, Nigeria. Phys Chem Earth 134:103562. https://doi.org/10.1016/j.pce.2024.103562

Panahi M, Sadhasivam N, Pourghasemi HR, Rezaie F, Lee S (2020) Spatial predic-tion of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR). J Hydrol 588:125033. https://doi.org/10.1016/j.jhydrol.2020.125033

Paul S, Das CS (2021) An investigation of groundwater vulnerability in the North 24 parganas district using DRASTIC and hybrid-DRASTIC models: a case study. Environ Adv 5:100093. https://doi.org/10.1016/j.envadv.2021.100093

Persaud E, Levison J (2021) Impacts of changing watershed conditions in the assessment of future groundwater contamination risk. J Hydrol 603, Part D:127142. https://doi.org/10.1016/j.jhydrol.2021.127142

Qian H, Li P, Howard KW, Yang C, Zhang X (2012) Assessment of groundwater vulnerability in the Yinchuan Plain, northwest China using OREADIC. Environ Monit Assess 184(6):3613–3628. https://doi.org/10.1007/s10661-011-2211-7

Qiao L, Inoue J, Zhu J (2024) Machine learning guided constitutive model and processing map for Fe2Ni2CrAl1.2 multi-principle element alloys. J Mater Res Technol 29:353–363. https://doi.org/10.1016/j.jmrt.2024.01.119

Raisa SS, Sarkar SK, Sadiq MA (2024) Advancing groundwater vulnerability assessment in Bangladesh: a comprehensive machine learning approach. Groundwater Sustain Dev 25: 101128. https://doi.org/10.1016/j.gsd.2024.101128

Rakib MA, Sasaki J, Matsuda H, Quraishi SB, Mahmud MJ, Bodrud-Dozag M, Atique Ullah AKM, Fatema KJ, Newaz MA, Bhuiyan MAH (2020) Groundwater salinization and associated co-contamination risk increase severe drinking water vulnerabilities in the southwestern coast of Bangladesh. Chemosphere 246:125646. https://doi.org/10.1016/j.chemosphere.2019.125646

Sahour H, Gholami V, Vazifedan M (2020) A comparative analysis of statistical and machine learning techniques for mapping the spatial distribution of groundwater salinity in a coastal aquifer. J Hydrol 591:125321. https://doi.org/10.1016/j.jhydrol.2020.125321

Saroughi M, Mirzania E, Achite M, Katipoğlu OM, Ehteram M (2024) Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz Plain, Iran). Environ Monit Assess 196:227. https://doi.org/10.1007/s10661-024-12357-z

Sarvaiya J, Singh D (2023) Prediction of performance parameters in friction stir processing using ANN and multiple regression models. Mater Today Proc. https://doi.org/10.1016/j.matpr.2023.04.422

Sattari MT, Apaydin H, Band SS, Mosavi A, Prasad R (2021) Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation. Hydrol Earth Syst Sci 25:603–618. https://doi.org/10.5194/hess-25-603-2021

Selvakumar S, Chandrasekar N, Srinivas Y, Selvam S, Kaliraj S, Magesh NS, Venkatramanan S (2021) Hydrogeochemical processes controlling the groundwater salinity in the coastal aquifers of Southern Tamil Nadu, India. Mar Pollut Bull 174:113264. https://doi.org/10.1016/j.marpolbul.2021.113264

Siarkos I, Arfaoui M, Tzoraki O, Zammouri M, Azaza FH (2023) Implementation and evaluation of different techniques to modify DRASTIC method for groundwater vulnerability assessment: a case study from Bouficha aquifer, Tunisia. Environ Sci Pollut Res 30:89459–89478. https://doi.org/10.1007/s11356-023-28625-3

Sihag P, Angelaki A, Chaplot B (2020) Estimation of the recharging rate of groundwater using random forest technique. Appl Water Sci 10:1–11. https://doi.org/10.1007/s13201-020-01267-3

Singh G, Singh J, Wani OA, Egbueri JC, Agbasi JC (2024) Assessment of groundwater suitability for sustainable irrigation: a comprehensive study using indexical, statistical, and machine learning approaches. Groundw Sustain Dev 24:101059. https://doi.org/10.1016/j.gsd.2023.101059

Singha S, Pasupuleti S, Singha SS, Kumar S (2021) Prediction of groundwater quality using efficient machine learning technique. Chemosphere 276:130265. https://doi.org/10.1016/j.chemosphere.2021.130265

Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88

Soued C, Bogard MJ, Finlay K, Bortolotti LE, Leavitt PR, Badiou P, Knox SH, Jensen S, Mueller P, Lee SC, Ng D, Wissel B, Chan CN, Page B, Kowal P (2024) Salinity causes widespread restriction of methane emissions from small inland waters. Nat Commun 15:717. https://doi.org/10.1038/s41467-024-44715-3

Srinivas Y, Raj AS, Olivier DH, Muthuraj D, Chandraseka N (2012) A robust behavior of feed forward back propagation algorithm of artificial neural networks in the application of vertical electrical sounding data inversion. Geosci Front 3(5):729–736. https://doi.org/10.1016/j.gsf.2012.02.003

Stempvoort DV, Ewert L, Wassenaar L (1993) Aquifer vulnerability index: a GIS - compatible method for groundwater vulnerability mapping Can. Water Resour J 18(1):25–37. https://doi.org/10.4296/cwrj1801025

Stigter TY, Ribeiro L, Dill AC (2006) Evaluation of an intrinsic and a specific vulnerability assessment method in comparison with groundwater salinisation and nitrate contamination levels in two agricultural regions in the south of Portugal. Hydrogeol J 14(1–2):79–99

Subbarayan S, Thiyagarajan S, Gangolu S, Devanantham A, Masthan RN (2023a) Assessment of groundwater vulnerable zones using conventional and Fuzzy-AHP DRASTIC for Visakhapatnam district, India. Groundw Sustain Dev 101054. https://doi.org/10.1016/j.gsd.2023.101054

Subbarayan S, Thiyagarajan S, Karuppannan S, Panneerselvam B (2023b) Enhancing groundwater vulnerability assessment: comparative study of three machine learning models and five classification schemes for Cuddalore district. Environ Res 242:117769. https://doi.org/10.1016/j.envres.2023.117769

Sun X, Cao W, Pan D, Li Y, Ren Y, Nan T (2024) Assessment of aquifer specific vulnerability to total nitrate contamination using ensemble learning and geochemical evidence. Sci Total Environ 912:169497. https://doi.org/10.1016/j.scitotenv.2023.169497

Tomer T, Katyal D, Joshi V (2019) Sensitivity analysis of groundwater vulnerability using DRASTIC method: a case study of National Capital Territory, Delhi, India. Groundw Sustain Dev 9:100271. https://doi.org/10.1016/j.gsd.2019.100271

Tripathy KP, Mishra AK (2024) Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions. J Hydrol 628:130458. https://doi.org/10.1016/j.jhydrol.2023.130458

Wang H, Yang Q, Liang J (2022) Interpreting the salinization and hydrogeochemical characteristics of groundwater in Dongshan Island, China. Mar Pollut Bull 178:113634. https://doi.org/10.1016/j.marpolbul.2022.113634

Wani AML, Abunada Z, Yenilmez F, Muhammetoglu A, Muhammetoglu H (2024) Comparative assessment of hydrochemical characterization and groundwater quality for irrigation in an autochthonous karst aquifer with the support of GIS: case study of Altinova, Turkey. Environ Earth Sci 83:237. https://doi.org/10.1007/s12665-024-11548-8

Wen ZX, Wu JL, Wang SS, Cheng JQ, Li Q (2024) Numerical study and machine learning on local flow and heat transfer characteristics of supercritical carbon dioxide mixtures in a sinusoidal wavy channel PCHE. Int J Heat Mass Transf 223:125278. https://doi.org/10.1016/j.ijheatmasstransfer.2024.125278

Wu H, Qian H, Chen J, Huo C (2017) Assessment of agricultural drought vulnerability in the Guanzhong Plain, China. Water Resour Manag 31(5):1557–1574. https://doi.org/10.1007/s11269-017-1594-9

Xiang HT, Lyu HM (2023) Assessment of vulnerability to waterlogging in subway stations using integrated EWM-TOPSIS. Smart Construct Sustain Cities 1:17. https://doi.org/10.1007/s44268-023-00020-4

Yilin S, Ying G, Yuanyuan G, Lanzhen W, Yanjun S (2024) Evaluating water resources sustainability of water-scarcity basin from a scope of WEF-Nexus decomposition: the case of Yellow River Basin. Environ Dev Sustain. https://doi.org/10.1007/s10668-024-04586-6

Yu H, Wu Q, Zeng Y, Zheng L, Xu L, Liu S, Wang D (2022) Integrated variable weight model and improved DRASTIC model for groundwater vulnerability assessment in a shallow porous aquifer. J Hydrol 608:127538. https://doi.org/10.1016/j.jhydrol.2022.127538

Zakaria N, Anornu G, Adomako D, Owusu-Nimo F, Gibrilla A (2021) Evolution of groundwater hydrogeochemistry and assessment of groundwater quality in the Anayari catchment. Groundw Sustain Dev 12:100489. https://doi.org/10.1016/j.gsd.2020.100489

Zarei T, Behyad R (2019) Predicting the water production of a solar seawater greenhouse desalination unit using multi-layer perceptron model. Sol Energy 177:595–603. https://doi.org/10.1016/j.solener.2018.11.059

Zeng T, Jin B, Glade T, Xie Y, Li Y, Zhu Y, Yin K (2024) Assessing the imperative of conditioning factor grading in machine learning-based landslide susceptibility modeling: a critical inquiry. CATENA 236:107732. https://doi.org/10.1016/j.catena.2023.107732

Zhang Q, Qian H, Ren W, Xu P, Li W, Yang Q, Shang J (2023) Salinization of shallow groundwater in the Jiaokou irrigation district and associated secondary environmental challenges. Sci Total Environ 908:168445. https://doi.org/10.1016/j.scitotenv.2023.168445

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This work is developed within the activities of the FCT—Foundation for Science and Technology, I.P., projects UIDB/04683/2020 and UIDP/04683/2020.

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Dhaoui, O., Antunes, I.M., Benhenda, I. et al. Groundwater salinization risk assessment using combined artificial intelligence models. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33469-6

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Moderna has been at the intersection of science, technology, and health for more than 10 years. Moderna’s mission is to deliver the greatest possible impact to people through mRNA medicines—with the COVID-19 vaccine being their most well-known breakthrough. 

The company has partnered with OpenAI since early 2023. Now, ChatGPT Enterprise is evolving how Moderna operates across each function.

Moderna is using its platform for developing mRNA medicines to bring up to 15 new products to market in the next 5 years—from a vaccine against RSV to individualized cancer treatments. In order to achieve its ambitions, Moderna has adopted a people-centric, technology-forward approach, constantly testing new technology and innovation that can increase human capacity and clinical performance.

We believe very profoundly at Moderna that ChatGPT and what OpenAI is doing is going to change the world. We’re looking at every business process—from legal, to research, to manufacturing, to commercial—and thinking about how to redesign them with AI.

Moderna brings AI to everyone

Moderna adopted generative AI the same way Moderna adopts other technology: with the mindset of using the power of digital to maximize its positive impact on patients. To allow AI to flourish, they knew they needed to start with the user and invest in laying a strong foundation for change.

Moderna’s objective was to achieve 100% adoption and proficiency of generative AI by all its people with access to digital solutions in six months. “We believe in collective intelligence when it comes to paradigm changes,” said Miller, “it’s everyone together, everyone with a voice and nobody left behind.” For this, Moderna assigned a team of dedicated experts to drive a bespoke transformation program. Their approach combined individual, collective and structural change management initiatives.   

Individual change management initiatives included in-depth research and listening programs, as well as trainings hosted in person, online and with dedicated AI learning companions. “Using AI to teach AI was key to our success”, Miller points out. Collective change management initiatives included an AI prompt contest to identify the top 100 AI power users who were then structured as a cohort of internal Generative AI Champions. Moderna’s culture of learning led to local office hours in every business line and geography, and scaled through an internal forum on AI, which now has 2,000 active weekly participants. Lastly, structural change management initiatives included engaging Moderna’s CEO and executive committee members to foster AI culture through leadership meetings and town halls as well as incentive programs and sponsored events with internal and external experts.  

 This work led to an early win with the launch of an internal AI chatbot tool, mChat, at the beginning of 2023. Built on OpenAI’s API, mChat was a success, adopted by more than 80% of employees across the company, building a solid foundation for the adoption of ChatGPT Enterprise.  

90% of companies want to do GenAI, but only 10% of them are successful, and the reason they fail is because they haven’t built the mechanisms of actually transforming the workforce to adopt new technology and new capabilities.

Building momentum with ChatGPT Enterprise

With the launch of ChatGPT Enterprise, Moderna had a decision to make: continue developing mChat as an all-purpose AI tool, or give employees access to ChatGPT Enterprise?

“As a science-based company, we research everything,” said Brice Challamel, Head of AI Products and Platforms at Moderna. Challamel’s team did extensive user testing comparing mChat, Copilot, and ChatGPT Enterprise. “We found out that the net promoter score of ChatGPT Enterprise was through the roof. This was by far the company-favorite solution, and the one we decided to double down on,” Challamel said.  

Once employees had a way to create their own GPTs easily, the only limit was their imaginations. “We were never here to fill a bucket, but to light a fire,” Challamel said. “We saw the fire spread, with hundreds of use cases creating positive value across teams. We knew we were on to something revolutionary for the company.”

The company’s results are beyond expectations. Within two months of the ChatGPT Enterprise adoption: 

• Moderna had 750 GPTs across the company
• 40% of weekly active users created GPTs 
• Each user has 120 ChatGPT Enterprise conversations per week on average

Augmenting clinical trial development with GPTs

One of the many solutions Moderna has built and is continuing to develop and validate with ChatGPT Enterprise is a GPT pilot called Dose ID. Dose ID has the potential to review and analyze clinical data and is able to integrate and visualize large datasets. Dose ID is intended for use as a data-analysis assistant to the clinical study team, helping to augment the team’s clinical judgment and decision-making.

 “Dose ID has provided supportive rationale for why we have picked a specific dose over other doses. It has allowed us to create customized data visualizations and it has also helped the study team members converse with the GPT to further analyze the data from multiple different angles,” said Meklit Workneh, Director of Clinical Development at Moderna. 

Dose ID uses ChatGPT Enterprise’s advanced data analysis feature to automate the analysis and verify the optimal vaccine dose selected by the clinical study team, by applying standard dose selection criteria and principles. Dose ID provides a rationale, references its sources, and generates informative charts illustrating the key findings. This allows for a detailed review, led by humans and with AI input, prioritizing safety and optimizing the vaccine profile prior to further development in late-stage clinical trials. 

“The Dose ID GPT has the potential to boost the amount of work we’re able to do as a team. We can comprehensively evaluate these extremely large amounts of data, and do it in a very efficient, safe, and accurate way, while helping to ensure security and privacy,” added Workneh.

Improving compliance and telling the company’s story

Moderna’s legal team boasts 100% adoption of ChatGPT Enterprise. “It lets us focus our time and attention on those matters that are truly driving an impact for patients,” said Shannon Klinger, Moderna’s Chief Legal Officer. 

Now, with the Contract Companion GPT, any function can get a clear, readable summary of a contract. The Policy Bot GPT helps employees get quick answers about internal policies without needing to search through hundreds of documents. 

Moderna’s corporate brand team has also found many ways to take advantage of ChatGPT Enterprise. They have a GPT that helps prepare slides for quarterly earnings calls, and another GPT that helps convert biotech terminology into approachable language for investor communications. 

“Sometimes we’re so in our own world, and AI helps the brand think beyond that,” explained Kate Cronin, Chief Brand Officer of Moderna. “What would my mother want to know about Moderna, versus a regulator, versus a doctor? How do we tell our story in an effective way across different audiences? That’s where I think there’s a huge opportunity.”

A team of a few thousand can perform like a team of 100,000

With an ambitious plan to launch multiple products in the next few years, Moderna sees AI as a key component to their success—and their ability to stay lean as a business while setting new benchmarks in innovation. 

“If we had to do it the old biopharmaceutical ways, we might need a hundred thousand people today,” said Bancel. “We really believe we can maximize our impact on patients with a few thousand people, using technology and AI to scale the company.” 

Moderna has been well positioned to leverage generative AI having spent the last decade building a robust tech stack and data platform. The company fosters a culture of learning and curiosity, attracting employees that excel in adopting new technologies and building AI-first solutions.

By making business processes at Moderna more efficient and accurate, the use of AI ultimately translates to better outcomes for patients. “I’m really thankful for the entire OpenAI team, and the time and engagement they have with our team, so that together we can save more lives,” Bancel said. 

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Transformations That Work

• Michael Mankins
• Patrick Litre

More than a third of large organizations have some type of transformation program underway at any given time, and many launch one major change initiative after another. Though they kick off with a lot of fanfare, most of these efforts fail to deliver. Only 12% produce lasting results, and that figure hasn’t budged in the past two decades, despite everything we’ve learned over the years about how to lead change.

Clearly, businesses need a new model for transformation. In this article the authors present one based on research with dozens of leading companies that have defied the odds, such as Ford, Dell, Amgen, T-Mobile, Adobe, and Virgin Australia. The successful programs, the authors found, employed six critical practices: treating transformation as a continuous process; building it into the company’s operating rhythm; explicitly managing organizational energy; using aspirations, not benchmarks, to set goals; driving change from the middle of the organization out; and tapping significant external capital to fund the effort from the start.

Lessons from companies that are defying the odds

Idea in Brief

The problem.

Although companies frequently engage in transformation initiatives, few are actually transformative. Research indicates that only 12% of major change programs produce lasting results.

Why It Happens

Leaders are increasingly content with incremental improvements. As a result, they experience fewer outright failures but equally fewer real transformations.

The Solution

To deliver, change programs must treat transformation as a continuous process, build it into the company’s operating rhythm, explicitly manage organizational energy, state aspirations rather than set targets, drive change from the middle out, and be funded by serious capital investments.

Nearly every major corporation has embarked on some sort of transformation in recent years. By our estimates, at any given time more than a third of large organizations have a transformation program underway. When asked, roughly 50% of CEOs we’ve interviewed report that their company has undertaken two or more major change efforts within the past five years, with nearly 20% reporting three or more.

• Michael Mankins is a leader in Bain’s Organization and Strategy practices and is a partner based in Austin, Texas. He is a coauthor of Time, Talent, Energy: Overcome Organizational Drag and Unleash Your Team’s Productive Power (Harvard Business Review Press, 2017).
• PL Patrick Litre leads Bain’s Global Transformation and Change practice and is a partner based in Atlanta.

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