research paper on population health

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Population Health and Health Services: Old Challenges and New Realities in the COVID-19 Era

Antonio sarría-santamera.

1 Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 02000, Kazakhstan; [email protected] (A.Y.); [email protected] (T.M.); [email protected] (B.O.); [email protected] (A.G.)

2 Spanish Network in Health Services Research and Chronic Diseases (REDISSEC), 28029 Madrid, Spain; se.iiicsi@zami (I.I.-I.); [email protected] (L.P.-N.)

Alua Yeskendir

Tilektes maulenkul, binur orazumbekova, abduzhappar gaipov, iñaki imaz-iglesia.

3 Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain; se.iiicsi@oneromm (T.M.-C.); se.iiicsi@larroct (T.C.)

Lorena Pinilla-Navas

Teresa moreno-casbas.

4 Center for Biomedical Research in Frailty and Health Aging (CIBERFES), 28029 Madrid, Spain

Teresa Corral

(1) Background: Health services that were already under pressure before the COVID-19 pandemic to maximize its impact on population health, have not only the imperative to remain resilient and sustainable and be prepared for future waves of the virus, but to take advantage of the learnings from the pandemic to re-configure and support the greatest possible improvements. (2) Methods: A review of articles published by the Special Issue on Population Health and Health Services to identify main drivers for improving the contribution of health services on population health is conducted. (3) Health services have to focus not just on providing the best care to health problems but to improve its focus on health promotion and disease prevention. (4) Conclusions: Implementing innovative but complex solutions to address the problems can hardly be achieved without a multilevel and multisectoral deliberative debate. The CHRODIS PLUS policy dialog method can help standardize policy-making procedures and improve network governance, offering a proven method to strengthen the impact of health services on population health, which in the post-COVID era is more necessary than ever.

COVID-19 is the biggest challenge that our societies have faced in living memory. Across the world, the COVID-19 pandemic is having a tremendous impact on our societies and health care systems. Even affluent countries are facing critical challenges, with governments having to rapidly pivot resources and bring in extra protections for groups at risk while seeking care for COVID-19, with mixed success. Health systems that were already under pressure before the pandemic, have not only the imperative to remain resilient and sustainable while continuing to be prepared for future waves of the virus but to take advantage of the learnings from the pandemic to re-configure and support the greatest possible improvements, well beyond this crisis.

We may define population health as the health outcomes of a group of individuals, including the distribution of such outcomes within the group [ 1 ]. Populations may be defined as geographic regions, like nations or communities, but we can define them to be other groups, such as employees, ethnic groups, disabled persons, or prisoners. Population health research aims to understand health, diseases, and their determinants, developing appropriate methodological approaches and analyzing the current issues affecting human health from different perspectives.

Population health is about creating a collective sense of responsibility across many organizations and individuals. It brings together a diverse range of professionals and disciplines, with a common aim of understanding, safeguarding, and improving the health of populations and individuals through education, cooperation, and research. The COVID-19 pandemic has highlighted the importance of a population approach to tackle unexpected health threats. Four pillars have been proposed for population health: the broader determinants of health; health behaviors and lifestyles; the places and communities we live in; and the health services [ 2 ].

This Special Issue of the IJERPH has been dedicated to exploring different angles and perspectives of the relationship between population health and one of those pillars: health services. Papers published in this Special Issue offer not just a rich sample of the wide relationship between the health of the populations and health services, but take-home lessons for improving the impact of health services on population health.

Health services, organizations, people, and actions whose primary intent is to promote, restore, or maintain health [ 3 ], have to play a critical role in improving population health with a consistent orientation towards health promotion and disease prevention [ 4 ]. Core elements of the population health approach include a focus on improving health and wellbeing rather than curing illnesses, understanding needs and solutions through a community perspective, with a life-cycle perspective, and a focus on vulnerable groups, addressing the social determinants of health and inter-sectoral partnerships and promoting healthier lifestyles and behaviors. Van Dale et al. show in their paper a series of key factors to strengthen health services engagement in community partnerships: inter-connecting with existing policies, defining a shared vision, creating an effective mix of different partners, encouraging effective leadership, keeping collaboration partners engaged, using a planned systematic approach, and ensuring sufficient resources [ 5 ].

As well as preventing chronic conditions, health services contribute to population health through the appropriate management of problems once they are diagnosed, aiming towards secondary and tertiary prevention. In our societies, chronic diseases represent a major burden for patients, their families, health care systems, and the society at large. As Wilczyński et al. reflect, preventive interventions have to address from a life-cycle perspective the entire range of determinants associated with those problems [ 6 ].

Evaluation of chronic disease programs and interventions is critical [ 7 ]. Improving effectiveness and patient outcomes when treating complex conditions, as Carrasco-Peña et al. identify, is strongly influenced by adherence to quality standards and guidelines [ 8 ]. In chronic disease management, it is fundamental to develop appropriate methodological approaches to identify patient and health care system structures and processes of care related to outcomes [ 9 ]. Nakamura et al. capture in their paper the critical role of new technologies that are going to maximize effective and efficient care [ 10 ], but also the existing differences in how those systems are used by different population groups.

A growing concern is the continuos grow in the complexity of long-term contions, as the paper by Ioakeim-Skoufa indicates [ 11 ]. Rodríguez-Blázquez et al. reports on the implementation of a multimorbidity care model in several countries, and regardless of the significant diversity in organizational aspects of the different settings where this model was implemented, there was a consistent improvement in the quality of care indicating the need to integrate an orientation towards multimorbidity in our health care systems [ 12 ].

The paper by Sarría-Santamera et al. reflects on the need to better identify patients’ sub-populations for diseases, like diabetes, with such a significant population impact, to target as many as possible treatments, linking patient phenotypic characteristics with treatments whose mechanism of action may be better fitted to their specific metabolic disturbances. The findings of Sanfillippo et al. are also relevant, reflecting a combination of variability in clinical practice in the management of patients with chest pain, and increasing use of coronary procedures in those patients even with normal troponin levels, showing that still further investigation is required to determine the risk profile, outcomes, and cost-effectiveness on managing these patients [ 13 ]. Wei and Zhang and Lv et al. discuss in their respective papers how different factors, at the individual level, like aging, presence of chronic diseases, education, residence, income, and self-care ability, as well as other factors, related to structural and social components, influence the utilization of health systems [ 14 , 15 ].

Health systems worldwide face increasing challenges from the rising costs of care, a growing number of elderly living with complex multimorbid problems, and the recognition of a failure to implement effective health promotion and disease prevention interventions. Health services have to adopt technological innovations [ 16 ] while controlling the overuse of health services [ 17 ], as well as advance the integrating health and non-health services (and resources) to coordinate actions among health care and public health services, social and community organizations [ 18 ].

Digitalization of health services has reached a new level during the pandemic. Digital technologies have become irreplaceable tools in pandemic response, management, and control: real-time monitoring systems, migration maps, data dashboards, real-time data collection devices, and artificial intelligence (AI) have been integrated into different steps of pandemic control, including surveillance, contact tracing, quarantine, testing, and clinical management [ 19 ]. Internet of Things, Big Data, Machine Learning, and AI are changing the delivery of health services. AI could be very effective in providing faster decision-making in diagnosis, treatment, and day-to-day monitoring of the COVID-19 cases and suspects, which can be especially valuable when health care professionals and health care systems experience extremely high workloads [ 20 ].

Digital technologies and data-driven decision-making, predictive health care based on big data analysis, telemedicine, wearable medical devices, and smartphone applications, may transform health services delivery by improving accessibility, making them less prone to human errors and more cost-effective. Remote virtual health care can become a game-changing tool in preventive medicine and management of chronic diseases.

The absence of effective treatment also prompted researchers to quickly search for therapeutics against COVID-19. AI algorithms based on big data analysis showed promising results in the fast identification of potential therapeutics with anti-viral properties and candidate vaccines [ 21 ]. However, the COVID-19 crisis has also revealed the unpreparedness not just of governments and health services, but also of biopharma industries: pharmaceutical companies invest more in medications against oncology, immunology, and cardiovascular diseases compared to infectious disease medications [ 22 ]. The current pandemic showed the danger and price of not having available vaccines and effective anti-infectious drugs and consequently the need to increase investments in infectious disease programs [ 23 ]. Institutions like the European Union have reacted and created initiatives, like the Global Research Collaboration for Infectious Disease Preparedness (GloPID-R), to address this problem [ 24 ].

Another important lesson from the COVID-19 pandemic are the problems of governments and local pharmaceutical companies with the supply of population with essential drugs, medical devices, and personal protective equipment. China and India are among the biggest producers of active pharmaceuticals in the world, and closure of their borders during the pandemic slowed down the production of certain medicines and increased drug prices [ 25 ]. Supply disruptions and medicine shortages during the pandemic may prompt governments to more concentrate on the production of their own essential therapeutics and medical devices to avoid future crisis.

Mental health is another critical area hardly hit by COVID-19. Social isolation, remote education and working, loss of income, limited physical activity, increased access to food and drinks, financial and emotional insecurity, and absence of social support can cause a variety of psychological problems including but not limited to distress, insomnia, anxiety, depression, eating disorders, and exacerbation of existing chronic conditions [ 26 ], worsening of psychiatric symptoms of individuals with preexisting mental disorders, symptoms of anxiety, depression, both among the general public and health care workers [ 27 ]. Those problems were persistent even after the quarantine and led to long-term behavioral changes [ 28 ].

The COVID-19 outbreak has reduced the possibility of traditional face-to-face care; thus, some countries have already introduced online mental health services and remote psychological interventions [ 29 , 30 ]. Shifting from the traditional way of mental health services delivery can cause some issues concerning confidentiality, data security, internet access, and ability to use technologies; even more, some people can struggle to interact during online sessions and for some individuals with lower digital literacy or specific health conditions it could be impossible to receive mental health services remotely [ 23 ]. Although, online mental health care has already shown some positive outcomes [ 27 , 31 ], remote therapy could not work for everyone and demands a more personal approach from the physician’s site. Long-lasting lockdowns and social isolation have revealed new potential of preventive mental health strategies in the forms of family and community support, and self-care [ 23 ]. It will be highly valuable making sure to establishing intersectoral links between different health services.

Implementing innovative but complex solutions to address the problems mentioned above is not simple. Sienkiewicz et al. describe how the CHRODIS PLUS policy dialogs have proved an effective mechanism to provoke deliberative discussion on a wide range of health policy topics in different settings, stimulating thought and concrete actions about priorities and rationales [ 32 ]. The suggested method helped to keep stakeholders engaged, raise their awareness of needs, challenges, and opportunities, setting concrete goals and objectives for a wide variety of health policy issues. The complex challenges that our health services face can hardly be achieved without a multilevel and multisectoral deliberative debate. The CHRODIS PLUS methodology can help standardize policy-making procedures and improve network governance through greater dialogue and civic engagement, offering a proven method to strengthen the impact of health services on population health, which in the post-COVID era is more necessary than ever.

Acknowledgments

We would like to acknowledge the enthusiastic contribution of all participants in the CHRODIS JA and CHRODIS PLUS projects.

Author Contributions

Conceptualization, A.S.-S.; investigation, T.M.; writing—original draft preparation, A.S.-S. and A.Y.; writing—review and editing, T.M., B.O., A.G., I.I.-I., L.P.-N., T.M.-C., and T.C.; funding acquisition, A.S.-S. All authors have read and agreed to the published version of the manuscript.

This research was funded partially by CHRODIS PLUS (European Union Health Programme (2014–2020) Grant 761307); utilization of large scale administrative health data for population research in Kazakhstan: an application in Diabetes Mellitus (NU 080420FD1916) and Clinico-epidemiological assessment of COVID 19 infection in Kazakhstan (NU 280720FD1901).

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Open Access

Peer-reviewed

Research Article

Frameworks for measuring population health: A scoping review

Roles Data curation, Formal analysis, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Health Services Research Centre, SingHealth, Singapore, Singapore, Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore

Roles Data curation, Investigation, Methodology, Project administration, Validation, Writing – review & editing

Affiliation Centre for Population Health Research and Implementation, SingHealth Regional Health System, Singapore, Singapore

Roles Data curation, Investigation, Methodology, Writing – review & editing

Affiliation Health Services Research, Changi General Hospital, Singapore, Singapore

Roles Data curation, Investigation, Methodology, Validation, Writing – review & editing

Roles Data curation, Investigation, Validation, Writing – review & editing

Roles Formal analysis, Methodology, Validation, Visualization, Writing – review & editing

Affiliation School of Biological Sciences, Nanyang Technological University, Singapore, Singapore

Roles Investigation, Writing – review & editing

Affiliation Care and Health Integration, Changi General Hospital, Singapore, Singapore

Affiliation Preventive Medicine Residency, National University Health System, Singapore, Singapore

Affiliation School of Computing and Information Systems, Singapore Management University, Singapore, Singapore

Roles Conceptualization, Project administration, Resources, Writing – review & editing

Roles Writing – review & editing

Affiliations Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore, Centre for Population Health Research and Implementation, SingHealth Regional Health System, Singapore, Singapore, Post-Acute and Continuing Care, Outram Community Hospital, Singapore, Singapore, Department of Family Medicine and Continuing Care, Singapore General Hospital, Singapore, Singapore, SingHealth Duke-NUS Family Medicine Academic Clinical Program, Singapore, Singapore

Roles Conceptualization, Resources, Supervision, Writing – review & editing

Affiliations SingHealth Duke-NUS Family Medicine Academic Clinical Program, Singapore, Singapore, SingHealth Office of Regional Health, Changi General Hospital, Singapore, Singapore

• Sze Ling Chan, 
• Clement Zhong Hao Ho, 
• Nang Ei Ei Khaing, 
• Ezra Ho, 
• Candelyn Pong, 
• Jia Sheng Guan, 
• Calida Chua, 
• Zongbin Li, 
• Trudi Lim, 

• Published: February 13, 2024
• https://doi.org/10.1371/journal.pone.0278434
• Reader Comments

Introduction

Many regions in the world are using the population health approach and require a means to measure the health of their population of interest. Population health frameworks provide a theoretical grounding for conceptualization of population health and therefore a logical basis for selection of indicators. The aim of this scoping review was to provide an overview and summary of the characteristics of existing population health frameworks that have been used to conceptualize the measurement of population health.

We used the Population, Concept and Context (PCC) framework to define eligibility criteria of frameworks. We were interested in frameworks applicable for general populations, that contained components of measurement of health with or without its antecedents and applied at the population level or used a population health approach. Eligible reports of eligible frameworks should include at least domains and subdomains, purpose, or indicators. We searched 5 databases (Pubmed, EMBASE, Web of Science, NYAM Grey Literature Report, and OpenGrey), governmental and organizational sites on Google and websites of selected organizations using keywords from the PCC framework. Characteristics of the frameworks were summarized descriptively and narratively.

Fifty-seven frameworks were included. The majority originated from the US (46%), Europe (23%) and Canada (19%). Apart from 1 framework developed for rural populations and 2 for indigenous populations, the rest were for general urban populations. The numbers of domains, subdomains and indicators were highly variable. Health status and social determinants of health were the most common domains across all frameworks. Different frameworks had different priorities and therefore focus on different domains.

Key domains common across frameworks other than health status were social determinants of health, health behaviours and healthcare system performance. The results in this review serve as a useful resource for governments and healthcare organizations for informing their population health measurement efforts.

Citation: Chan SL, Ho CZH, Khaing NEE, Ho E, Pong C, Guan JS, et al. (2024) Frameworks for measuring population health: A scoping review. PLoS ONE 19(2): e0278434. https://doi.org/10.1371/journal.pone.0278434

Editor: Angela Mendes Freitas, University of Coimbra: Universidade de Coimbra, PORTUGAL

Received: November 15, 2022; Accepted: October 3, 2023; Published: February 13, 2024

Copyright: © 2024 Chan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and S2 File .

Funding: This research is supported by the National Medical Research Council (NMRC) through the SingHealth PULSES II Centre Grant (CG21APR1013).

Competing interests: The authors have declared that no competing interests exist.

Population health has become an increasingly prominent concept in public health discourse, governance, and research in recent years. In their seminal paper, Kindig and Stoddart defines population health as an approach to understanding health that transcends the individual, focusing on interrelated factors and conditions shaping the health of a population. These includes the environment, social and cultural forces, lifestyle choices and government policies [ 1 ]. In other words, health cannot be fully understood without a contextualisation of socioeconomic and other factors, such as lifestyle, that are shaped by environments and communities [ 2 ]. This change in focus and understanding of health originated during the 1970s-80s in response to the growing body of evidence on social determinants of health, and increasing advocacy for social justice and equity [ 3 ]. In contrast to the traditional biomedical model that focused on individual risk factors of diseases, such as obesity, alcohol consumption or family history, a population health approach adopts an upstream preventive approach by addressing root causes, rather than symptoms, to achieve health outcomes.

Population health indicators provide a means for government agencies and Non-Governmental Organisations (NGO) to monitor public health, evaluate interventions, and guide population health policies. Summary measures such as life-expectancy are commonly used to measure the health of a population and for benchmarking against others but are limited on their own, as they do not provide information on other aspects of health [ 4 ]. With health and its antecedents being complex and multifaceted constructs, so is the selection of relevant population health indicators. In a scoping review of population health indices, only 7 out of 27 indices had a theoretical or conceptual foundation guiding the aggregation of indicators in a meaningful way [ 5 ].

A framework should therefore precede indicator selection [ 4 ]. Frameworks provide a structure by which to organise the dynamic and interrelated factors between individuals and their environment, and through which to develop hypotheses about how such relationships affect health outcomes over time [ 6 ]. For instance, the widely accepted Canadian Institutes of Health Research population health framework provides an integrated view of health through upstream forces (a whole spectrum of cultural, economic, social and other forces), proximal causes of heath (such as physiological risk factors), lifespan processes, disparities across sub-populations, health services, and health outcomes, as well as the indicators and indices used to measure them [ 7 ]. Others may differ depending on their purpose and definition of health and population health.

The usage of a population health framework is necessary as it provides a theoretical grounding and context for selection of indicators and clarifies the role of each indicator [ 5 ]. Indeed, this is a step many government agencies and NGOs have taken in their population health efforts. There have been reviews on population health indicators [ 5 , 7 , 8 ]. However, to our knowledge there is no work that organises and clarifies this growing body of literature.

In this paper, we conducted a scoping review with the aim of providing an overview and summary of the characteristics of existing population health frameworks that have been used to conceptualize the measurement of population health. Specific aims were to understand what domains were included in the frameworks, how or why they were chosen, and what some representative indicators under each domain were.

This scoping review follows the guidelines described by the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist, a minimum set of items for reporting of scoping reviews to promote transparent reporting of scoping reviews [ 9 ] ( S1 File ).

Eligibility criteria

The eligibility criteria of population health frameworks were guided by the elements of the Population, Concept, and Context (PCC) framework. In the population element, we were interested in frameworks that were applied to general populations, which included subsets by demographic variables (e.g. age or ethnicity). However, we excluded populations which were defined by illnesses or diseases (e.g. stroke or mental health patients), or institutional settings (e.g. workplace, schools).

For the Concept element, frameworks should contain components of measurement of health, with or without its antecedents. Frameworks by definition convey structure, at least in the form of categorization [ 6 ]. Therefore, eligible frameworks should fulfil this definition. Simple lists of indicators without categories are excluded. Frameworks should also be novel, so mere representations of known literature or frameworks with insufficient explanation, and logic models for specific programs were excluded. For context, frameworks should be applied at the macrolevel, or use a population health approach.

Eligible reports of eligible frameworks would need to include at least one of the following dimensions– 1) Domains and subdomains; 2) purpose of the framework; or 3) population health indicators used. Where there were more than 1 report for the same framework, we selected the one with the most relevant and comprehensive information. If another report supplemented information not found in this primary report, we would include both. We included primary articles of any study design, reviews and selected grey literature. Conference abstracts, theses and dissertations, letters to editors, commentaries, non-English articles, and articles published before 1990 were excluded.

Information sources

We searched MEDLINE (PubMed), EMBASE, Web of Science, NYAM Grey Literature Report and OpenGrey databases. In addition, we searched governmental and organizational sites on Google (site:.gov OR site:.org OR site:.net OR site:.eu) and websites of the following government agencies and NGOs known to have population health initiatives and/or frameworks:

• UK National Health Service (NHS)
• Agency for Healthcare Research and Quality (AHRQ)
• Centres for Disease Control (CDC)
• US Department of Health and Human Services
• Public Health Agency of Canada
• Australian Government Department of Health
• World Health Organization (WHO)
• Organisation for Economic Co-operation and Development (OECD)
• Public Health England
• European Union (EU) CDC
• National Quality Forum (NQF)
• Health Information Technology, Evaluation, and Quality Center (HITEQ)
• The King’s Fund
• Africa Population and Health Research Centre
• Canterbury District Health Board

Search strategy

We used the keywords ‘framework’ and ‘population health’ from the concept and context elements as search terms, respectively. Depending on the database, we used these terms as keywords or also included controlled vocabulary that corresponded to them. The keywords or controlled vocabulary were combined using the BOOLEAN operator ‘OR’ and ‘AND’ within and across the PCC elements, respectively. The search terms are given in S2 File . Where possible, filters were applied to select only human studies and English articles. The search of the databases was performed from 1 Jan 1990 to 5 May 2023. For some databases (Pubmed, EMBASE, Web of Science) we further applied a ‘title/abstract’ filter to improve the specificity of the search results. If we came across reports that mention an eligible framework but did not contain the relevant details to be included, we then searched for reports on that particular framework. We also searched reference lists of included reports.

Selection of sources of evidence

Three reviewers (SLC, CZHH, NEEK) developed and piloted the search strategy. Two stages of screenings were performed to select the sources of evidence. At the first stage, the titles and abstracts of each source was screened and selected for full text review by two reviewers independently. In the second stage, the full texts of articles selected in the first stage were also reviewed by 2 reviewers independently. In both stages, a third reviewer would make the final decision in the event of a conflict.

Data charting process

A data charting form to extract data of interest was developed by one reviewer (SLC) and piloted by another (CZHH). Data from each report was extracted by one reviewer and reviewed by a second reviewer. Any discrepancies were resolved by consensus between the data extractor and reviewer.

The data items included citation details, details on the framework (e.g. name, country of origin, organization that developed it, type of population it is applicable to, approach to development, dimensions in framework apart from domains, if framework assessed indicators by certain cross-cutting variables such as life stages, socioeconomic factors, and/or health-related sectors), and the domains and indicators used in the framework, including definitions or descriptions where available. For domains, we recorded up to 2 further levels of sub-domains (total 3 levels).

Synthesis of results

To facilitate summary and presentation of results, some variables were reduced to a smaller number of categories manually by a single reviewer (SLC). These variables were the type of organization developing the frameworks, types of population the framework was applicable to, and dimensions of the framework. Types of organizations were broadly categorized into governmental, academic, non-government organizations, non-profit organizations, intergovernmental organizations, and private foundations. Populations were grouped in to general, rural and indigenous populations. Finally, dimensions cut across domains and indicators and we focused mainly on a lifespan, health equity and sector approach. For the lifespan approach, this generally involve diving into indicators relevant for different life stages and/or breaking down indicators by age groups. For the equity approach this typically involves examining indicators by certain socioeconomic factors, such as education level, income, and ethnicity. For the sector approach, this involves looking at indicators specific for different health-related sectors such as clinical care, public health, and community and social services. We categorized frameworks under ‘dimensions’ into lifespan, equity and/or other specific dimensions mentioned.

The characteristics of the frameworks were then summarized descriptively using counts and proportions, and median and ranges, as appropriate. Domains were aggregated by concept using hierarchical clustering and manual refinement for purposes of visualization. The final clustering was agreed on by 3 reviewers (SLC, CP, JSG). The domain concepts, and number of domains, subdomains and indicators were visualized using a word cloud and heatmap, respectively. Other aspects of the frameworks were summarized narratively.

Search results

A total of 57 population health frameworks were included in this review ( Fig 1 ). The characteristics of the frameworks and their details are shown in Tables 1 and 2 , respectively. The full list of the domains, subdomains and indicators are provided in S3 File .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

The PRISMA diagram shows the numbers of reports retrieved from various sources and flow through the stages of the scoping review. A total of 57 reports were included in this review. The diagram was generated using an open source R shiny app [ 10 ].

https://doi.org/10.1371/journal.pone.0278434.g001

https://doi.org/10.1371/journal.pone.0278434.t001

https://doi.org/10.1371/journal.pone.0278434.t002

Characteristics of population health frameworks

Majority of the frameworks originated from the US (45.6%), Europe (22.8%) and Canada (19.3%). None were from Asia. Most were published between 2001 and 2020 (64.9%). Governmental (including intergovernmental) and academic organizations accounted for majority of framework development (84.2%). Only three frameworks were developed for specific populations (2 for indigenous and 1 for rural), while the rest were for the general or urban population. Two-thirds of the frameworks mentioned some dimension, and these were slightly more frameworks using the lifespan approach compared to the equity approach (29.8% vs. 21.1%).

Domains and subdomains

Majority of the frameworks have between 1 to 5 domains (70.2%) but have more level 2 sub-domains (26.3% have 6–10, 29.8% have 11–20 and 19.3% have >20). The median number of domains and level 2 subdomains are 4 (range 2–16) and 10 (range 0–65), respectively ( S1 Fig ). Half of the frameworks do not have level 3 subdomains. Of those that do, most have >10 (72.4%). The median number of indicators is 18 (range 0–255). Twenty-six frameworks did not have indicators (45.6%). Of those that do, majority have >20 indicators (83.9%).

The most common concepts were health, (social) determinants of health, healthcare system and health behaviours ( S2 Fig ). The myriad of domains has gradually accumulated over the years. In frameworks published before 2000, health was the key domain, social determinants of health emerged in the next 2 decades (2001–2020) followed by healthcare system, health behaviours, functional limitations and activities of daily living in the recent frameworks ( S3 Fig ).

For health, most frameworks used summary indicators of health such as mortality and life-expectancy, and indicators of a few selected health conditions. However, four frameworks had longer lists of indicators for specific communicable and non-communicable diseases [ 12 , 26 , 27 , 43 , 50 ]. Of note, psychological or mental health risk factors and/or outcomes feature in 31 (54%) of the frameworks, highlighting its emerging importance [ 12 , 17 – 19 , 22 , 25 – 30 , 32 – 35 , 38 , 39 , 41 – 46 , 48 – 50 , 54 , 56 , 58 , 59 , 62 ].

Social determinants of health, which encompasses the full set of social conditions in which people live and work [ 66 ], were present under some label or other in all except 7 frameworks [ 16 , 34 , 42 , 50 , 52 , 54 , 60 ]. Some of the frameworks elaborate on these factors, with sub-domains and indicators on the physical environment, social environment, and even politics, national and global trends [ 12 , 21 – 23 , 26 , 29 , 35 , 43 , 53 , 55 – 59 , 63 , 64 , 67 ]. For example, the conceptual framework for urban health measures sub-domains such as immigration, globalization and the changing role of government [ 21 ]. The framework for community contextual characteristics, one of the two frameworks with the largest number of indicators, also measures the economic, employment, education, political, environmental, housing, governmental, transport aspects in the region where the population of interest is located [ 29 ]. Interestingly, crime and violence features in 16 frameworks, as this affects the physical safety of people in a community [ 12 , 15 , 26 , 29 , 30 , 33 , 41 , 43 – 45 , 53 , 56 , 59 , 62 , 63 , 67 ]. Many frameworks also measure lifestyle and health-related behaviours. Apart from the common ones like diet, physical activity, smoking and alcohol use, some frameworks include sexual behaviour, use of illicit drugs, seatbelt behaviour, immunization or health screening, breastfeeding and induced abortion [ 12 , 15 , 27 – 30 , 32 , 33 , 39 , 45 , 55 , 58 , 59 , 62 ]. One even included measures of parenting practices [ 43 ].

Almost a third (31.6%) of the frameworks have domains that pertain to the healthcare system or healthcare performance. One example is the OECD framework, which assesses health system performance within the context of other contextual determinants of health [ 46 ]. Within the construct of healthcare performance, common subdomains are accessibility, capacity, quality, patient-centeredness, cost and effectiveness [ 11 , 16 , 19 , 24 , 31 , 32 , 34 , 39 , 43 , 46 , 48 , 51 , 53 , 54 ].

A few of the frameworks had specific focuses and therefore unique domains and indicators that are relevant largely for their setting. For example, the reporting framework for indigenous adolescents in Australia contained domains that were largely relevant for that community, such as ‘family, kinship and community health’, which explored family roles and responsibilities, contact with extended family, removal from family, participation in community events and sense of belonging to the community [ 12 ]. Another example is the Ghana’s Holistic Assessment Tool, which contains indicators for health-related United Nations sustainable development goals (SDGs) such as proportion of deliveries attended by a trained health worker, proportion of children under 5 years sleeping under insecticide treated net, and tuberculosis treatment success rate, and certain endemic communicable diseases such as non-acute flaccid paralysis polio rate [ 42 ].

Approach to framework development

Evans and Stoddart developed a population health framework in 1990 [ 20 ] based on a much earlier 1974 Whitepaper titled “A new perspective on the health of Canadians”, which recognized the limitations of the healthcare system on improving health status and presented a preliminary framework of the ‘health field’ [ 68 ]. Subsequent frameworks were mostly developed from one or a combination of four approaches: 1) adaptation from an existing framework [ 11 , 12 , 33 , 45 , 46 , 48 – 51 , 56 , 58 – 60 , 63 , 65 ], 2) environmental scan of existing frameworks and literature review to summarize current knowledge of health determinants [ 7 , 14 , 16 – 20 , 24 , 25 , 29 , 32 , 36 , 37 , 44 , 48 , 52 , 57 , 61 , 63 ], 3) consulting and getting inputs from experts and stakeholders [ 12 , 17 , 19 , 24 , 26 – 29 , 35 , 39 , 41 , 48 , 52 – 55 , 62 , 63 ] and 4) basing on past work (e.g. primary data collection, drawing on secondary data, past population health efforts, etc), priorities and goals of the organization developing it [ 7 , 11 , 21 , 38 , 61 , 64 , 67 ].

Population health has been a popular concept in healthcare for the past 3 decades but interestingly does not have a unanimous definition [ 1 , 2 , 69 ]. The most commonly used definition, which originated from Kindig and Stoddart, defines population health as ‘the health outcomes of a group of individuals, including the distribution of such outcomes within the group” [ 1 ]. Nevertheless, people working on ‘population health’ would have different focuses, goals and populations of interest [ 69 ]. This may explain the large number of population health frameworks we found in this review.

Population health has its roots from recognition of health disparities by socioeconomic factors from as early as the 18 th century to early epidemiological studies that informed public health measures, particularly in Britain and France, and finally to a renewed interest in the last 2 decades due to a range of health problems facing the world [ 70 ]. Development of the population health approach in Canada, driven by the government and healthcare leaders, began in the 1970s [ 71 ]. Improving population health was motivated by the articulation of the Triple Aims as a goal for the US healthcare system in the late 2000s [ 72 ]. It is therefore unsurprising that most of the frameworks originate from US, Europe and Canada. Even with purposive searching of organizations in the Southern hemisphere such as Australia and New Zealand, the results were still dominated by the Northern hemisphere, reflecting the state of development of population health in the world. Similarly, the lack of frameworks from Asia might be because much of the work done in improving the health of populations is ‘public health’ rather than ‘population health’.

Health status and social determinants of health were the most common domains across the frameworks. As seen from the word cloud, there were also many other domains that were closely related to and/or could be considered subdomains of one of these domains. This is because different frameworks have different level of detail, and the hierarchy of domains and subdomains are different in level of detail across frameworks. In other words, a subdomain in one framework could be a domain in another, or an indicator in one framework could be a subdomain in another. It is therefore also difficult to summarize domains and subdomains in a simple way across the frameworks.

The domains and subdomains chosen in different frameworks largely reflects the purpose, information needs of varying stakeholders, and the focus of the organization(s) developing them. It is unsurprising to see that some key domains appear in many frameworks, and domains are branched out to varying degrees in different frameworks. For example, social determinants of health features in all frameworks except 7 frameworks [ 16 , 34 , 42 , 50 , 52 , 54 , 60 ]. Some frameworks have a heavy focus on health status, such as the Healthy Montogomery Core Measures Set, Triple Aim, Euro-REVES 2 and Ohio health priorities, with the Euro-REVES 2 framework even measuring activities of daily living and degree of functional limitations [ 26 , 27 , 50 , 60 ]. Other frameworks break down the social determinants into considerable detail, such as the framework for community contextual characteristics, life course health development framework, Healthy Cities Indicators, and others [ 12 , 22 , 23 , 26 , 29 , 38 , 49 , 53 , 55 , 56 , 59 , 63 , 64 , 67 ]. Several have a heavier focus on healthcare performance, such as the EU Joint Assessment Framework, European Community Health Indicators (ECHI), OECD, the Primary Healthcare Performance Initiative (PHCPI), National scorecard for the US health system and the Ireland HSPA framework [ 19 , 34 , 39 , 46 , 48 , 54 ]. Others are generally more balanced between the domains.

It is also noteworthy that almost half of the frameworks did not have any indicators and these tended to be older frameworks. About 61% of frameworks developed in 2010 and before did not have indicators while the converse is true for those developed after 2010. There was likely stronger focus on understanding the range of factors affecting population health and identifying priorities for improving population health in the earlier period. As organizations started to implement population health management strategies, measurement of population health started to feature more and more recent frameworks tended to include specific indicators. The inclusion of specific indicators also implies the ability to measure them, and therefore the availability of health information systems for data collection. These have generally become more well developed in the recent decade or so, also explaining why more recent frameworks have indicators. Nevertheless, frameworks without indicators can still offer a theoretical basis for selecting indicators that are relevant and feasible for a given setting.

The results of this scoping review can serve as an evidence base for governments and/or health systems developing their own population health frameworks and selecting indicators for their population health initiatives. They can select and adapt from the frameworks available, and assess the relevance of the range of domains, subdomains and indicators in their context. Populations are largely unique as they are shaped by their local and wider contextual factors. As such, no one framework used in one population or healthcare system is likely directly applicable to another population or healthcare system without adaptation. Population health practitioners can derive any level of detail that matches their interests and requirements from this review, from a broad sense of the literature down to specific indicators. The range of subdomains and indicators could also be sources of new hypotheses in a given region or jurisdiction for the purposes of population health research.

Settings which are further ahead in the population health journey with existing indicators can also use these results to assess what domains and subdomains have been covered, and where the gaps are. For example, population health is an increasingly important national priority in Singapore and the Ministry of Health is planning several major initiatives to improve the health of the general population [ 73 , 74 ]. To achieve this, the Ministry is working closely with the three major public healthcare clusters in Singapore to develop a set of population health indicators and the evidence base here can help inform the choices. With an initial set of indicators, practitioners can also interrogate their data systems and medical records to determine if they are available or if they need to build prospective data collection tools. This can also be an iterative process for selecting indicators using the results here as a resource. One constraint of the data in its current form though is the difficulty in navigating the long list of domains, subdomains and indicators. In future work, we aim to design a dashboard that allows for interactive exploration of the scoping review data.

There are limitations to this scoping review. Firstly, some frameworks might have been missed due to our language restriction, especially those in Asia. However, many official documents from this region are available in English, so this might not have impacted the search results significantly. Secondly, there are many terms and concepts in the literature that have overlaps with population health, such as public health, urban health, global health, population health management, health equity, health system performance and social determinants of health. Based on our inclusion criteria, concepts like urban health, rural health, community health and global health would be included as they pertain to general populations albeit in different types of settings. Related concepts such as health equity, social determinants of health and health system performance were not the focus of the search and could be part of the frameworks included. However, if a framework was focused on one of these concepts alone without the measurement of health status, then it would be excluded. Some frameworks also focused more on population health management and if it looked more like a logic model for specific interventions then these would also be excluded [ 75 , 76 ]. Overall, this review represents a useful collection of frameworks used for measuring the health of a population and its key antecedents [ 60 ].

We found 57 frameworks for the measurement of population health with variable numbers of domains, subdomains and indicators, and depth of detail. The key domains apart from health status were social determinants of health, health behaviours and healthcare system performance. These results serve as a useful resource for governments and healthcare organizations for informing their population health measurement efforts. Specifically, when developing their own population health framework and/or selection of population health indicators, they can identify common domains and subdomains that other organizations include, as well as consider others more systematically for relevance in their context.

Supporting information

S1 file. prisma-scr checklist..

https://doi.org/10.1371/journal.pone.0278434.s001

S2 File. Search strategy.

https://doi.org/10.1371/journal.pone.0278434.s002

S3 File. Domains, subdomains and indicators.

This file contains the full list of domains, subdomains and indicators from the 57 included population health frameworks.

https://doi.org/10.1371/journal.pone.0278434.s003

S1 Fig. Heatmap of number of domains, subdomains and indicators.

L2: level 2, L3: level 3, This is a visualization of the numbers of domains, subdomains and indicators in each framework in both figures and shading. Blank cells represent absence of the corresponding subdomain and/or indicators.

https://doi.org/10.1371/journal.pone.0278434.s004

S2 Fig. Wordcloud for framework domains.

Level 1 domains in all frameworks were clustered by concept using a combination of hierarchical clustering and manual edit. The sizes of the concepts are proportional to the number of domains in each concept.

https://doi.org/10.1371/journal.pone.0278434.s005

S3 Fig. Wordcloud for framework domains by year of publication.

Level 1 domains in all frameworks were clustered by concept using a combination of hierarchical clustering and manual edit. The sizes of the concepts are proportional to the number of domains in each concept. The concepts are presented by decade when the frameworks were published. A: Before 2000, B: 2001 to 2010, C: 2011 to 2020, D: After 2020.

https://doi.org/10.1371/journal.pone.0278434.s006

Acknowledgments

We would like to thank Ms Sabrina Liau for her assistance with article screening.

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Accelerating population health improvement

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• Related content
• Peer review
• Pedro Delgado , vice president 1 2 ,
• Kristine Binzer , general practitioner 3 ,
• Amar Shah , chief quality officer 4 5 6 ,
• Jesper Ekberg , public health director 7 ,
• Jafet Arrieta , senior director 1 ,
• Dominique Allwood , assistant director improvement 8 9
• 1 Institute for Healthcare Improvement, Boston, MA, USA
• 2 Harvard TH Chan School of Public Health, Boston, MA, USA
• 3 Bridge for Better Health, Region Sjaelland, Denmark
• 4 East London NHS Foundation Trust, London, UK
• 5 Royal College of Psychiatrists, London, UK
• 6 University of Leicester, Leicester, UK
• 7 Region Jönköping County, Jönköping, Sweden
• 8 The Health Foundation, London, UK
• 9 Imperial College London, London, UK
• Correspondence to: P Delgado pdelgado{at}ihi.org

Pedro Delgado and colleagues describe how applying improvement methods to working with populations could help close equity gaps

As the covid-19 pandemic shines a bright light on longstanding health equity gaps, 1 concerted action around social determinants of health to close these gaps continues to increase. Improvement methods (including shared tools and language) traditionally used in healthcare are agnostic in nature and can also be used in sectors such as education, local government, law enforcement, and others to improve social determinants of health. Such adoption could catalyse population health improvement efforts with and for the populations they serve.

Three related concepts are core to this article. “Population health” is defined as the health outcomes of a group of individuals in a specified population, including the distribution of such outcomes within the group. “Population health management” efforts seek to optimise the health of populations over individual life spans. 2 3 We did not find a common definition for “population health improvement,” but we think this is an area that will start to generate more learning and evidence over the coming years. Ahead of such developments, we suggest an early definition: concerted, intentional, and systematic efforts by those working together towards measurable improvement of health and wellbeing outcomes, co-produced with and for the population in their locality. 1

Actors jointly working to pursue better health for well defined populations (including citizens, healthcare providers at all levels, councils or municipalities, businesses, schools, fire services, voluntary sectors, housing associations, social services, and police) will benefit from having a shared method that includes a common language and tools and can be applied across four areas: defining the system, describing shared aims and the work required to achieve them, measuring systematically over time, and acknowledging that change happens. These four components form the foundation of the improvement method, and their systematic application 4 can bring health economy actors together in pursuit of better population health.

A common method to tackle shared challenges

People are living longer; technology is evolving rapidly; and the costs associated with lack of proactive, concerted actions to prevent and manage non-communicable diseases have the potential to bankrupt healthcare systems and affect other sectors of the economy. With these drivers as a backdrop, several trends have taken shape in healthcare, including a shift towards tackling upstream factors, prevention, and self-management of conditions. 5 Care models are shifting from specialized care to primary care, which has demanded the development of support systems, including use of technology to enable virtual care. 5 6 Similarly, healthcare providers’ priorities are shifting from volume to value, 7 tackling quality and all of its dimensions 5 and ensuring access to high quality care for all, with increased attention to inequities in outcomes for distinct populations.

Healthcare provider organisations are focused on partnering with citizens and communities to improve health and with patients to improve care. 7 They are increasingly looking beyond the walls of their institutions to understand their effects more widely, 8 with a greater awareness of the bi-directional connection between environmental changes and health, combining the concepts of planetary health and sustainability. 9 There is a clear push towards health economy integration and place based health, 10 11 and organisations are making efforts to enhance the contribution of health systems as anchor institutions. 8

Building on these trends, governments have been promoting strategies to pursue better care and better health at sustainable costs. 12 13 14 Health economies are therefore formally and informally fostering hands-on collaboration among traditional partners, such as healthcare institutions, and non-traditional partners, such as sectors related to the social determinants of health, to serve their local populations. In England, for example, the NHS is leading efforts to formalise collaboration by moving Integrated Care Partnerships into legislation by April 2022. 15

As these partnerships form and evolve over time, a common improvement method (tools and language) provides a shared approach that stakeholders in and across sectors can use to translate strategy and evidence based changes, using local expertise, into measurable results.

Strategies for population health improvement

Box 1 shows priority areas and strategies for health systems to consider when undertaking population health improvement.

Strategies for successful population health improvement

Define the population and design accordingly

Develop bold ambitions and bold aims

Act with and for the population

Build a portfolio of projects focused on each population health aim

Segment for equity

Measure what matters

Embrace an asset based approach

Embrace humility to generate trust

Test your way into better partnership work, in pursuit of results

Make health improvement everyone’s business and make improvement skills available to all

Define and co-design

Identify a group of people with similar needs or characteristics for whom a portfolio of interventions might improve outcomes. Start with the question, “Who is not thriving?” to identify broad populations, such as adult mental health or children and young people’s health, and then select more specific population segments to focus improvement efforts by identifying an aim and changes to test, such as reducing suicide rates in men aged 16-24 in a particular geographical area. The King’s Fund defines four interdependent pillars as the “system” of population health: individual health behaviour and choices; the places and communities that individuals live in and with; an integrated health and care system; and the wider determinants of health. 16 These can be used to help define the population. A three part data review is also useful for population stratification to better understand the needs and assets of the population, which then inform the design choices to improve outcomes. 17

Bold ambitions for population health improvement need to be supported by aims that specify how much, by when, for whom, and where. Start by developing a bold purpose statement such as “being the best place to grow up.” 18 Next, create more specific and measurable aim statements that relate to the population rather than the service; for example, under the “best place to grow up” the vision could be “90% of all children in each community planning partnership area will have reached all of the expected developmental milestones and learning outcomes by the end of primary school) by the end of 2021.” Good aim setting will help identify opportunities to segment populations, establish relationships, and ensure that impact can be measured.

Co-design and co-production with individuals from the target population should be embraced at every step of the process. In population health improvement efforts, activating the agency of the population as well as that of those coordinating their care is fundamental. The process of working together towards the aim is as important as the aim itself and requires engaging key actors in the health economy (as described above), including citizens and service users, to design and adopt the changes needed to improve. People’s health is heavily influenced by factors outside of their care such as their social and economic environment, physical environment, and individual characteristics and behaviours. 19 So individuals are protagonists in efforts to improve population health, not passive “receivers” of health. Activating people’s agency is at the heart of the Institute for Healthcare Improvement’s Psychology of Change Framework . 20 The framework describes methods and approaches around five inter-related domains of practice that organisations can use to advance and sustain improvement.

The complexity of the population health system is such that health economies will almost inevitably need a portfolio of projects occurring in parallel to make progress. Our experience emphasises the value of identifying key drivers for each aim and tackling them through targeted improvement projects, ensuring the efforts are also aligned with strategic priorities of these systems. “Think big and start small” is a mantra we often use.

Partnering for equity

There is no quality without equity. Stratifying data allows for better understanding of variation and gaps in outcomes, which in turn allows for tailoring strategies that respond to the specific needs of different populations to eliminate equity gaps. 21 If a differentiated approach is not adopted, the gap between people who have access to the best possible health and those who do not will widen, leading to avoidable suffering, intergenerational cycles of poor health, and high costs for health economies. 22 The World Health Organization defines equity as “the absence of avoidable, unfair, or remediable differences among groups of people, whether those groups are defined socially, economically, demographically or geographically, or by other means of stratification . . . it implies that ideally everyone should have a fair opportunity to attain their full health potential and that no one should be disadvantaged from achieving this potential.” 23

Between 2017 and 2019, the Institute for Healthcare Improvement ran an initiative with eight health systems in the United States to improve equity in access to and quality of care, as well as health outcomes, through the practical application of improvement methods, collaboration, and shared learning. 24 One key element was ensuring that health systems had the capacity to collect and stratify data to better understand which populations were benefitting and which ones were being left behind, including data about race, language, and ethnicity. This enabled health systems to create better solutions that took into consideration the specific needs and conditions of those left behind. 24

Health systems should proactively partner with individuals, communities, and institutions within and beyond healthcare. There is a growing realisation that the historical approach to health and healthcare, which is largely dependent on professionally trained and qualified health and healthcare “experts,” needs to be reconsidered. The most successful population health improvement efforts involve actors that are open and willing to value each other’s assets (such as will, abilities, and resources) and understand that the whole is greater than the sum of its parts.

Integration will move forward at the speed of trust. In the earliest stages, health economies that are learning to work together will require humility to accept that the protagonist is the citizen, not any single actor, and will require designing systems that are organized around the needs of those citizens. This is hard to do and must be intentionally designed early in the process to create an environment of psychological safety, to develop a sense of community guided by a unified purpose,, and to foster trust and a set of behaviours that build trust, instil purpose, and generate energy. 25 Municipal leaders from the Bridge for Better Health effort in Denmark have a performance indicator related to the quality of their relationships with partners as a way of focusing attention on humility and good collaboration. 13

Measure and learn

Health systems should identify what matters most to the population of interest and develop measures to track progress. The large number of measures health systems are often required to track leads to diffusion of impact and exhaustion of staff, who find themselves collecting data for many indicators with little connection to the purpose of such efforts. 26 More importantly, tracking too many measures might not directly benefit the people that health systems serve. Population health improvement efforts often struggle because health economy actors do not feel ownership of broad aims, and it is difficult to define who is responsible for achieving outcomes when data are collected. We foresee a future in which health systems will start to include other aspects in their measurement efforts: relationships between partners in the health economy and the environmental effects of the carbon footprint resulting from more home based health and care models, for example.

Avoiding planning paralysis is important. Too often, integration efforts in health economies dedicate a disproportionate amount of time and energy to establishing governance arrangements and idealised strategies and plans, without paying enough attention to how ambitions will be tested, implemented, and scaled up. Integration is frequently seen as an end in itself, when it’s clearly a means to an end—measurable outcomes for better population health. We encourage testing and learning, from the integration process to implementing specific changes to improve population health. This iterative testing and learning approach enables refinement of the strategy and “the work” of improving population health, and it is a cornerstone of the improvement methods described above.

Improving population health starts and ends with each citizen. Making improvement everyone’s business will create the opportunity to put improvement knowledge in their hands. We hope in the future that citizens will develop fluency in improvement methods and be able to design their personal health driver diagrams, to test changes in their own lives that are co-designed with members of their “life system,” and to measure progress over time. The same principle applies to all health economy actors, who yield the benefit of having a common language for implementation to progress towards better population health. The jargon filled nature of improvement literature needs to be tackled to make the content accessible to citizens, families, health professionals, and other actors of health economies.

Covid-19 will continue to have a profound effect on the health of populations globally and is already challenging health systems to work agilely with local partners to better serve their communities. Our experience based reflections are offered as both a provocation and an invitation to stakeholders in population health improvement to adopt a common improvement method to accelerate progress. Furthermore, we think that clearly defining population health improvement as a field of learning can help those working towards better population health share lessons, successes, and opportunities from their efforts. We envision a future where systematic use of a shared improvement method will yield valuable lessons about improving population health, and a thriving population health improvement learning community will continue to grow in numbers and strength.

Key messages

Improvement methods traditionally used in healthcare can also be used by other actors outside healthcare working to improve population health

The adoption of improvement methods by stakeholders working to improve population health has the potential to catalyse their joint efforts

Using common implementation tools and language can help to achieve shared aims

Population health improvement learning is likely to exponentially increase in months and years to come

Contributors and sources: PD and JA have worked with partners across Latin America in population health improvement efforts including local municipalities, education, health services, citizens and others working together in pursuit of better outcomes. PD has also worked in the UK with partners in pursuit of better population health. AS has worked across East London NHS Foundation Trust and through the Royal College of Psychiatrists with a range of partners in pursuit of better care and health at sustainable costs, using improvement methods. KB is a practising GP, who is working in the community based partnership Bridges for Better Health in Region Sjaelland (Denmark) for better population health and health equity, applying improvement methods. JE is working in pursuit of better population health as the public health director in Region Jönköping County. JE is also leading the national initiative Strategy for health at the Swedish Association of Local Authorities and Regions (SALAR). DA is a consultant in public health medicine and works at Imperial College Healthcare NHS Trust and through the Health Foundation on efforts to improve population health.

Competing interests: We have read and understood BMJ policy on competing interests and have the following to declare: none.

This article is part of a series commissioned by The BMJ based on ideas generated by a joint editorial group with members from the Health Foundation and The BMJ , including a patient/carer. The BMJ retained full editorial control over external peer review, editing, and publication. Open access fees and The BMJ ’s quality improvement editor post are funded by the Health Foundation.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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Addressing Social Determinants to Improve Population Health : The Balance Between Clinical Care and Public Health

• 1 Boston Medical Center, Boston, Massachusetts
• 2 Department of Pediatrics, Boston University School of Medicine, Boston, Massachusetts

In 2015, a pediatrician in Flint, Michigan, recognized the relationship between an increase in her patients’ blood lead levels and the city’s recent change in water supply. The ensuing public health crisis was as revealing as it was tragic. Large numbers of children were found to have blood lead levels that put them at risk for neurotoxic sequelae, and an entire community became dependent on bottled water.

Four years later, the US Preventive Services Task Force (USPSTF) released its updated recommendation statement on screening children for elevated lead levels in the blood. 1 Although the USPSTF acknowledged the harm of elevated lead levels and confirmed the accuracy of lead screening tests, it found evidence for treating screen-detected individuals to be virtually nonexistent. On this basis, the USPSTF concluded that the evidence was insufficient to assess the balance of benefits and harms of screening for lead levels in children.

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Silverstein M , Hsu HE , Bell A. Addressing Social Determinants to Improve Population Health : The Balance Between Clinical Care and Public Health . JAMA. 2019;322(24):2379–2380. doi:10.1001/jama.2019.18055

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Advancing Health Disparities Research in Population Health

EDITOR IN CHIEF'S COLUMN — Volume 15 — November 29, 2018

Leonard Jack Jr, PhD, MSc

Suggested citation for this article: Jack L Jr. Advancing Health Disparities Research in Population Health. Prev Chronic Dis 2018;15:180588. DOI: http://dx.doi.org/10.5888/pcd15.180588 external icon .

Author Information

Healthy People 2020 defines health disparities as “a particular type of health difference that is closely linked with social, economic, and/or environmental disadvantage” (3). As part of its mission, PCD has published papers identifying the effect of behavioral, psychological, genetic, environmental, biological, and social factors on health outcomes. PCD has also sought out research on the effectiveness of interventions addressing these factors, with the focus on reducing the disproportionate burden of chronic diseases among at-risk populations. This collection features 9 articles that address this topic from multiple perspectives:

• The influence of implementation factors on the efficacy of school-based behavioral change interventions in low-income schools;
• The economic factors linked to food insecurity and dietary consumption on obesity among diverse populations;
• The relationships between consuming nuts, obesity-related foods, and body mass index among overweight and obese African American women in a rural setting;
• The differences in health care services for diabetes care between men and women;
• The influence of income, employment status, and education level on the prevalence of chronic disease among American Indian/Alaska Natives;
• The contribution of falls and fall-related injuries to injury and death among older adults with chronic kidney disease;
• The influence of sedentary behavior and the use of electronic screen devices among Mexican-origin children;
• The creation of a diabetic retinopathy screening tool for a low-income population; and
• The building of chronic disease epidemiology, surveillance, and evaluation in state and local health departments.

Childhood obesity continues to be a national concern, especially among low-income households (4). Blaine and colleagues described efforts to implement the “Eat Well and Keep Moving” and “Planet Health” behavioral change interventions as part of the Massachusetts Childhood Obesity Research Demonstration (MA-CORD) project in 2 school districts facing resource limitations and competing priorities (5). Researchers shared important insights on the role that key implementation outcomes such as fidelity, cost, reach, and sustainability played in school participation and sustainability of intervention activities.

The effect of short-term and long-term economic strain on the health and well-being of individuals and families is well established in published literature (6). Economic factors have been linked to food insecurity and obesity across the life stages (7). Using a spatial-based approach, Kim and colleagues identified new insights into the relationship between county-level income inequality, poverty, and obesity prevalence across New York State (8). Researchers found that higher income inequality was associated with lower obesity rates and that higher percentages of poverty were associated with higher obesity rates.

High obesity rates among African Americans continue to be a tremendous public health concern (9). High obesity rates have been linked to numerous factors, including biology, dietary consumption, population characteristics, access to care, socioeconomic status, and environment (10). Sterling and coauthors conducted research that monitored and analyzed changes in nut intake, other obesity-related foods (red or processed meats, added sugars), and body mass index during a 2-year weight loss intervention (11). The weight loss intervention targeted 383 overweight and obese African American women living in rural Alabama and Mississippi. Researchers found that nut consumers had a lower body mass index than non-nut eaters. Even after accounting for kilocalorie consumption and physical activity engagement, weight loss by the end of the intervention was significant among nut consumers but not among non-nut consumers. Researchers found that intervention results were linked to nut consumers consuming less red meat than non-nut consumers and greater amounts of other nutritionally rich foods, such as fruits and vegetables.

The existence of disparities in the use of health care services by men and women has been the subject of increased empirical study in recent years (12,13). Mesa observed 100 patients with type 2 diabetes aged 45 or older who lived in Ventura County, California, to compare differences in health care services (hemoglobin A 1c test, cholesterol test, and retina examination) between men and women (14). During 1 year, although men and women had access to similar health care services for diabetes, men had higher hemoglobin A 1c levels and lower rates of showing up for appointments. Findings from this study provide evidence that continued efforts are needed to identify motivating factors to increase appointment scheduling and attendance among men.

Chronic diseases such as heart disease, diabetes, kidney disease, and chronic lower respiratory disease disproportionately affect American Indians/Alaska Native populations, resulting in low life expectancy (15). Adamsen and colleagues conducted a national survey to measure the influence of income, employment status, and education level on the prevalence of chronic disease in a sample of 14,632 American Indians/Alaska Natives from 2011 through 2014 (16). Researchers found that most (89.7%) study participants were diagnosed with at least 1 chronic disease. American Indians/Alaska Natives with middle-to-low income levels and those who were unemployed were more likely to have received a diagnosis of a chronic disease. The authors discussed how economic development and job creation may decrease the prevalence of chronic disease in tribal communities.

Falls and fall-related injuries are the leading cause of injury and death among adults aged 65 or older (17), especially among those with chronic kidney disease (18). Kistler and colleagues performed a secondary analysis of 157,753 adults aged 65 or older in the 2014 Behavioral Risk Factor Surveillance System (19). Researchers found that adults aged 65 or older with chronic kidney disease were at increased risk of falling compared with adults in the same age range without chronic kidney disease. Researchers also found that modifiable factors such as physical function and recent exercise were most closely related to reduced risk and could be an appropriate target for fall prevention and rehabilitation programs.

Diverse factors, including family history, behavior, dietary habits, and environmental characteristics, simultaneously influence obesity among children in the United States (20). McDonald and her team of researchers examined sedentary behavior and the use of electronic screen devices among low-income Mexican-origin children aged 6 to 10 years living in rural communities near the US–Mexico border (21). Through interviews of 202 parents, researchers found that increased odds of heavy screen use were associated with having a television on while children ate. Parents reported that children also had access to electronic devices, social media, and the internet. Consistent with previously published research, this research affirmed the need to reduce screen time among children, particularly those at high risk for obesity.

Diabetes is a major public health crisis in Mexico, with mortality rates among the highest in the world (22). Diabetes is associated with complications, such as diabetic retinopathy, that impede quality of life among patients (23). Last year’s PCD Student Research Paper Contest winner in the graduate (master’s degree) category, authored by Mendoza-Herrera and colleagues, presented research results on a tool they developed to screen for diabetic retinopathy in a low-income population (24). These researchers developed the screening tool after analyzing biochemical, clinical, anthropometric, and sociodemographic information on 1,000 adults living with diabetes in low-income communities in Mexico. They developed a low-cost and easy-to-use screening tool that accounted for risk factors for diabetic retinopathy such as time since diabetes diagnosis, high blood glucose levels, systolic hypertension, and physical inactivity.

And finally in this collection, PCD examined the unique position of public health workers in state and local health departments to address social determinants of health, health inequities, and population health improvements across a range of chronic conditions in the United States (25). Calanan and colleagues described the efforts of CDC’s State Chronic Disease Epidemiology Assignee Program, a national program designed to build state and local chronic disease epidemiology, surveillance, and evaluation capacity by placing CDC field assignees in state and local health departments (26). The authors discussed how these assignees provide assistance in critical areas including conducting epidemiologic studies, building surveillance systems, evaluating chronic disease prevention and control programs, analyzing data, and training entry-level and mid-level chronic disease epidemiologists.

The articles selected for this collection demonstrate PCD’s commitment to publishing cutting-edge research for researchers, practitioners, and policy makers to better understand the multifactorial causes of health disparities, so they can develop the most effective strategies for improving health outcomes. Findings across research shared in this collection highlight the importance of employing effective interventions that address both individual and contextual factors (27). In dedicating this special collection to Dr Cunningham for his career as a social epidemiologist, published author, and esteemed PCD associate editor, we honor the excellent work that has been accomplished so far and promise to continue identifying and publishing health disparities research that increases the public health field’s understanding of what actions to take. Authors are encouraged to visit the Author’s Corner section of the journal’s website at https://www.cdc.gov/pcd/for_authors/index.htm to learn more about article types that best fit their research addressing population-based approaches to ameliorate health disparities.

Leonard Jack Jr, PhD, MSc, Editor in Chief, Preventing Chronic Disease: Public Health Research, Practice, and Policy, Office of Medicine and Science, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 4770 Buford Hwy NE, Mailstop F-80, Atlanta, GA 30341. Email: [email protected] .

• Jack L Jr. Using Using PCD ’s first-ever external review to enhance the journal’s worldwide usefulness to researchers, practitioners, and policy makers. Prev Chronic Dis 2018;15:. CrossRef external icon PubMed external icon
• Jack L Jr. In memory of Dr. Timothy Cunningham. https://www.cdc.gov/pcd/announcements.htm/#tim. Accessed November 13, 2018.
• US Department of Health and Human Services. Section IV: Advisory Committee findings and recommendation. In: The Secretary’s Advisory Committee on National Health Promotion and Disease Prevention Objectives for 2020. Phase I report: recommendations for the framework and format of Healthy People 2020. http://www.healthypeople.gov/sites/default/files/phaseI_0.pdf. Accessed November 17, 2018.
• Li M, Mustillo S, Anderson J. Childhood poverty dynamics and adulthood overweight/obesity: unpacking the black box of childhood. Soc Sci Res 2018;76(Nov):92–104. CrossRef external icon PubMed external icon
• Blaine RE, Franckle RL, Ganter C, Falbe J, Giles C, Criss S, et al. Using school staff members to implement a childhood obesity prevention intervention in low-income school districts: the Massachusetts Childhood Obesity Research Demonstration (MA-CORD Project), 2012–2014. Prev Chronic Dis 2017;14:. CrossRef external icon PubMed external icon
• Doom JR, Mason SM, Suglia SF, Clark CJ. Pathways between childhood/adolescent adversity, adolescent socioeconomic status, and long-term cardiovascular disease risk in young adulthood. Soc Sci Med 2017;188(Sept):166–75. CrossRef external icon PubMed external icon
• Gundersen C, Ziliak JP. Food insecurity and health outcomes. Health Aff (Millwood) 2015;34(11):1830–9. CrossRef external icon PubMed external icon
• Kim D, Wang F, Arcan C. Geographic association between income inequality and obesity among adults in New York State. Prev Chronic Dis 2018;15:. CrossRef external icon PubMed external icon
• Warnecke RB, Oh A, Breen N, Gehlert S, Paskett E, Tucker KL, et al. Approaching health disparities from a population perspective: the National Institutes of Health Centers for Population Health and Health Disparities. Am J Public Health 2008;98(9):1608–15. CrossRef external icon PubMed external icon
• Fosse E, Helgesen MK, Hagen S, Torp S. Addressing the social determinants of health at the local level: opportunities and challenges. Scand J Public Health 2018;46(20_suppl):47–52. CrossRef external icon PubMed external icon
• Sterling SR, Bertrand B, Judd S, Carson TL, Chandler-Laney P, Baskin ML. Longitudinal analysis of nut-inclusive diets and body mass index among overweight and obese African American women living in rural Alabama and Mississippi, 2011–2013. Prev Chronic Dis 2017;14:. CrossRef external icon PubMed external icon
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• Vaidya V, Partha G, Karmakar M. Gender differences in utilization of preventive care services in the United States. J Womens Health (Larchmt) 2012;21(2):140–5. CrossRef external icon PubMed external icon
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• Espey DK, Jim MA, Cobb N, Bartholomew M, Becker T, Haverkamp D, et al. Leading causes of death and all-cause mortality in American Indians and Alaska Natives. Am J Public Health 2014;104(Suppl 3):S303–11. CrossRef external icon PubMed external icon
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• Paliwal Y, Slattum PW, Ratliff SM. Chronic health conditions as a risk factor for falls among the community-dwelling US older adults: a zero-inflated regression modeling approach. BioMed Res Int 2017;2017:. CrossRef external icon PubMed external icon
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Population Health: What Is It and Why Is It Important?

From COVID to cancer, a variety of health concerns loom large and threaten to shorten lifespans and limit quality of life. These issues are best addressed by skilled public health professionals, who apply research-backed strategies to improve access to health services and advocate for community-centric policies.

Population health plays an increasingly vital role in this effort,. and can play heavily into everything from health care systems management to chronic illness prevention.

To clarify these important concepts, we’re diving into what population health involves, how it's related to public health, and why both are important. We will also provide insight into the expanding role of health data analysis and the value of developing these skills while pursuing a master’s degree in public health.

What Is Population Health?

It is impossible to truly understand population health without first developing a strong grasp of public health. The World Health Organization (WHO) provides a simple but powerful description: "Public health aims to provide maximum benefit for the largest number of people."

Population health instead focuses on the health status and health outcomes within a specific group of people. The term 'population' refers to groups of patients linked by defined similarities, such as their health diagnoses, geographic location, or health care provider.

Historical Context of Population Health

While the concept of public health dates back millennia, population health as a practice represents a far more recent development. The term was coined by Robert Evans and Greg Stoddart in 1990. Stoddart later worked with David Kindig to develop a more targeted use of the term as "the health outcomes of a group of individuals, including the distribution of such outcomes within the group."

Since then, the concept of population health has expanded in vital ways, used by social scientists and medical practitioners. In  , the population in question often involves groups of patients, while social science now emphasizes the "primal role of factors outside the traditional biomedical model."

Key Components of Population Health

Public health is a multifaceted concept that references the many factors that can play into health and well-being. The Centers for Disease Control and Prevention (CDC) references 10 Essential Public Health Services (EPHS) that all communities must implement and maintain to promote positive health outcomes.

As CDC resources reveal , EPHS strives to "actively promote policies, systems, and overall community conditions" and also, "remove systemic and structural barriers that have resulted in health inequities."

Population health involves understanding and addressing the diverse factors that influence health outcomes across different populations. Unlike the broad scope of public health, population health zeroes in on targeted interventions tailored to specific communities or population groups. This approach considers a range of determinants, including social, economic, environmental, and behavioral factors, that affect the health of these groups.

The essential public health services identified by the CDC are:

• Assess and monitor. Public health improvements are not possible unless there is a clear understanding of where deficits exist and to what extent they are problematic. As such, health data must be collected and analyzed continually, with data sharing strongly encouraged.  
• Investigate and diagnose. Real-time monitoring and epidemiological identification allow communities to swiftly respond to acute health concerns. Rapid screening capabilities are essential, with data ideally gathered from a variety of real-time sources.  
• Communicate effectively. Once health concerns are understood, these findings should be shared via social media, mass media and other channels. Messaging must be "culturally and linguistically appropriate" based on the makeup of the community in question. Influencers and stakeholders can play a valuable role in crafting and sharing messages that resonate.  
• Strengthen and support communities. From transportation to housing, a variety of agencies should form coalitions that recognize the multifaceted and holistic nature of modern health issues. These partnerships can be a valuable source of insights but must be continually nurtured.  
• Create and implement policies. As we'll discuss in more detail below, public policies set the foundation for improving access to necessary services. These policies should be examined and improved over time to correct previous injustices.  
• Use regulatory actions. Once policies have been established, they should be applied and enforced equitably. This means different things in different contexts; enforcing sanitary codes, for example, or following up when preventable injuries are revealed in occupational settings.  
• Ensure equitable access. All individuals deserve access to high-quality, cost-effective care, along with social services that safeguard and promote health at the community level. Health diversity systems must be engaged to address gaps in care or other barriers.  
• Build a skilled public health workforce. It takes a sizable and well-trained public health workforce to achieve the outcomes highlighted above. Priorities include comprehensive skill development and a culture of lifelong learning.  
• Improve and innovate public health functions. Ongoing research can contribute greatly to the "evidence base of effective public health practice." A blend of qualitative and quantitative findings can inform decision-making and help spur much-needed innovation.  
• Build a strong organizational infrastructure. A variety of infrastructures support the public health system. Across these organizations, resources must be allocated and utilized effectively. Accountability, transparency, and inclusivity are essential.

Role of Data in Population Health

Data plays a central role in advancing population health initiatives, enabling the identification of health disparities, the tailoring of interventions to specific groups, and the measurement of outcomes to inform future efforts. Population health relies on the detailed analysis of health data to understand the complex interplay of factors that influence health outcomes for different populations. Health care data analytics are critical for extracting insights from data, identifying trends, and evaluating the impact of interventions.

In population health, data analytics extend beyond traditional health care settings, encompassing a wide range of determinants including socioeconomic status, education, environment, and lifestyle factors. By integrating data from these diverse sources, population health professionals can develop a comprehensive understanding of health challenges and opportunities within specific populations, thereby informing targeted, effective interventions.

Moreover, the use of predictive analytics in population health is growing, allowing for the anticipation of health trends and the proactive management of health risks. This forward-looking approach enables health care providers and public health officials to allocate resources more efficiently and implement preventative measures tailored to the needs of specific populations.

Through the strategic use of data, population health initiatives can achieve their goal of improving health outcomes and reducing disparities, demonstrating the crucial role of data-driven decision-making in addressing the complex health challenges faced by diverse populations.

Challenges in Population Health

Despite advancements in technology and developments in the medical world, population health faces significant challenges, which include:

• Health disparities. Population health researchers recognize that huge disparities exist based on race, income, gender, sexual orientation and geographic location. A useful example highlighted by the National Conference of State Legislatures (NCSL) is that those living in rural areas are more likely to die as a result of cancer or heart disease. Similarly, people of color more frequently suffer alarming health complications in pregnancy, childbirth and postpartum.  
• Chronic diseases. While exciting advancements have all but eradicated many diseases that were once deadly (and in doing so, greatly extended life expectancy across the globe), chronic concerns are increasingly common and, in many ways, more difficult to address. From heart disease to diabetes, these health issues cause significant suffering and can also increase susceptibility to many acute conditions.  
• Funding limitations. Public health agencies have long struggled to secure sufficient funding, a problem that has only gotten worse with time. A concerning report from the Trust for America's Health explains, "Decades of underfunding have left the nation’s public health system ill-equipped to protect the health of Americans."

Population Health Policies and Administration

Public policy can provide a powerful blueprint to help drive improvements in health and well-being across communities. Because legislators often struggle to recognize the long-term value of these solutions, there is a clear need for public health advocates committed to strengthening the impact of local health departments and agencies.

Public health advocates are determined to improve access to federal funding and help legislators pass laws that protect the public from significant health hazards. There is also a public element necessary to ensure that proper licensing and credentialing are promoted and enforced across the health care workforce. Furthermore, as the EPHS reveals, public advocacy and legislative efforts may involve "health considerations in laws from other sectors," such as zoning.

Career Opportunities in Population Health

Population health can form the basis of a rewarding career path. No two roles in population health are identical; a lot depends on the unique needs of the community in question and the resources available to tackle these challenges. Compelling career opportunities include:

• Community health specialist. Working closely with specific demographic groups or within clearly defined geographic locations, community health specialists develop a thorough understanding of the communities they serve and the unique challenges those communities face.  
• Public health analyst. Sometimes referred to as population health analysts, these skilled professionals collect and analyze data related to various facets of public health, such as substance abuse or environmental hazards. Through their research on existing issues and related programs or policies, they can make data-backed recommendations for where and how to improve.  
• Epidemiologist. As investigators focused on the causes of (and factors that exacerbate) disease, epidemiologists advocate for protective policies and educate the public about health risks. From researchers to program coordinators, it takes many skilled professionals to develop programs and projects, collect data, perform surveillance reports and convey findings to leaders and the public.  
• Public health manager. As the administrative backbone of health services, public health management strives to streamline health-related systems while ensuring the optimal use of potentially limited resources.

The Future of Population Health

Challenges abound, but a bright future is within reach for population health as a practice and as a career track. Many of the most exciting opportunities relate to the rise of health data analysis, including an increased emphasis on predictive analytics. These data-driven strategies allow public health agencies to do more with less and can provide evidence-backed support to drive effective health policy implementation.

Beyond technology, future trends in public health will largely center around efforts to boost equitable care and services, as evidenced by the central goals of Healthy People 2030 to "Eliminate health disparities, achieve health equity, and attain health literacy to improve the health and well-being of all." These efforts will call for evidence-based interventions, including the use of data-driven strategies to track health disparities.

Learn More, Today

Are you passionate about public health and eager to make a difference? Comprehensive education is the first step to a successful career. Given the increasing reliance on data-informed solutions in health services administration, it's clear that a graduate-level education is critical.

University of Minnesota online master of public health degree programs meet the unique needs of today's busy public health professionals. Contact us today to learn more about these graduate programs and the role they could play in shaping your public health career.

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• The Impact of Chronic Underfunding on America’s Public Health System: Trends, Risks, and Recommendations, 2023
• What is population health?
• On the Distinction—or Lack of Distinction—Between Population Health and Public Health
• What Epidemiologists Do
• The 10 Essential Public Health Services (PDF)

Hypergraphs for Frailty Analysis Research Paper

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• Zoe Hancox   ORCID: orcid.org/0000-0003-0473-5971 8 ,
• Samuel D. Relton   ORCID: orcid.org/0000-0003-0634-4587 8 ,
• Andrew Clegg   ORCID: orcid.org/0000-0001-5972-1097 8 ,
• Philip G. Conaghan   ORCID: orcid.org/0000-0002-3478-5665 8 &
• Dan Schofield   ORCID: orcid.org/0000-0002-9251-8653 9  

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Frailty and multimorbidity becomes more prevalent as the population continues to age. We employ directed hypergraphs to represent the complex interactions among multiple health conditions and capture the inter-dependencies within multimorbidity sets. We introduce the inclusion of ‘Mortality’ nodes into directed hypergraphs. Through the analysis of ResearchOne data, we aim to identify the most prevalent combinations of frailty conditions alongside their co-occurrence with mortality, providing valuable knowledge for healthcare professionals to improve patient care and develop targeted interventions. We demonstrate that hypergraphs enable us to determine the probability of acquiring another electronic frailty index (eFI) condition, understand condition connectivity and sequentiality, and identify the most influential hyperarcs. The findings from this study suggest that hypergraphs enable us to retain progression information compared to holistic views, facilitating the implementation of more effective healthcare strategies.

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Acknowledgements

This work uses data provided by patients and collected by the NHS as part of their care and support. The methodological hypergraphs work was conducted during a PhD internship at NHS England. Zoe Hancox receives support through EPSRC funding (Grant No. EP/S024336/1).

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Hancox, Z., Relton, S.D., Clegg, A., Conaghan, P.G., Schofield, D. (2024). Hypergraphs for Frailty Analysis Research Paper. In: De Smedt, J., Soffer, P. (eds) Process Mining Workshops. ICPM 2023. Lecture Notes in Business Information Processing, vol 503. Springer, Cham. https://doi.org/10.1007/978-3-031-56107-8_21

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Patients’ Experiences With Digitalization in the Health Care System: Qualitative Interview Study

Authors of this article:

Original Paper

• Christian Gybel Jensen 1 * , MA   ; 
• Frederik Gybel Jensen 1 * , MA   ; 
• Mia Ingerslev Loft 1, 2 * , MSc, PhD  

1 Department of Neurology, Rigshospitalet, Copenhagen, Denmark

2 Institute for People and Technology, Roskilde University, Roskilde, Denmark

*all authors contributed equally

Corresponding Author:

Mia Ingerslev Loft, MSc, PhD

Department of Neurology

Rigshospitalet

Inge Lehmanns Vej 8

Phone: 45 35457076

Email: [email protected]

Background: The digitalization of public and health sectors worldwide is fundamentally changing health systems. With the implementation of digital health services in health institutions, a focus on digital health literacy and the use of digital health services have become more evident. In Denmark, public institutions use digital tools for different purposes, aiming to create a universal public digital sector for everyone. However, this digitalization risks reducing equity in health and further marginalizing citizens who are disadvantaged. Therefore, more knowledge is needed regarding patients’ digital practices and experiences with digital health services.

Objective: This study aims to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients in the neurology field and address the following research questions: (1) How do patients use digital services and digital tools? (2) How do they experience them?

Methods: We used a qualitative design with a hermeneutic approach. We conducted 31 semistructured interviews with patients who were hospitalized or formerly hospitalized at the department of neurology in a hospital in Denmark. The interviews were audio recorded and subsequently transcribed. The text from each transcribed interview was analyzed using manifest content analysis.

Results: The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools. Our findings show that digital tools were experienced differently, and specific conditions were important for the possibility of engaging in digital practices, including having access to social resources; possessing physical, cognitive, and communicative capabilities; and feeling motivated, secure, and comfortable. These prerequisites were necessary for participants to have positive experiences using digital tools in the health care system. Those who did not have these prerequisites experienced challenges and, in some cases, felt left out.

Conclusions: Experiences with digital practices and digital health services are complex and multifaceted. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges. To ensure that digitalization does not create inequities in health, it is necessary for developers and institutions to be aware of the differences in digital health literacy, focus on simplifying communication with patients and next of kin, and find flexible solutions for citizens who are disadvantaged.

Introduction

In 2022, the fourth most googled question in Denmark was, “Why does MitID not work?” [ 1 ]. MitID (My ID) is a digital access tool that Danes use to enter several different private and public digital services, from bank accounts to mail from their municipality or the state. MitID is a part of many Danish citizens’ everyday lives because the public sector in Denmark is digitalized in many areas. In recent decades, digitalization has changed how governments and people interact and has demonstrated the potential to change the core functions of public sectors and delivery of public policies and services [ 2 ]. When public sectors worldwide become increasingly digitalized, this transformation extends to the public health sectors as well, and some studies argue that we are moving toward a “digital public health era” that is already impacting the health systems and will fundamentally change the future of health systems [ 3 ]. While health systems are becoming more digitalized, it is important that both patients and digitalized systems adapt to changes in accordance with each other. Digital practices of people can be understood as what people do with and through digital technologies and how people relate to technology [ 4 ]. Therefore, it is relevant to investigate digital practices and how patients perceive and experience their own use of digital tools and services, especially in relation to existing digital health services. In our study, we highlight a broad perspective on experiences with digital practices and particularly add insight into the challenges with digital practices faced by patients who have acute or chronic illness, with some of them also experiencing physical, communicative, or cognitive difficulties.

An international Organization for Economic Cooperation and Development report indicates that countries are digitalized to different extents and in different ways; however, this does not mean that countries do not share common challenges and insights into the implementation of digital services [ 2 ].

In its global Digital Government Index, Denmark is presented as one of the leading countries when it comes to public digitalization [ 2 ]. Recent statistics indicate that approximately 97% of Danish families have access to the internet at home [ 5 ]. The Danish health sector already offers many different digital services, including web-based delivery of medicine, e-consultations, patient-related outcome questionnaires, and seeking one’s own health journal or getting test results through; “Sundhed” [ 6 ] (the national health portal) and “Sundhedsjournalen” (the electronic patient record); or the apps “Medicinkortet” (the shared medication record), “Minlæge” (My Doctor, consisting of, eg, communication with the general practitioner), or “MinSP” (My Health Platform, consisting of, eg, communication with health care staff in hospitals) [ 6 - 8 ].

The Danish Digital Health Strategy from 2018 aims to create a coherent and user-friendly digital public sector for everyone [ 9 ], but statistics indicate that certain groups in society are not as digitalized as others. In particular, the older population uses digital services the least, with 5% of people aged 65 to 75 years and 18% of those aged 75 to 89 years having never used the internet in 2020 [ 5 ]. In parts of the literature, it has been problematized how the digitalization of the welfare state is related to the marginalization of older citizens who are socially disadvantaged [ 10 ]. However, statistics also indicate that the probability of using digital tools increases significantly as a person’s experience of using digital tools increases, regardless of their age or education level [ 5 ].

Understanding the digital practices of patients is important because they can use digital tools to engage with the health system and follow their own health course. Researching experiences with digital practices can be a way to better understand potential possibilities and barriers when patients use digital health services. With patients becoming more involved in their own health course and treatment, the importance of patients’ health literacy is being increasingly recognized [ 11 ]. The World Health Organization defines health literacy as the “achievement of a level of knowledge, personal skills and confidence to take action to improve personal and community health by changing personal lifestyles and living conditions” [ 12 ]. Furthermore, health literacy can be described as “a person’s knowledge and competencies to meet complex demands of health in modern society, ” and it is viewed as a critical step toward patient empowerment [ 11 , 12 ]. In a digitalized health care system, this also includes the knowledge, capabilities, and resources that individuals require to use and benefit from eHealth services, that is, “digital health literacy (eHealth literacy)” [ 13 ]. An eHealth literacy framework created by Norgaard et al [ 13 ] identified that different aspects, for example, the ability to process information and actively engage with digital services, can be viewed as important facets of digital health literacy. This argument is supported by studies that demonstrate how patients with cognitive and communicative challenges experience barriers to the use of digital tools and require different approaches in the design of digital solutions in the health sector [ 14 , 15 ]. Access to digital services and digital literacy is becoming increasingly important determinants of health, as people with digital literacy and access to digital services can facilitate improvement of health and involvement in their own health course [ 16 ].

The need for a better understanding of eHealth literacy and patients’ capabilities to meet public digital services’ demands as well as engage in their own health calls for a deeper investigation into digital practices and the use of digital tools and services from the perspective of patients with varying digital capabilities. Important focus areas to better understand digital practices and related challenges have already been highlighted in various studies. They indicate that social support, assessment of value in digital services, and systemic assessment of digital capabilities are important in the use and implementation of digital tools, and they call for better insight into complex experiences with digital services [ 13 , 17 , 18 ]. Therefore, we aimed to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients, addressing the following research questions: how do patients use digital services and digital tools, and how do they experience them?

We aimed to investigate digital practices and experiences with digital health services and digital tools; therefore, we used a qualitative design and adopted a hermeneutic approach as the point of departure, which means including preexisting knowledge of digital practices but also providing room for new comprehension [ 19 ]. Our interpretive approach is underpinned by the philosophical hermeneutic approach by Gadamer et al [ 19 ], in which they described the interpretation process as a “hermeneutic circle,” where the researcher enters the interpretation process with an open mind and historical awareness of a phenomenon (preknowledge). We conducted semistructured interviews using an interview guide. This study followed the COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist [ 20 ].

Setting and Participants

To gain a broad understanding of experiences with public digital health services, a purposive sampling strategy was used. All 31 participants were hospitalized or formerly hospitalized patients in a large neurological department in the capital of Denmark ( Table 1 ). We assessed whether including patients from the neurological field would give us a broad insight into the experiences of digital practices from different perspectives. The department consisted of, among others, 8 inpatient units covering, for example, acute neurology and stroke units, from which the patients were recruited. Patients admitted to a neurological department can have both acute and transient neurological diseases, such as infections in the brain, stroke, or blood clot in the brain from which they can recover completely or have persistent physical and mental difficulties, or experience chronic neurological and progressive disorders such as Parkinson disease and dementia. Some patients hospitalized in neurological care will have communicative and cognitive difficulties because of their neurological disorders. Nursing staff from the respective units helped the researchers (CGJ, FGJ, and MIL) identify patients who differed in terms of gender, age, and severity of neurological illness. Some patients (6/31, 19%) had language difficulties; however, a speech therapist assessed them as suitable participants. We excluded patients with severe cognitive difficulties and those who were not able to speak the Danish language. Including patients from the field of neurology provided an opportunity to study the experience of digital health practice from various perspectives. Hence, the sampling strategy enabled the identification and selection of information-rich participants relevant to this study [ 21 ], which is the aim of qualitative research. The participants were invited to participate by either the first (CGJ) or last author (MIL), and all invited participants (31/31, 100%) chose to participate.

All 31 participants were aged between 40 to 99 years, with an average age of 71.75 years ( Table 1 ). Out of the 31 participants, 10 (32%) had physical disabilities or had cognitive or communicative difficulties due to sequela in relation to neurological illness or other physical conditions.

Data Collection

The 31 patient interviews were conducted over a 2-month period between September and November 2022. Of the 31 patients, 20 (65%) were interviewed face-to-face at the hospital in their patient room upon admission and 11 (35%) were interviewed on the phone after being discharged. The interviews had a mean length of 20.48 minutes.

We developed a semistructured interview guide ( Table 2 ). The interview questions were developed based on the research aim, findings from our preliminary covering of literature in the field presented in the Introduction section, and identified gaps that we needed to elaborate on to be able to answer our research question [ 22 ]. The semistructured interview guide was designed to support the development of a trusting relationship and ensure the relevance of the interviews’ content [ 22 ]. The questions served as a prompt for the participants and were further supported by questions such as “please tell me more” and “please elaborate” throughout the interview, both to heighten the level of detail and to verify our understanding of the issues at play. If the participant had cognitive or communicative difficulties, communication was supported using a method called Supported Communication for Adults with Aphasia [ 23 ] during the interview.

The interviews were performed by all authors (CGJ, FGJ, and MIL individually), who were skilled in conducting interviews and qualitative research. The interviewers are not part of daily clinical practice but are employed in the department of neurology from where the patients were recruited. All interviews were audio recorded and subsequently transcribed verbatim by all 3 authors individually.

a PRO: patient-related outcome.

Data Analysis

The text from each transcribed interview was analyzed using manifest content analysis, as described by Graneheim and Lundman [ 24 ]. Content analysis is a method of analyzing written, verbal, and visual communication in a systematic way [ 25 ]. Qualitative content analysis is a structured but nonlinear process that requires researchers to move back and forth between the original text and parts of the text during the analysis. Manifest analysis is the descriptive level at which the surface structure of the text central to the phenomenon and the research question is described. The analysis was conducted as a collaborative effort between the first (CGJ) and last authors (MIL); hence, in this inductive circular process, to achieve consistency in the interpretation of the text, there was continued discussion and reflection between the researchers. The transcriptions were initially read several times to gain a sense of the whole context, and we analyzed each interview. The text was initially divided into domains that reflected the lowest degree of interpretation, as a rough structure was created in which the text had a specific area in common. The structure roughly reflected the interview guide’s themes, as guided by Graneheim and Lundman [ 24 ]. Thereafter, the text was divided into meaning units, condensed into text-near descriptions, and then abstracted and labeled further with codes. The codes were categorized based on similarities and differences. During this process, we discussed the findings to reach a consensus on the content, resulting in the final 4 categories presented in this paper.

Ethical Considerations

The interviewees received oral and written information about the study and its voluntary nature before the interviews. Written informed consent was obtained from all participants. Participants were able to opt of the study at any time. Data were anonymized and stored electronically on locked and secured servers. The Ethics Committee of the Capitol Region in Denmark was contacted before the start of the study. This study was registered and approved by the ethics committee and registered under the Danish Data Protection Agency (number P2021-839). Furthermore, the ethical principles of the Declaration of Helsinki were followed for this study.

The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools.

Social Resources as a Digital Lifeline

Throughout the analysis, it became evident that access to both material and social resources was of great importance when using digital tools. Most participants already possessed and had easy access to a computer, smartphone, or tablet. The few participants who did not own the necessary digital tools told us that they did not have the skills needed to use these tools. For these participants, the lack of material resources was tied particularly to a lack of knowledge and know-how, as they expressed that they would not know where to start after buying a computer—how to set it up, connect it to the internet, and use its many systems.

However, possessing the necessary material resources did not mean that the participants possessed the knowledge and skill to use digital tools. Furthermore, access to material resources was also a question of having access to assistance when needed. Some participants who had access to a computer, smartphone, and tablet and knew how to use these tools still had to obtain help when setting up hardware, updating software, or getting a new device. These participants were confident in their own ability to use digital devices but also relied on family, friends, and neighbors in their everyday use of these tools. Certain participants were explicitly aware of their own use of social resources when expressing their thoughts on digital services in health care systems:

I think it is a blessing and a curse. I think it is both. I would say that if I did not have someone around me in my family who was almost born into the digital world, then I think I would be in trouble. But I feel sorry for those who do not have that opportunity, and I know quite a few who do not. They get upset, and it’s really frustrating. [Woman, age 82 years]

The participants’ use of social resources indicates that learning skills and using digital tools are not solely individual tasks but rather continuously involve engagement with other people, particularly whenever a new unforeseen problem arises or when the participants want a deeper understanding of the tools they are using:

If tomorrow I have to get a new ipad...and it was like that when I got this one, then I had to get XXX to come and help me move stuff and he was sweet to help with all the practical stuff. I think I would have cursed a couple of times (if he hadn’t been there), but he is always helpful, but at the same time he is also pedagogic so I hope that next time he showed me something I will be able to do it. [Man, age 71 years]

For some participants, obtaining assistance from a more experienced family member was experienced as an opportunity to learn, whereas for other participants, their use of public digital services was even tied directly to assistance from a spouse or family member:

My wife, she has access to mine, so if something comes up, she can just go in and read, and we can talk about it afterwards what (it is). [Man, age 85 years]

The participants used social resources to navigate digital systems and understand and interpret communication from the health care system through digital devices. Another example of this was the participants who needed assistance to find, answer, and understand questionnaires from the health care department. Furthermore, social resources were viewed as a support system that made participants feel more comfortable and safer when operating digital tools. The social resources were particularly important when overcoming unforeseen and new challenges and when learning new skills related to the use of digital tools. Participants with physical, cognitive, and communicative challenges also explained how social resources were of great importance in their ability to use digital tools.

Possessing the Necessary Capabilities

The findings indicated that possessing the desire and knowing how to use digital tools are not always enough to engage with digital services successfully. Different health issues can carry consequences for motor skills and mobility. Some of these consequences were visibly affecting how our participants interacted with digital devices, and these challenges were somewhat easy to discover. However, our participants revealed hidden challenges that posed difficulties. In some specific cases, cognitive and communicative inabilities can make it difficult to use digital tools, and this might not always be clear until the individual tries to use a device’s more complex functions. An example of this is that some participants found it easy to turn on a computer and use it to write but difficult to go through security measures on digital services or interpret and understand digital language. Remembering passwords and logging on to systems created challenges, particularly for those experiencing health issues that directly affect memory and cognitive abilities, who expressed concerns about what they were able to do through digital tools:

I think it is very challenging because I would like to use it how I used to before my stroke; (I) wish that everything (digital skills) was transferred, but it just isn’t. [Man, age 80 years]

Despite these challenges, the participants demonstrated great interest in using digital tools, particularly regarding health care services and their own well-being. However, sometimes, the challenges that they experienced could not be conquered merely by motivation and good intentions. Another aspect of these challenges was the amount of extra time and energy that the participants had to spend on digital services. A patient diagnosed with Parkinson disease described how her symptoms created challenges that changed her digital practices:

Well it could for example be something like following a line in the device. And right now it is very limited what I can do with this (iPhone). Now I am almost only using it as a phone, and that is a little sad because I also like to text and stuff, but I also find that difficult (...) I think it is difficult to get an overview. [Woman, age 62 years]

Some participants said that after they were discharged from the hospital, they did not use the computer anymore because it was too difficult and too exhausting , which contributed to them giving up . Using digital tools already demanded a certain amount of concentration and awareness, and some diseases and health conditions affected these abilities further.

Big Feelings as Facilitators or Barriers

The findings revealed a wide range of digital practices in which digital tools were used as a communication device, as an entertainment device, and as a practical and informative tool for ordering medicine, booking consultations, asking health-related questions, or receiving email from public institutions. Despite these different digital practices, repeating patterns and arguments appeared when the participants were asked why they learned to use digital tools or wanted to improve their skills. A repeating argument was that they wanted to “follow the times, ” or as a participant who was still not satisfied with her digital skills stated:

We should not go against the future. [Woman, age 89 years]

The participants expressed a positive view of the technological developments and possibilities that digital devices offered, and they wanted to improve their knowledge and skills related to digital practice. For some participants, this was challenging, and they expressed frustration over how technological developments “moved too fast ,” but some participants interpreted these challenges as a way to “keep their mind sharp. ”

Another recurring pattern was that the participants expressed great interest in using digital services related to the health care system and other public institutions. The importance of being able to navigate digital services was explicitly clear when talking about finding test answers, written electronic messages, and questionnaires from the hospital or other public institutions. Keeping up with developments, communicating with public institutions, and taking an interest in their own health and well-being were described as good reasons to learn to use digital tools.

However, other aspects also affected these learning facilitators. Some participants felt alienated while using digital tools and described the practice as something related to feelings of anxiety, fear, and stupidity as well as something that demanded “a certain amount of courage. ” Some participants felt frustrated with the digital challenges they experienced, especially when the challenges were difficult to overcome because of their physical conditions:

I get sad because of it (digital challenges) and I get very frustrated and it takes a lot of time because I have difficulty seeing when I look away from the computer and have to turn back again to find out where I was and continue there (...) It pains me that I have to use so much time on it. [Man, age 71 years]

Fear of making mistakes, particularly when communicating with public institutions, for example, the health care system, was a common pattern. Another pattern was the fear of misinterpreting the sender and the need to ensure that the written electronic messages were actually from the described sender. Some participants felt that they were forced to learn about digital tools because they cared a lot about the services. Furthermore, fears of digital services replacing human interaction were a recurring concern among the participants. Despite these initial and recurring feelings, some participants learned how to navigate the digital services that they deemed relevant. Another recurring pattern in this learning process was repetition, the practice of digital skills, and consistent assistance from other people. One participant expressed the need to use the services often to remember the necessary skills:

Now I can figure it out because now I’ve had it shown 10 times. But then three months still pass... and then I think...how was it now? Then I get sweat on my forehead (feel nervous) and think; I’m not an idiot. [Woman, age 82 years]

For some participants, learning how to use digital tools demanded time and patience, as challenges had to be overcome more than once because they reappeared until the use of digital tools was more automatized into their everyday lives. Using digital tools and health services was viewed as easier and less stressful when part of everyday routines.

Life Without Digital Tools: Not a Free Choice

Even though some participants used digital tools daily, other participants expressed that it was “too late for them.” These participants did not view it as a free choice but as something they had to accept that they could not do. They wished that they could have learned it earlier in life but did not view it as a possibility in the future. Furthermore, they saw potential in digital services, including digital health care services, but they did not know exactly what services they were missing out on. Despite this lack of knowledge, they still felt sad about the position they were in. One participant expressed what she thought regarding the use of digital tools in public institutions:

Well, I feel alright about it, but it is very, very difficult for those of us who do not have it. Sometimes you can feel left out—outside of society. And when you do not have one of those (computers)...A reference is always made to w and w (www.) and then you can read on. But you cannot do that. [Woman, age 94 years]

The feeling of being left out of society was consistent among the participants who did not use digital tools. To them, digital systems seemed to provide unfair treatment based on something outside of their own power. Participants who were heavily affected by their medical conditions and could not use digital services also felt left out because they saw the advantages of using digital tools. Furthermore, a participant described the feelings connected to the use of digital tools in public institutions:

It is more annoying that it does not seem to work out in my favour. [Woman, age 62 years]

These statements indicated that it is possible for individuals to want to use digital tools and simultaneously find them too challenging. These participants were aware that there are consequences of not using digital tools, and that saddens them, as they feel like they are not receiving the same treatment as other people in society and the health care system.

Principal Findings

The insights from our findings demonstrated that our participants had different digital practices and different experiences with digital tools and services; however, the analysis also highlighted patterns related to how digital services and tools were used. Specific conditions were important for the possibility of digital practice, including having access to social resources; possessing the necessary capabilities; and feeling motivated, secure, and comfortable . These prerequisites were necessary to have positive experiences using digital tools in the health care system, although some participants who lived up to these prerequisites were still skeptical toward digital solutions. Others who did not live up to these prerequisites experienced challenges and even though they were aware of opportunities, this awareness made them feel left out. A few participants even viewed the digital tools as a threat to their participation in society. This supports the notion of Norgaard et al [ 13 ] that the attention paid to digital capability demands from eHealth systems is very important. Furthermore, our findings supported the argument of Hjeltholt and Papazu [ 17 ] that it is important to better understand experiences related to digital services. In our study, we accommodate this request and bring forth a broad perspective on experiences with digital practices; we particularly add insight into the challenges with digital practices for patients who also have acute or chronic illness, with some of them also experiencing physical, communicative, and cognitive difficulties. To our knowledge, there is limited existing literature focusing on digital practices that do not have a limited scope, for example, a focus on perspectives on eHealth literacy in the use of apps [ 26 ] or intervention studies with a focus on experiences with digital solutions, for example, telemedicine during the COVID-19 pandemic [ 27 ]. As mentioned by Hjeltholt et al [ 10 ], certain citizens are dependent on their own social networks in the process of using and learning digital tools. Rasi et al [ 28 ] and Airola et al [ 29 ] argued that digital health literacy is situated and should include the capabilities of the individual’s social network. Our findings support these arguments that access to social resources is an important condition; however, the findings also highlight that these resources can be particularly crucial in the use of digital health services, for example, when interpreting and understanding digital and written electronic messages related to one’s own health course or when dealing with physical, cognitive, and communicative disadvantages. Therefore, we argue that the awareness of the disadvantages is important if we want to understand patients’ digital capabilities, and the inclusion of the next of kin can be evident in unveiling challenges that are unknown and not easily visible or when trying to reach patients with digital challenges through digital means.

Studies by Kayser et al [ 30 ] and Kanoe et al [ 31 ] indicated that patients’ abilities to interpret and understand digital health–related services and their benefits are important for the successful implementation of eHealth services—an argument that our findings support. Health literacy in both digital and physical contexts is important if we want to understand how to better design and implement services. Our participants’ statements support the argument that communication through digital means cannot be viewed as similar to face-to-face communication and that an emphasis on digital health literacy demonstrates how health systems are demanding different capabilities from the patients [ 13 ]. We argue that it is important to communicate the purposes of digital services so that both the patient and their next of kin know why they participate and how it can benefit them. Therefore, it is important to make it as clear as possible that digital health services can benefit the patient and that these services are developed to support information, communication, and dialogue between patients and health professionals. However, our findings suggest that even after interpreting and understanding the purposes of digital health services, some patients may still experience challenges when using digital tools.

Therefore, it is important to understand how and why patients learn digital skills, particularly because both experience with digital devices and estimation of the value of digital tools have been highlighted as key factors for digital practices [ 5 , 18 ]. Our findings indicate that a combination of these factors is important, as recognizing the value of digital tools was not enough to facilitate the necessary learning process for some of our participants. Instead, our participants described the use of digital tools as complex and continuous processes in which automation of skills, assistance from others, and time to relearn forgotten knowledge were necessary and important facilitators for learning and understanding digital tools as well as becoming more comfortable and confident in the use of digital health services. This was particularly important, as it was more encouraging for our participants to learn digital tools when they felt secure, instead of feeling afraid and anxious, a point that Bailey et al [ 18 ] also highlighted. The value of digital solutions and the will to learn were greater when challenges were viewed as something to overcome and learn from instead of something that created a feeling of being stupid. This calls for attention on how to simplify and explain digital tools and services so that users do not feel alienated. Our findings also support the argument that digital health literacy should take into account emotional well-being related to digital practice [ 32 ].

The various perspectives that our participants provided regarding the use of digital tools in the health care system indicate that patients are affected by the use of digital health services and their own capabilities to use digital tools. Murray et al [ 33 ] argued that the use of digital tools in health sectors has the potential to improve health and health delivery by improving efficacy, efficiency, accessibility, safety, and personalization, and our participants also highlighted these positive aspects. However, different studies found that some patients, particularly older adults considered socially vulnerable, have lower digital health literacy [ 10 , 34 , 35 ], which is an important determinant of health and may widen disparities and inequity in health care [ 16 ]. Studies on older adult populations’ adaptation to information and communication technology show that engaging with this technology can be limited by the usability of technology, feelings of anxiety and concern, self-perception of technology use, and the need for assistance and inclusive design [ 36 ]. Our participants’ experiences with digital practices support the importance of these focus areas, especially when primarily older patients are admitted to hospitals. Furthermore, our findings indicate that some older patients who used to view themselves as being engaged in their own health care felt more distanced from the health care system because of digital services, and some who did not have the capabilities to use digital tools felt that they were treated differently compared to the rest of society. They did not necessarily view themselves as vulnerable but felt vulnerable in the specific experience of trying to use digital services because they wished that they were more capable. Moreover, this was the case for patients with physical and cognitive difficulties, as they were not necessarily aware of the challenges before experiencing them. Drawing on the phenomenological and feministic approach by Ahmed [ 37 ], these challenges that make patients feel vulnerable are not necessarily visible to others but can instead be viewed as invisible institutional “walls” that do not present themselves before the patient runs into them. Some participants had to experience how their physical, cognitive, or communicative difficulties affected their digital practice to realize that they were not as digitally capable as they once were or as others in society. Furthermore, viewed from this perspective, our findings could be used to argue that digital capabilities should be viewed as a privilege tied to users’ physical bodies and that digital services in the health care system are indirectly making patients without this privilege vulnerable. This calls for more attention to the inequities that digital tools and services create in health care systems and awareness that those who do not use digital tools are not necessarily indifferent about the consequences. Particularly, in a context such as the Danish one, in which the digital strategy is to create an intertwined and user-friendly public digital sector for everyone, it needs to be understood that patients have different digital capabilities and needs. Although some have not yet had a challenging experience that made them feel vulnerable, others are very aware that they receive different treatment and feel that they are on their own or that the rest of the society does not care about them. Inequities in digital health care, such as these, can and should be mitigated or prevented, and our investigation into the experiences with digital practices can help to show that we are creating standards and infrastructures that deliberately exclude the perspectives of those who are most in need of the services offered by the digital health care system [ 8 ]. Therefore, our findings support the notions that flexibility is important in the implementation of universal public digital services [ 17 ]; that it is important to adjust systems in accordance with patients’ eHealth literacy and not only improve the capabilities of individuals [ 38 ]; and that the development and improvement of digital health literacy are not solely an individual responsibility but are also tied to ways in which institutions organize, design, and implement digital tools and services [ 39 ].

Limitations

This qualitative study provided novel insights into the experiences with public digital health services from the perspective of patients in the Danish context, enabling a deeper understanding of how digital health services and digital tools are experienced and used. This helps build a solid foundation for future interventions aimed at digital health literacy and digital health interventions. However, this study has some limitations. First, the study was conducted in a country where digitalization is progressing quickly, and people, therefore, are accustomed to this pace. Therefore, readers must be aware of this. Second, the study included patients with different neurological conditions; some of their digital challenges were caused or worsened by these neurological conditions and are, therefore, not applicable to all patients in the health system. However, the findings provided insights into the patients’ digital practices before their conditions and other challenges not connected to neurological conditions shared by patients. Third, the study was broad, and although a large number of informants was included, from a qualitative research perspective, we would recommend additional research in this field to develop interventions that target digital health literacy and the use of digital health services.

Conclusions

Experiences with digital tools and digital health services are complex and multifaceted. The advantages in communication, finding information, or navigating through one’s own health course work as facilitators for engaging with digital tools and digital health services. However, this is not enough on its own. Furthermore, feeling secure and motivated and having time to relearn and practice skills are important facilitators. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges that require assistance. Digitalization of the health care system means that patients do not have the choice to opt out of using digital services without having consequences, resulting in them receiving a different treatment than others. To ensure digitalization does not create inequities in health, it is necessary for developers and the health institutions that create, design, and implement digital services to be aware of differences in digital health literacy and to focus on simplifying communication with patients and next of kin through and about digital services. It is important to focus on helping individuals meet the necessary conditions and finding flexible solutions for those who do not have the same privileges as others if the public digital sector is to work for everyone.

Acknowledgments

The authors would like to thank all the people who gave their time to be interviewed for the study, the clinical nurse specialists who facilitated interviewing patients, and the other nurses on shift who assisted in recruiting participants.

Conflicts of Interest

None declared.

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Abbreviations

Edited by A Mavragani; submitted 14.03.23; peer-reviewed by G Myreteg, J Eriksen, M Siermann; comments to author 18.09.23; revised version received 09.10.23; accepted 27.02.24; published 11.04.24.

©Christian Gybel Jensen, Frederik Gybel Jensen, Mia Ingerslev Loft. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.