precision medicine essay

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Precision Medicine, AI, and the Future of Personalized Health Care

Kevin b. johnson.

1 Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville Tennessee, USA

2 Department of Pediatrics, Vanderbilt University Medical Center, Nashville Tennessee, USA

Wei‐Qi Wei

Dilhan weeraratne.

3 IBM Watson Health, Cambridge Massachusetts, USA

Mark E. Frisse

Karl misulis.

4 Department of Clinical Neurology, Vanderbilt University Medical Center, Nashville Tennessee, USA

Jane L. Snowdon

The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less‐common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.

In a recent National Academy of Medicine report about the current and future state of artificial intelligence (AI) in health care, the authors noted “unprecedented opportunities” to augment the care of specialists and the assistance that AI provides in combating the realities of being human (including fatigue and inattention) and the risks of machine error. Importantly, the report notes that whereas care must be taken with the use of these technologies, much promise exists. 1 The digitization of health‐related data and the rapid uptake in technology are fueling transformation and progress in the development and use of AI in healthcare. 2 , 3 , 4 However, multimodal data integration, security, federated learning (which requires fundamental advances in areas, such as privacy, large‐scale machine learning, and distributed optimization), model performance, and bias may pose challenges to the use of AI in health care. 5

Three main principles for successful adoption of AI in health care include data and security, analytics and insights, and shared expertise. Data and security equate to full transparency and trust in how AI systems are trained and in the data and knowledge used to train them. As humans and AI systems increasingly work together, it is essential that we trust the output of these systems.

Analytics and insights equate to purpose and people where “augmented intelligence” and “actionable insights” support what humans do, not replace them. AI can combine input from multiple structured and unstructured sources, reason at a semantic level, and use these abilities in computer vision, reading comprehension, conversational systems, and multimodal applications to help health professionals make more informed decisions (e.g., a physician making a diagnosis, a nurse creating a care plan, or a social services agency arranging services for an elderly citizen). Shared expertise equates to our complementary relationship with AI systems, which are trained by and are supporting human professionals, leading to workforce change, which leads to new skills. The ability to create cutting‐edge AI models and build high‐quality business applications requires skilled experts with access to the latest hardware.

A vast amount of untapped data could have a great impact on our health—yet it exists outside medical systems. 6 Our individual health is heavily influenced by our lifestyle, nutrition, our environment, and access to care. These behavioral and social determinants and other exogenous factors can now be tracked and measured by wearables and a range of medical devices. These factors account for about 60% of our determinants of health (behavioral, socio‐economical, physiological, and psychological data), our genes account for about 30%, and last our actual medical history accounts for a mere 10%. 6 Over the course of our lifetimes, we will each generate the equivalent of over 300 million books of personal and health‐related data that could unlock insights to a longer and healthier life. 7

The phenomenon of big data can be described using the five Vs: volume, velocity, variety, veracity, and value. Volume refers to the vast amount of complex and heterogenous data, which makes data sets too large to store and analyze using traditional database technology. Velocity refers to the speed at which new data are generated and moves around. Variety refers to the different types of structured, semistructured, and unstructured data, such as social media conversations and voice recordings. Veracity refers to the certainty, accuracy, relevance, and predictive value of the data. Value refers to the conversion of data into business insights. Whereas the volume, variety, velocity, and veracity of data are contributing to the increasing complexity of data management and workloads—creating a greater need for advanced analytics to discover insights—mobile devices have made technology more consumable, creating user demand for interactive tools for visual analytics.

Big data analytics and AI are increasingly becoming omnipresent across the entire spectrum of health care, including the 5 Ps spanning: payer, provider, policy maker/government, patients, and product manufacturers. Up to 10% of global health care expenditure is due to fraud and abuse and AI‐based tools help mitigate fraud, waste, and abuse in payer programs. 8 Reliable identification of medical coding errors and incorrect claims positively impacts payers, providers, and governments by saving inordinate amounts of money, time, and efforts. 9 As an example, IBM DataProbe, an AI‐based business intelligence tool, was able to detect and recover US $41.5 million in fee‐for‐service payments over a 2‐year period in Medicaid fraud for Iowa Medicaid Enterprise. 10 In the provider space, AI is used for evidence‐based clinical decision support, 11 detection of adverse events, and the usage of electronic health record (EHR) data to predict patients at risk for readmission. 12 Healthcare policymakers and government use AI‐based tools to control and predict infections and outbreaks. An example is FINDER, a machine‐learned model for real‐time detection of foodborne illness using anonymous and aggregated web search and location data. 13 Another example is the integrated data hub and care‐management solution using IBM Connect360 and IBM Watson Care Manager that Sonoma County, California government agencies used to transform health and healthcare for socially disadvantaged groups and other displaced individuals during a time of community‐wide crisis. 14 This solution enabled integration of siloed data and services into a unified citizen status view, identification of clinical and social determinants of health from structured and unstructured sources, construction of algorithms to match clients with services, and streamlining of care coordination during the 2017 and 2019 Sonoma County wildfires. With the advent of the global pandemic coronavirus disease 2019 (COVID‐19) in early 2020, such a model can be used to predict at‐risk populations, and potentially provide additional risk information to clinicians caring for at‐risk patients. 15 The use of AI for patients and life sciences/healthcare products are addressed extensively in the sections that follow.

AI is not, however, the only data‐driven field impacting health and health care. The field of precision medicine is providing an equal or even greater influence than AI on the direction of health care 16 and has been doing so for more than a decade. 17 Precision medicine aims to personalize care for every individual. This goal requires access to massive amounts of data, such as data collected through the United Kingdom’s UK Biobank and the All of Us project, coupled with a receptive health care ecosystem willing to abandon the conventional approach to care in favor of a more highly individualized strategy. The convergence of these fields will likely accelerate the goals of personalized care and tightly couple AI to healthcare providers for the foreseeable future. In the sections that follow, we will briefly summarize the capabilities of existing AI technology, describe how precision medicine is evolving, and, through a series of examples, demonstrate the potentially transformative effect of AI on the rate and increasing breadth of application for precision medicine.

The past 10 years have seen remarkable growth and acceptance of AI in a variety of domains and in particular by healthcare professionals. AI provides rich opportunities for designing intelligent products, creating novel services, and generating new business models. At the same time, the use of AI can introduce social and ethical challenges to security, privacy, and human rights. 1

AI technologies in medicine exist in many forms, from the purely virtual (e.g., deep‐learning‐based health information management systems and active guidance of physicians in their treatment decisions) to cyber‐physical (e.g., robots used to assist the attending surgeon and targeted nanorobots for drug delivery). 18 The power of AI technologies to recognize sophisticated patterns and hidden structures has enabled many image‐based detection and diagnostic systems in healthcare to perform as well or better than clinicians, in some cases. 19 AI‐enabled clinical decision‐support systems may reduce diagnostic errors, augment intelligence to support decision making, and assist clinicians with EHR data extraction and documentation tasks. 20 Emerging computational improvements in natural language processing (NLP), pattern identification, efficient search, prediction, and bias‐free reasoning will lead to further capabilities in AI that address currently intractable problems. 21 , 22

Advances in the computational capability of AI have prompted concerns that AI technologies will eventually replace physicians. The term “augmented intelligence,” coined by W.R. Ashby in the 1950s, 23 may be a more apt description of the future interplay among data, computation, and healthcare providers and perhaps a better definition for the abbreviation “AI” in healthcare. A version of augmented intelligence, described in the literature in Friedman’s fundamental theorem of biomedical informatics, 24 relates strongly to the role of AI in health care (depicted in Figure 1 ). Consistent with Friedman’s description of augmented intelligence, Langlotz at Stanford stated that “Radiologists who use AI will replace radiologists who don’t.” 25

A version of the Friedman’s fundamental theorem of informatics describing the impact of augmented intelligence. “The healthcare system with AI will be better than the healthcare system without it.” AI, artificial intelligence.

An AI system exhibits four main characteristics that allow us to perceive it as cognitive: understanding, reasoning, learning, and empowering. 26 An AI system understands by reading, processing, and interpreting the available structured and unstructured data at enormous scale and volume. An AI system reasons by understanding entities and relationships, drawing connections, proposing hypotheses, deriving inferences, and evaluating evidence that allows it to recognize and interpret the language of health and medicine. An AI system learns from human experts and real‐world cases by collecting feedback, learning from outcomes at all levels and granularities of the system, and continuing to improve over time and experience. An AI system empowers and interacts clinicians and users by providing a more integrated experience in a variety of settings, combining dialog, visualization, collaboration, and delivering previously invisible data and knowledge into actionable insights. In contrast, humans excel at common sense, empathy, morals, and creativity.

Augmenting human capabilities with those provided by AI leads to actionable insights in areas such as oncology, 27 imaging, 28 and primary care. 29 For example, a breast cancer predicting algorithm, trained on 38,444 mammogram images from 9,611 women, was the first to combine imaging and EHR data with associated health records. This algorithm was able to predict biopsy malignancy and differentiate between normal and abnormal screening results. The algorithm can be applied to assess breast cancer at a level comparable to radiologists, as well has having the potential to substantially reduce missed diagnoses of breast cancer. 30 This combined machine‐learning and deep‐learning model trained on a dataset of linked mammograms and health records may assist radiologists in the detection of breast cancer as a second reader.

Precision medicine

The field of precision medicine is similarly experiencing rapid growth. Precision medicine is perhaps best described as a health care movement involving what the National Research Council initially called the development of “a New Taxonomy of human disease based on molecular biology,” or a revolution in health care triggered by knowledge gained from sequencing the human genome. 31 The field has since evolved to recognize how the intersection of multi‐omic data combined with medical history, social/behavioral determinants, and environmental knowledge precisely characterizes health states, disease states, and therapeutic options for affected individuals. 32 For the remainder of this paper, we will use the term precision medicine to describe the health care philosophy and research agenda described above, and the term personalized care to reflect the impact of that philosophy on the individual receiving care.

Precision medicine offers healthcare providers the ability to discover and present information that either validates or alters the trajectory of a medical decision from one that is based on the evidence for the average patient, to one that is based upon individual’s unique characteristics. It facilitates a clinician’s delivery of care personalized for each patient. Precision medicine discovery empowers possibilities that would otherwise have been unrealized.

Advances in precision medicine manifest into tangible benefits, such as early detection of disease 33 and designing personalized treatments are becoming more commonplace in health care. 34 The power of precision medicine to personalize care is enabled by several data collection and analytics technologies. In particular, the convergence of high‐throughput genotyping and global adoption of EHRs gives scientists an unprecedented opportunity to derive new phenotypes from real‐world clinical and biomarker data. These phenotypes, combined with knowledge from the EHR, may validate the need for additional treatments or may improve diagnoses of disease variants.

Perhaps the most well‐studied impact of precision medicine on health care today is genotype‐guided treatment. Clinicians have used genotype information as a guideline to help determine the correct dose of warfarin. 35 The Clinical Pharmacogenetics Implementation Consortium published genotype‐based drug guidelines to help clinicians optimize drug therapies with genetic test results. 36 Genomic profiling of tumors can inform targeted therapy plans for patients with breast or lung cancer. 34 Precision medicine, integrated into healthcare, has the potential to yield more precise diagnoses, predict disease risk before symptoms occur, and design customized treatment plans that maximize safety and efficiency. The trend toward enabling the use of precision medicine by establishing data repositories is not restricted to the United States; examples from Biobanks in many countries, such as the UK Biobank, 37 BioBank Japan, 38 and Australian Genomics Health Alliance 39 demonstrate the power of changing attitudes toward precision medicine on a global scale.

Although there is much promise for AI and precision medicine, more work still needs to be done to test, validate, and change treatment practices. Researchers face challenges of adopting unified data formats (e.g., Fast Healthcare Interoperability Resources), obtaining sufficient and high quality labeled data for training algorithms, and addressing regulatory, privacy, and sociocultural requirements.

Future Synergies Between AI and Precision Medicine

AI and precision medicine are converging to assist in solving the most complex problems in personalized care. Figure 2 depicts five examples of personalized healthcare dogma that are inherently challenging but potentially amenable to progress using AI. 40 , 41 , 42

Dimensions of synergy between AI and precision medicine. Both precision medicine and artificial intelligence (AI) techniques impact the goal of personalizing care in five ways: therapy planning using clincal, genomic or social and behavioral determinants of health, and risk prediction/diagnosis, using genomic or other variables.

Genomic considerations in therapy planning: Patients with pharmacogenomically actionable variants may require altered prescribing or dosing

Genome‐informed prescribing is perhaps one of the first areas to demonstrate the power of precision medicine at scale. 43 However, the ability to make real‐time recommendations hinges on developing machine‐learning algorithms to predict which patients are likely to need a medication for which genomic information. The key to personalizing medications and dosages is to genotype those patients before that information is needed. 44

This use case was among the earliest examples of the convergence between AI and precision medicine, as AI techniques have proven useful for efficient and high‐throughput genome interpretation. 45 As noted recently by Zou and colleagues, 46 deep learning has been used to combine knowledge from the scientific literature with findings from sequencing to propose 3D protein configurations, identify transcription start sites, model regulatory elements, and predict gene expression from genotype data. These interpretations are foundational to identifying links among genomic variation and disease presentation, therapeutic success, and prognosis.

In medulloblastoma, the emergence of discrete molecular subgroups of the disease following AI‐mediated analysis of hundreds of exomes, has facilitated the administration of the right treatment, at the right dosage, to the right cohort of pediatric patients. 47 Although conventional treatment of this disease involved multimodal treatment, including surgery, chemotherapy, and whole brain radiation, precision genomics has enabled treatment of the “wingless” tumor subgroup, which is more common in children, with chemotherapy alone—obviating the need for radiation. 48 Avoiding radiotherapy is particularly impactful for mitigating potential neurocognitive sequelae and secondary cancers from whole‐brain radiation among disease survivors. 49 , 50

The initial successful paradigm of AI in imaging recognition has also given rise to radiogenomics. Radiogenomics, as a novel precision medicine research field, focuses on establishing associations between cancer imaging features and gene expression to predict a patient’s risk of developing toxicity following radiotherapy. 51 , 52 , 53 For example, Chang et al . proposed a framework of multiple residual convolutional neural networks to noninvasively predict isocitrate dehydrogenase genotype in grades II–IV glioma using multi‐institutional magnetic resonance imaging datasets. Besides, AI has been used in discovering radiogenomic associations in breast cancer, 52 liver cancer, 54 and colorectal cancer. 53 Currently, limited data availability remains the most formidable challenge for AI radiogenomics. 51

Knowing the response to therapy can help clinicians choose the right treatment plan. AI demonstrates potential applications in this area. For example, McDonald et al . trained a support vector machine using patients’ gene expression data to predict their response to chemotherapy. Their data show encouraging outcomes across multiple drugs. 55 Sadanandam et al . proposed approaches of discovering patterns in gene sequences or molecular signatures that are associated with better outcomes following nontraditional treatment. Their findings may assist clinicians in selecting a treatment that is most likely to be effective. 56 Although tremendous progress has been made using AI techniques and genomics to predict treatment outcome, more prospective and retrospective clinical research and clinical studies still need to be conducted to generate the data that can then train the algorithms.

Environmental considerations in therapy planning: A patient’s zip code should not impact care quality and availability

Incorporating environmental considerations into management plans require sufficient personal and environmental information, which may affect a patient’s risk for a poor outcome, knowledge about care alternatives, and conditions under which each alternative may be optimal.

One such example has been the challenge of identifying homelessness in some patients. 57 , 58 , 59 These patients may require care in varying locations over a short period, requiring frequent reassessments of patient demographic data. Related issues, such as transportation, providing medications that require refrigeration, or using diagnostic modalities that require electricity (for monitoring), need to be modified accordingly.

Another environmental consideration is the availability of expertise in remote locations, including the availability of trained professionals at the point of need. AI has provided numerous examples of augmenting diagnostic capabilities in resource‐poor locations, which may translate into better patient classification and therefore more personalized therapy planning. Examples include the use of deep learning to identify patients with malaria 60 and cervical cancer, 61 as well as predicting infectious disease outbreaks, 62 environmental toxin exposure, 63 and allergen load. 64

Clinical considerations in therapy planning: Co‐morbidities are always in play and AI can assist stratification

Finally, in addition to genomic considerations and social determinants of health, clinical factors are imperative to successful therapy planning. Age, co‐morbidities, and organ function in particular predicate treatment considerations and AI has emerged as a central pillar in stratifying patients for therapy. In one study, machine learning classifiers were used to analyze 30 co‐morbidities to identify critically ill patients who will require prolonged mechanical ventilation and tracheostomy placement. 65 Other studies have used AI algorithms to analyze bedside monitored adverse events and other clinical parameters to predict organ dysfunction and failure. 66 , 67

Genomic considerations in risk prediction or diagnosis: Patients with genome‐validated risk for disease may warrant different preventive care strategies

Actress Angelina Jolie’s response to her inheritance of the BRCA gene illustrates the potential impact of more advanced genomic information on disease risk and prevention options. 68 This case is not novel; the case of Woodie Guthrie and Huntington’s disease disclosed a similar conundrum for health care. 69 Although the ethics of genetic testing without a clear cure continues to be debated, the broad availability of genetic information offered by next‐generation sequencing and direct‐to‐consumer testing renders personalized prevention and management of serious diseases a reality. 70

Cardiovascular medicine is an area with a long history of embracing predictive modeling to assess patient risk. 71 Recent work has uncovered methods to predict heart failure 72 and other serious cardiac events in asymptomatic individuals. 73 When combined with personalized prevention strategies, 74 , 75 these models may positively impact disease incidence and sequela. Complex diseases, such as cardiovascular disease, often involve the interplay among gender, genetic, lifestyle, and environmental factors. Integrating these attributes requires attention to the heterogeneity of the data. 76 AI approaches that excel at discovering complex relationships among a large number of factors provide such opportunities. A study from Vanderbilt demonstrated early examples of combining EHR and genetic data, with positive results in cardiovascular disease prediction. 77 AI‐enabled recognition of phenotype features through EHR or images and matching those features with genetic variants may allow faster genetic disease diagnosis. 78 For example, accurate and fast diagnosis for seriously ill infants that have a suspected genetic disease can be attained by using rapid whole‐genome sequencing and NLP‐enabled automated phenotyping. 79

Nongenomic considerations in risk prediction or diagnosis: Patients with altered speech or gait patterns might be at risk for depression

Automated speech analytics have benefited from improvements in the technical performance of NLP, understanding, and generation. Automated speech analytics may provide indicators for assessment and detection of early‐stage dementia, minor cognitive impairment, Parkinson’s disease, and other mental disorders. 80 , 81 , 82 , 83 Efforts also are underway to detect changes in mental health using smartphone sensors. 84

AI‐assisted monitoring may also be used in real‐time to assess the risk of intrapartum stress during labor, guiding the decision of cesarean section vs. normal vaginal deliveries, in an effort to decrease perinatal complications and stillbirths. 85 This exemplifies real‐time AI‐assisted monitoring of streaming data to reduce manual error associated with human interpretation of cardiotocography data during childbirth.

AI is also being used in the detection and characterization of polyps in colonoscopy. 86 Wider adaptation of AI during endoscopy may lead to a higher rate of benign adenoma detection and reduction of cost and risk for unwarranted polypectomy. 87 AI‐mediated image analysis aimed at improving disease risk prediction and diagnosis will likely continue to increase in use for detection of diabetic retinopathy 88 and metastasis in cancer, 89 as well as for identification of benign melanoma. 90 AI‐based image analysis has become a part of a direct‐to‐consumer diagnostic tool for anemia as well. 91

The widespread use of home monitoring and wearable devices has long been accompanied by the expectation that collected data could help detect disease at an earlier state. Indeed, these advances have fueled new, noninvasive, wearable applications for monitoring and detecting specific health conditions, such as diabetes, epilepsy, pain management, Parkinson’s disease, cardiovascular disease, sleep disorders, and obesity. 92 Digital biomarkers are expected to facilitate remote disease monitoring outside of the physical confines of a hospital setting and can support decentralized clinical trials. 93 Wearable tools that provide continuous multidimensional measurements of preselected biomarkers would enable the detection of minimum residual disease and monitor disease progression. 94 In the field of cancer care, evolving technology using wearable devices continuously analyzes circulating tumor cells to screen for relapsed disease. 95

Ongoing Challenges Using AI in Precision Medicine

We have observed increasing efforts to implement AI in precision medicine to perform tasks such as disease diagnosis, predicting risk, and treatment response. Although most of these studies showed promising experimental results, how AI improved health care is not fully demonstrated. In reality, the success of transforming an AI system to a real‐world application not only depends on the accuracy but also relies on the capability of working accurately in a reliable, safe, and generalizable manner. 5 For example, the difference among institutions in coding definitions, report formats, or cohort diversity, may result in a model trained using one‐site data to not work well in another site ( https://www.bmj.com/content/368/bmj.l6927 ). Here, we highlighted three main challenges that would impact the success of transitions to real‐world healthcare.

• Fairness and bias. The health data can be biased while building and processing the dataset (e.g., a lack of diverse sampling, missing values, and imputation methods; https://datasociety.net/library/fairness‐in‐precision‐medicine/ ). An AI model trained on the data might amplify the bias and make nonfavorable decisions toward a particular group of people characterized by age, gender, race, geographic, or economic level. Such unconscious bias may harm clinical applicability and health quality. Thus, it is crucial to detect and mitigate the bias in data and models. Some potential solutions include improving the diversity of the data, such as the All of Us program that aimed to recruit participants with diverse backgrounds. AI communities also proposed several techniques to fight against bias ( https://arxiv.org/abs/1908.09635 ). IBM has developed an online toolkit (AI Fairness 360) that implemented a comprehensive set of fairness metrics to help researchers examine the bias among datasets and models, and algorithms to mitigate bias in classifiers ( https://doi.org/10.1147/JRD.2019.2942287 ). However, fairness and protected attributes are closely related to the domain context and applications. More work is needed in biomedical research to define and explore the fairness and bias in AI models trained with historical patient data. To address the challenge, a collaborative effort that involves the AI and biomedical community is needed.
• Socio‐environmental factors. The environmental factors and workflows where the AI model would be deployed may impact model performance and clinical efficacy. A recent prospective study carried out by Google Health evaluated an AI system for screening diabetic retinopathy in a real clinical environment. The AI system was developed to augment diabetic retinopathy screening by providing in‐time assessment, before this the process may take several weeks. Despite a specialist‐level accuracy (> 90% sensitivity and specificity) achieved on retrospective patient data; however, the system has undergone unexpected challenges when applied to Thailand clinics ( https://doi.org/10.1145/3313831.3376718 ). For example, the variety of conditions and workflows in clinics impaired the quality of the images that did not meet the system'’ high standards, resulting in a high rejection rate of images. The unstable internet connection restricted the processing speed of the AI models and caused a longer waiting time for the patients. Travel and travel costs may deter participants from remaining in the study. Such prospective studies highlighted the importance of validating the AI models in the clinical environment and considering an iteration loop—that collects users’ feedback as new input for learning and system improvement 96 before applying the AI system widely. Of note, in healthcare, obtaining such feedback would take a long time at a high cost. It may take a longer time to evaluate a therapy’s effect and associated long‐term health outcomes than what is required to validate whether a product is appealing to a customer. There is a need to explore other ways to facilitate creation of high‐performing AI systems, for example, generating synthetic data that carries similar distributions and variances as the real‐world data, or leveraging a simulated environment. Early examples by groups, such as Baowaly and colleagues, 89 demonstrate much promise, but more AI research efforts are needed.
• Data safety and privacy. Data is crucial to an AI‐driven system. As AI and precision medicine are converging, data (e.g., genomics, medical history, behaviors, and social data that covers peoples’ daily lives) will be increasingly collected and integrated. Individuals’ concerns for data privacy are closely related to trust when they use AI‐enabled services. Building a safe and well‐controlled ecosystem for data storage, management, and sharing is essential, requiring new technology adoptions, and collaborations, as well as the creation of new regulations and business models.

The training of AI methods and validation of AI models using large data sets prior to applying the methods to personal data may address many of the challenges facing precision medicine today. The cited examples reinforce the importance of another potential use of augmented intelligence, namely that of the role of technology in the hands of consumers to help communicate “just‐in‐time” risk or as an agent of behavior change. Although most studies to date are small and the data are limited, the ability to identify at‐risk patients will translate into personalized care when identification is combined with strategies to notify and intervene. Researchers are actively pursuing the use of mobile apps, wearables, voice assistants, and other technology to create person‐specific interfaces to intelligent systems. A review of these approaches is beyond the scope of this paper.

Active research in both AI and precision medicine is demonstrating a future where health‐related tasks of both medical professionals and consumers are augmented with highly personalized medical diagnostic and therapeutic information. The synergy between these two forces and their impact on the healthcare system aligns with the ultimate goal of prevention and early detection of diseases affecting the individual, which could ultimately decrease the disease burden for the public at large, and, therefore, the cost of preventable health care for all.

This work was funded by a partnership between IBM Watson Health and Vanderbilt University Medical Center.

Conflict of Interest

Drs. Weeraratne, Rhee, and Snowdon are employed by IBM Watson Health. All other authors declared no competing interests for this work.

Acknowledgment

The authors thank Karlis Draulis for his assistance with the figures.

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• Published: 04 January 2023

Precision medicine: affording the successes of science

• Christine Y. Lu 1 ,
• Vera Terry 2 &
• David M. Thomas   ORCID: orcid.org/0000-0002-2527-5428 3  

npj Precision Oncology volume  7 , Article number:  3 ( 2023 ) Cite this article

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• Drug development
• Molecular medicine

Science has made remarkable advances in understanding the molecular basis of disease, generating new and effective rationally-designed treatments at an accelerating rate. Ironically, the successes of science is creating a crisis in the affordability of equitable health care. The COVID-19 pandemic underscores both the value of science in health care, and the apparently inevitable tension between health and the economy. Drug development in ever-smaller target populations is a critical component of the rising costs of care. For structural and historical reasons, drug development is inefficient and poorly integrated across the public and private sectors. We postulate an alternative, integrated model in which governments and industry share the risks and benefits of drug development. The Australian government recently announced support for a AU$185 million innovative multi-stakeholder public-private partnership model for sustainable precision oncology, accelerating biomarker-dependent drug development through integrating clinical trials into the standard of care.

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Introduction

Scientific advances in understanding the molecular basis of diseases are generating new and effective rationally-designed treatments that promise to improve health outcomes. However, health systems face increasing challenges to the sustainability of equitable health care. Even prior to the COVID-19 pandemic, health expenditure as a fraction of gross domestic product (GDP) has been rising year-on-year in all higher-income countries, reaching 17% in the US and 10.2% in Australia in 2019. Costs are rising in part due to the demand for health care by growing and ageing populations. Drug development is a major contributor to health costs – the average cost of getting a new drug into the market is estimated at US$1.3 billion 1 . Countries such as the US, the UK, and Australia are tackling the affordability crisis by engaging in risk-sharing agreements with industry (also called managed entry agreements, patient access schemes, among other terms) 2 . Globally, governments and industry recognize that fundamental changes are essential to help address the sustainability of health care.

This issue is particularly pressing for cancer, the leading cause of death in higher-income countries. Fundamentally a genetic disease, cancer treatment is being radically transformed by the twin influences of genomic technologies and rational drug development. By 2018, biomarker-dependent drugs accounted for 42% of approvals by the US Food and Drug Administration (FDA) in 2018, a doubling from 2014 3 . The widening gap between the potential of science to improve health, and its affordability, is exemplified by non-small cell lung cancer, the leading cause of cancer deaths world-wide. There are now 10 molecularly distinct subtypes of non-small cell lung cancer for which there are effective therapies, accounting for more than 50% of all affected patients 4 . However, only 3 of these therapies are publicly reimbursed in Australia. The Australian Pharmaceutical Benefits Advisory Committee considers five major factors in their recommendations for new drug coverage and reimbursement 5 . Clinical impact comprises only one of these five factors, with the remainder concerning cost-effectiveness and budgetary impact.

In March 2022, the Australian government announced support for a radical approach to solving this problem, the establishment of a AU$185 million (US$130 million) innovative multi-stakeholder public-private joint venture for precision oncology 6 , whose conceptual framework is outlined in this article. We explore the current inefficiencies in drug development in single-payer health systems (exemplified by Canada, Denmark, Norway, Australia, Taiwan and Sweden) due to the often adversarial relationship between governments and industry. Further, we propose that affordable patient access to health care is achievable through collaborative engagement between governments and industry in drug development. Although focused on precision oncology, the ideas outlined here are broadly relevant to the sustainability of science-led transformations in health care.

Drug development: challenges and opportunities

Public and private sector engagement in drug development can be conceptualized in three distinct stages along the value chain 7 (Fig. 1 ). Stage 1 involves discovery research – predominantly funded by government and philanthropy, and undertaken in the public sector. Basic medical research generates intellectual property, which is licensed or sold by academic institutions to industry, partially recouping the costs of research. Stage 2 involves the industry pursuing priority drug targets with a focus on lead compound identification and medicinal chemistry, followed by clinical trials (phases 1 to 3). Industry funds the health sector to conduct trials in increasingly biomarker-selected populations, often within hospitals. The failure rate is high, with 15-35% of drugs reaching phase 2 trials ultimately obtaining regulatory approval 1 . This figure is lower for oncology at 6.7%. In stage 3, new drugs may become a reimbursed standard of care after policymakers review for clinical utility and cost-effectiveness in certain jurisdictions. In single-payer health systems, the public sector pays industry for these drugs, with the price in reflecting the total costs of drug development (including those that fail), as well as maximizing commercial returns.

Red arrows depict investments; green arrows depict returns; blue arrows indicate exchanges of value; yellow arrows indicate information transfer. Sizes of arrows represent the relative magnitude of monetary value transfer.

This model is characterized by siloing of each stage from the other, and the use of money as the unit of value transfer. Each partner pays for the services or goods with the goal of maximizing monetary gains within each stage, and the cumulative cost rises as drug development proceeds from stage 1 to stage 3. Considering the interdependent nature of drug development, we contend that the lack of integration between health sector and industry contributes to inefficiencies that increase the net cost.

Stage 2, focussing on clinical trials, is arguably where the greatest interdependence between the public and private sectors lies. Much of the total cost of drug development is due to clinical trials 8 , which are necessarily conducted by industry in the context of health systems. Through increasingly complex regulatory and governance processes, health systems contribute to trials inefficiency and costs 9 , 10 , 11 , 12 . Clinical trials are not considered core business for most health systems. For example, only 8% of adult cancer patients participate in trials in Australia 13 . While money is the major unit of value exchange between industry and health systems, each party has additional assets of mutual interest that could form an alternative basis of value exchange. The health system’s main assets are the patients required for clinical trials and the data that is a natural byproduct of health care delivery. The value of this data is currently largely unrealized, although it constitutes the raw substrate for rational drug development and health technology assessment. Industry expends billions on the creation of ‘real-world data’ assets 14 , exemplified by the market capitalization of entities like Tempus and Flatiron Health 15 , 16 . Ironically, industry must ultimately recoup the additional costs of generating real-world data from the health system as part of the total costs of drug development.

Industry’s main asset of interest to the health system are the drugs and other technologies it produces to improve health outcomes, currently mostly accessible universally only in Stage 3. For industry, the costs of manufacturing drugs are a fraction of the total costs of trials (patient screening, consent, data collection). Nonethless, increasing participation in biomarker-dependent clinical trials would accelerate drug development.

Can we conceive a public-private partnership in which the health system actively supports clinical trials as a standard of care, effectively realizing the value of information in exchange for early drug access, while increasing the overall efficiency and decreasing the costs of drug development?

Clinical trials: a new standard of care?

These concepts have clear application in oncology, where advances in genomics have radically accelerated drug development. It is estimated that, of more than 840 oncology drugs in development in 2018, >90% are biomarker-dependent 17 . A biomarker, as defined by FDA and the National Institutes of Health, is a “characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions” 18 . As noted above, clinical trials are not a core part of standard oncology practice. In the 1990s, a phase 1 study of a new chemotherapy offered a low response rate (e.g., 5%) with unquantifiable risk of toxicities. The situation is changing rapidly. Participation in a phase 1 study of a rationally-designed drug directed at a matched biomarker now offers a response rate in excess of 30% 19 . Consider doxorubicin, a standard of care for a patient with newly diagnosed metastatic sarcoma, with response rates of between 10% and 30% 20 , 21 , 22 . For patients carrying the relevant biomarker, a phase 1 study today appears to offer a better chance of response than the standard of care.

It is important to note that trials-based therapies carry inherently greater uncertainty than standard-of-care treatment, which should be considered in the following proposed model. For this reason, we have focused on the terminal stages of a cancer patient journey, where most patients run out of standard-of-care options. In these circumstances of unmet medical needs, clinical trials of biomarker-dependent therapies offer additional important options for patients who remain fit enough for treatment. Further, we anticipate that trial drugs may have roles even at earlier stages of the cancer journey, where standard-of-care treatments exist. Participation ethically approved clinical trials randomizing new against standard-of-care cancer treatments could offer access to promising new therapies. Given the uncertainty attached to the clinical benefits of participation in clinical trials, it will be important to monitor clinical outcomes prospectively as part of the proposed model.

Enhanced efficiency and reduced costs of biomarker-dependent drug development

If the principle of patient benefit is accepted, does increased access to clinical trials make economic sense? The rising costs of new drugs has fundamentally changed the answer to this question. Let us assume that the health system bears standard-of-care treatment cost of $60,000 per patient per year, such that the total cost to the health system is $600,000 for 10 patients treated (Fig. 2a ). If 5 of 10 patients access a drug provided for free by an industry partner through a trial, this provides a treatment cost-offset of $300,000 to the health system compared to standard-of-care drug access (Fig. 2b ). Effectively, a fraction of the government pharmaceutical budget could be partially re-purposed to support the expansion of trials as a standard of care. Even if the health system was to invest an additional $5,000 per patient to support genomic screening and participation in clinical trials and cost per patient treated is $23,300, the system would still save $36,700 per patient assuming an additional 5 people would enrol and receive treatment through trials (Fig. 2c ; compared to Fig. 2a , cost per patient treated is $60,000). The more people access therapies via clinical trials, the better for the economic benefit to the health system. To incentivize industry to conduct trials, the health system needs to contribute to making trials more efficient. For industry, an efficient increase in clinical trials participation reduces the length of time to conduct trials and associated costs.

©[Leremy Gan] via Canva.com. a : current standard of care treatment; b : clinical trials-based care; c : expanded clinical trials supported by population-level screening.

For oncology drug development, health system-industry collaboration offers many opportunities for efficiencies and cost savings. Oncology drugs increasingly target biomarkers, often in the form of mutations detectable by genomic screening. Cancers are being increasingly subdivided therapeutically according to these biomarkers, generating the need to identify specific subpopulations carrying the cognate therapeutic biomarker for clinical trials. The cost of screening large numbers of patients to find these subgroups adds to the costs of drug development. A relevant drug target may be present in fewer than 1% of the general population, meaning that 100 people need to be screened to find one eligible participant. Currently, industry bears the costs of identifying such patients for clinical trials through diagnostic screening tests, almost invariably focusing on a single gene target, conducted separately for each trial and participating institution. Typically these single gene tests reflect the regulatory bodies’ requirement that a purpose-built companion diagnostic be approved with the therapy – a co-dependent technology.

The advent of comprehensive genomic profiling (CGP) can radically transform the efficiency of identifying subpopulations for clinical trials. CGP enables the screening of hundreds of potential drug targets in a single assay 23 , 24 , such that one test could be used to triage patients for dozens of trials. Realising the efficiencies of this approach requires the shift from trial-specific single gene testing, to CGP screening of patient populations on behalf of multiple trials.

To illustrate this, consider the following hypothetical example of 10 companies intending to perform 10 trials (Fig. 3 ). Each trial is dependent on screening for a distinct biomarker present in the population at a 1% frequency. Each trial needs to screen 2,000 patients to identify 20 patients with the relevant biomarker (assuming for the purposes of argument 100% enrolment of suitable candidates onto each trial). Using a diagnostic screening test that costs $500 per individual, a 20-patient trial requires a $1 million screening budget (Fig. 3a ). For a trial that costs $1 million to run (20 patients x $50,000 per patient enrolled), screening may account for half of the total cost. Collectively, 10 trials need to screen 20,000 patients using 10 biomarker-specific and purpose-built tests, with total screening expenditure of $10 million.

©[Pixeden] via Canva.com.

However, if the 10 biomarkers for these trials are mutually exclusive, then in principle only 2000 patients need to be screened to support all 10 trials through a single CGP test (Fig. 3b ). If a CGP test costs $2500 per individual, screening 2000 individuals would cost $5 million to support 10 trials. Alternatively, using the original $10 million budget for all 10 trials, 4000 patients could be screened, identifying those patients twice as fast, thereby cutting the time to trial completion by half. In summary, the reduced costs of identifying eligible patients, and the acceleration of trials completion, are the twin drivers for decreasing the costs of drug development. Furthermore, CGP cost is declining, making population-level screening for guiding drug treatments more feasible.

Screening for 10 trials involving multiple industry partners, requires an ‘honest broker’ operating on behalf of all parties, with access to the patient populations to be screened. For these reasons, health systems, either directly or via an ‘honest broker’, are ideally placed to undertake biomarker screening in partnership with industry. The funding for CGP tests may be cost-efficient for health systems as patients might shift from system-reimbursed therapy to trial-based therapy.

The collaborative model has an important additional benefit by expanding screening from sites where trials are conducted, to a much larger population across the entire health system (Fig. 4 ). Screening is limited to trial sites in the traditional model. The sponsor needs to open more trial sites to maximise the population to be screened because of the patient catchment area of the institution. If sites do not routinely undertake screening, this is funded by each trial. Opening each trial site adds costs and time related to governance and monitoring complexities. In contrast, the collaborative model would need fewer trial sites since the number of trial sites is predicated on the site trial capacity, not its patient catchment. To illustrate this point, for rare cancer populations, trials may not be feasible if the sponsor opens trial sites at 12 institutions to identify 22 patients for a trial, missing the opportunity of recruiting 28 people who are outside the trial sites (Fig. 4a ). On the other hand, all 50 people could be identified by an independent population-based screening program in the collaborative model, who are referred to only four sites opened for trial conduct, increasing trial efficiency (Fig. 4b ). This increases the clinical experience at each site, and the contribution of site investigators in answering the trial question. It also increases the system capacity for trials in total, by distributing the burden of trial conduct across a broader range of treating centres, whereas currently the burden of trials falls asymmetrically on high-volume centres, whose capacity may be saturated. Finally, it increases clinical trials engagement of centres which might otherwise not participate due to their patient volumes. Some of the cost savings outlined above could be reallocated to subsidies to support patient travel across a broader network of trial centres.

a Screening and trial sites for clinical trials by model. The numbers above each institution represent eligible patients for a biomarker dependent trial. b Differences in efficiency for trial recruitment between models. ©[Visual Generation, Pixeden] via Canva.com.

Importantly, the collaborative model will lead to better health outcomes for patients overall, for several reasons: (1) the total number of patients receiving biomarker-dependent therapies should increase compared with the existing model, due to enhanced access to trials; (2) the greater efficiency of CGP screening means that a greater fraction of patients carrying the relevant treatable biomarker will be identified than is currently the case; and (3) the greater speed and efficiency of trials conduct will reduce the net costs of drug development. In short, the greater the number of trials, the greater the amortization of the costs of CGP screening (or the greater the numbers of patients that can be screened with the same resource).

Structural considerations

The interface between health systems and industry must take into account several considerations. Health systems are not well-designed for direct engagement with industry. The entrepreneurial approach important to a successful partnership with industry is not a common feature of bureaucracies. To date, precision medicine has typically been funded using relatively short-term, project-based research funding, which are usually limited in scale and do not consider long-term sustainability. Moreover, there are good reasons to retain some degree of independence of government from industry, recognizing the competing interests between the economic, health, citizen protection and regulatory functions of government in drug development. Finally, in some health systems, there is variable alignment between federal and state or provincial governments. In Australia, although precision medicine constitutes a matter of national interest, it is delivered through hospital networks primarily funded by state/territory governments. A similar model also exists in Canada. Integrating the interests and roles of both federal and state/territory governments has been a structural impediment to nationally consistent health care.

A solution to these requirements is being tested in Australia is the creation of a non-profit company (Omico) with both state and Federal government funding, which has developed a joint venture framework for co-investment with industry in a national precision oncology platform 6 . Omico provides a stable, national ‘honest broker’ role on behalf of the health system with the cultural and legal freedom to develop innovative, collaborative engagement between industry and the health system. In practice, Omico commissions biomarker screening for referred patients, and returns a report to the referring clinician, who then decides whether the recommended clinical trial is appropriate for their patient. We note that the proposed collaborative model has application beyond cancer to all health conditions relying on biomarker-dependent drug development (e.g., cardiovascular, renal, endocrine).

Economic benefits

In addition to better health care, there are economic benefits to greater engagement between the public sector and industry. Health is typically seen as an ‘expenditure’ portfolio for the government. The pharmaceutical industry generates revenue in excess of US$1.25 trillion globally, growing at 5% per annum 25 . For a population of 25 million, the Australian pharmaceutical industry contributes more than AU$8.9 billion (US$6.3 billion) annually to the economy, and supports almost 23,000 full-time jobs 26 . Cancer clinical trials alone contribute over AU$1 billion (US$0.7 billion) annually to the Australian economy, supporting nearly 7,000 highly skilled jobs 26 . Investments in the life sciences sector generates economic growth, jobs in education, training, infrastructure and support roles, stimulating commercialization of medical research through accelerated local biomarker-dependent drug development, and enhancing greater engagement with global pharmaceutical industry. These outcomes constitute strategic goals common to the health system and economy, the academic sector, and industry.

There are obvious challenges to the success of public-private partnerships that include social and legal issues, political will, bureaucratic inertia, and legitimate competing interests. Social and legal issues relate to privacy concerns and commercialisation of health data. This is partly due to the perception of antagonistic interests of industry and citizens, and partly due to perceptions of research as irrelevant to health care. Disengagement of the public sector from industry leaves patients without access to drugs, and society with fewer options for economic growth. At the other extreme, a fully privatised healthcare model may compromise long-term societal value. Tempo also contributes to the challenges: the low risk appetite of health systems leads to structural and cultural conservativism, while research has an inherently greater risk appetite. The question is whether the risk appetite of health systems can be adjusted to benefit patients who are dying from treatable diseases.

Future directions

The model proposed ambitiously advocates for clinical trials as a standard of care. Due to the inherent clinical and economic uncertainties, we have proposed that it should be implemented initially for patients who have exhausted conventional treatment options. However, the model could apply at all stages of the cancer journey, where participation in randomised clinical trials could be a standard-of-care. This approach would require sufficient evidence from earlier phase testing to ethically justify randomisation between existing and new treatments. Biomarker screening is also evolving rapidly. Beyond comprehensive genomic panels, whole genome and transcriptome sequencing approaches are being evaluated, and offer potential advantages 27 . While routine pathology processing and costs make focused panels more practical currently, the technology is rapidly evolving and costs of whole genome sequencing are falling, and routine practice will likely adapt over time. One more general benefit of integration of research into standard-of-care is to adapt conservative health systems to align better with the accelerating pace of scientific development. Notably, there are therapeutic biomarkers beyond genomics (DNA and RNA), including proteomics and the microbiome, that are transforming cancer clinical research and care. At the system level, a data-driven approach is clearly important to optimising the integration of research into standard-of-care effectively, equitably, and sustainably.

The increasing faction of GDP due to health expenditure world-wide is driven by the inexhaustible social appetite for better health outcomes and the successes of science. Recent experience with COVID-19 and HIV have shown that science is critical to solving health crises in real-time, and reinforced the tension between health and the economy. The crisis in long-term affordability of health care is exacerbated by aging populations and relative diminution of taxpayer base funding health care. Two conclusions are clear: (1) the rate-limiting step in realising these outcomes is no longer scientific, but lies in our health system’s ability to integrate science into health care; and (2) the public and private sectors have complementary roles in delivering the benefits of science to mankind. Like climate change, health care is too important to society to be left either to the public or private sectors alone, and can only be addressed in partnership.

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Acknowledgements

We are grateful to Alyssa Halbisen, BS, Harvard Pilgrim Health Care Institute, for her administrative support. We also wish to thank our many colleagues who have contributed to discussions that gave rise to the ideas in this paper. This commentary did not receive funding.

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Christine Y. Lu

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Genomic Cancer Medicine Laboratory, Garvan Institute of Medical Research, Omico: Australian Genomic Cancer Medicine Centre Ltd, St Vincent’s Clinical School, Faculty of Medicine, UNSW, Sydney, Australia

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C.Y.L. developed the ideas and wrote the manuscript; V.T. developed the ideas and wrote the manuscript; D.M.T. conceived the ideas, provided funding, and wrote the manuscript.

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DMT is the CEO of Omico, a non-profit precision oncology program. He has received research support as well as honoraria and speakers bureau from Astra Zeneca, Roche, Pfizer, Novartis, Eisai, Beigene, Seattle Genetics, Janssen, Bayer, Microba, InterVenn, Merck. VT is deputy CEO of Omico. The remaining authors declare no competing interests.

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Lu, C.Y., Terry, V. & Thomas, D.M. Precision medicine: affording the successes of science. npj Precis. Onc. 7 , 3 (2023). https://doi.org/10.1038/s41698-022-00343-y

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Systems-Aligned Precision Medicine—Building an Evidence Base for Individuals Within Complex Systems

• 1 Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
• 2 Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
• 3 Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill

Advances in data collection and storage, computing, and analytics, combined with a growing recognition of the clinical importance of heterogeneity among individuals, are producing new solutions to clinical problems and a paradigmatic shift in evidence generation: precision medicine. The tagline for precision medicine—right treatment, right patient, right time—distills the operating principle of tailoring treatment decisions to individuals’ unique characteristics and evolving health. 1 - 3 While this goal is not new, what is novel is the growing capacity of analytic tools to generate data-driven strategies for increasing the precision of prevention and care. 2 , 4 Put otherwise, the engine of empirical precision medicine is advanced analytics, and the fuel is patient-level data.

A statistical approach to precision medicine uses patient-level data and formalizes clinical decision-making into a model composed of patients’ features, key decision points, treatment options at those decision points, and primary outcomes to be optimized. What is remarkable about statistical precision medicine algorithms, known as optimal dynamic treatment regimes , 3 is how they generate knowledge from data. Rather than relying on known or hypothesized relationships between interventions, health states, and outcomes, statistical and machine learning analytics can learn directly from the data how to tailor treatments optimally. The translational vision of precision medicine is to integrate optimal treatment recommendations into point-of-care tools that can offer real-time, tailored decision support.

Yet the question of what treatment works best for whom, to some degree, relies on broad and multilevel contextual factors. The clinical decision-making that happens as part of routine care, rather than in a research setting, is shaped by complexity at nearly every stage. Patient preferences, digitized workflows, billing and payment practices, and labor shortages can affect clinical decisions, and differences in resources, options for interdisciplinary care, and costs across care settings may restrict available treatment choices altogether. Furthermore, disparities in health care access, the social determinants of health (SDOH), and exposure to structural racism are formidable barriers that can prevent equitable access to optimal treatments, even if the underlying treatment rule could be statistically estimated. These factors extend far beyond the pared-down, formalized clinical decision-making models that underlie existing precision medicine algorithms.

An evidence base shaped by scientific methods that remove the variability in the actions, interactions, and environments of patients and clinicians alike, whether intentionally or unintentionally, will not address this real-world complexity. Health and health care decisions are shaped, and often constrained, by complex systems. 5 While we tend to focus on formal systems, such as governmental or health care systems, a system can be any set of interconnected elements embedded in a structure that interact to determine outcomes of interest. 6 What will work best for a given patient at a certain point in time is shaped by a web of patient factors (eg, symptoms, clinical history, preferences) in addition to clinician and institutional factors (eg, constrained time/resources) and societal factors (eg, access to care and differing SDOH-related health outcomes).

A new strategy for generating and evaluating evidence for precision medicine is needed—a strategy primed to translate data-driven individualized care strategies to be useful in complex systems. We call this strategy systems-aligned precision medicine , the goal of which is to deliver on the “right treatment, right patient, right time” tagline, but also to consider patients in their specific contexts.

Working at the systems level presents unique challenges. Problem-driven pragmatic solutions in precision medicine must thus start with defining the relevant system and its components. A systems-aligned approach then relies on broad and ongoing patient and other stakeholder engagement using structured participatory systems science methods to help untangle the clinical and nonclinical complexity relevant to a medical problem or population. This approach can illuminate diverse perspectives and priorities surrounding the potential interventions and identify constraints on decision-makers who represent the end users of future precision medicine decision support tools. 5 , 7 A deep understanding of the relevant system also will facilitate the generation of complexity-aware precision medicine problem statements and subsets of discrete, solvable questions contained therein to increase the usefulness of precision medicine evidence in practice. Grounding precision medicine interventions in their specific systems will require new adjustments to traditional study designs, collection of varied process and outcome measures, and methodological triangulation to embed a system lens into evidence generation and evaluation. 8 Additional quantitative modeling tools from the interdisciplinary field of systems science will be essential to design context-specific, data-driven clinical decision support algorithms.

The care of diabetes in older adults represents both the challenges and the opportunities for practicing a pipeline of systems-aligned precision medicine. One-quarter of US adults 65 years and older have diabetes, and their numbers are expected to increase as the population continues to age. 9 , 10 Older adults with diabetes are a heterogenous population; thus, diabetes care must be carefully tailored to individuals’ medical, functional, and cognitive status. 10 Moreover, the risk of hypoglycemia increases with age; severe events are associated with morbidity (eg, falls, fractures, hospital admissions) and mortality, so older adults often require adjustments of diabetes medications and self-management regimens to mitigate this risk over time. In the future, dynamic treatment regimens may offer valuable clinical decision support for diabetes care of older adults. These regimens could be designed to optimize the selection and dosing of medications, to identify the most appropriate and safe glycemic targets for patients, to match behavioral or technologic strategies to prevent hypoglycemia, and support adherence to broader care plans.

In addition to being tailored to older adults’ medical needs, however, precision medicine algorithms must also account for patients’ lived experiences and support networks. The system shaping diabetes outcomes for older adults includes many stakeholders (eg, patients, clinicians, payers, institutions), varied resources, evolving treatment options and best practices, and factors outside of the health care setting (eg, living situation, values, preferences, SDOH). Stakeholder-engaged system mapping with older adults and their caregivers may elucidate the interplay of diabetes self-care alongside the demands of managing other chronic diseases, constraints on treatment options regarding out-of-pocket costs and insurance rules, and challenges with navigating multiple specialty care experiences. To ensure that statistical models address the unique needs of different subpopulations, it is essential to include individuals who bring the greatest complexity, such as older adults with cognitive impairment or limited social support. The participatory methods applied with clinicians and health care institutions may further reveal the high-level patterns of how patients receive primary diabetes care, as well as the differing perspectives, roles, and responsibilities surrounding diabetes management in older adults across different settings. Understanding these dimensions of the larger system can elucidate opportunities to optimize patient-oriented dynamic treatment regimens that make sense when deployed across both primary care and specialty care settings and ensure the clinical decision support is utilitarian for diverse end users. Evaluation within and across settings can inform how differences in patient populations, systems-level resources, and clinical priorities shape whether, when, and how decision support can be used to tailor therapies for pragmatic and cost-effective health improvements among the population.

Precision medicine, to date, has been focused on individualized care decisions. A broader strategy of systems-aligned precision medicine is now needed, using patient data, stakeholder engagement, rigorous statistical methods, and an embedded awareness of complex systems to learn what is the right treatment for the right patient at the right time and in each patient’s unique context.

Published: July 29, 2022. doi:10.1001/jamahealthforum.2022.2334

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Kahkoska AR et al. JAMA Health Forum .

Corresponding Author: Anna Kahkoska, MD, PhD, 135 Dauer Dr, McGavran-Greenberg Hall 2205A, Chapel Hill, NC 27599 ( [email protected] ).

Conflict of Interest Disclosures: Dr Kahkoska reported a grant from the Diabetes Research Connection outside the submitted work. No other disclosures were reported.

Funding/Support: Dr Kahkoska was supported by a grant from the National Institutes of Health’s National Center for Advancing Translational Sciences (No. KL2TR002490).

Role of the Funder/Sponsor: The funder had no role in the preparation, review, approval, or submission of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Additional Contributions: We thank Michael R. Kosorok, PhD (University of North Carolina), for extensive input into the development of the concepts reflected in this essay and for providing feedback on multiple drafts. We also thank Laura A. Young, MD, PhD (University of North Carolina), for insights into diabetes care and management in older adults.

Additional Information: Dr Kahkoska and Ms Freeman are co−first authors.

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Kahkoska AR , Freeman NLB , Hassmiller Lich K. Systems-Aligned Precision Medicine—Building an Evidence Base for Individuals Within Complex Systems. JAMA Health Forum. 2022;3(7):e222334. doi:10.1001/jamahealthforum.2022.2334

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Nih…turning discovery into health ®, the promise of precision medicine.

Historically, doctors have had to make most recommendations about disease prevention and treatment based on the expected response of an average patient.  This one-size-fits-all approach works well for some patients and some conditions, but not so much for others. Precision medicine is an innovative approach that takes into account individual differences in patients’ genes, environments, and lifestyles.  Millions of people have already been touched by the area of precision medicine that has grown directly from biomedical research.

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Decades of NIH research has led to several types of cancer immunotherapy drugs.

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A significant goal of precision medicine is to implement this strategy broadly in medical care – focusing on the right drug at the right dose at the right time for the right patient.

Rare Diseases

Rare diseases were once considered medical curiosities with little public-health impact. But though such diseases are individually rare, collectively an estimated 25 to 30 million Americans are affected.

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Patients’ and professionals’ views related to ethical issues in precision medicine: a mixed research synthesis

• Anke Erdmann   ORCID: orcid.org/0000-0003-3708-8889 1 ,
• Christoph Rehmann-Sutter 2 &
• Claudia Bozzaro 1  

BMC Medical Ethics volume  22 , Article number:  116 ( 2021 ) Cite this article

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Precision medicine development is driven by the possibilities of next generation sequencing, information technology and artificial intelligence and thus, raises a number of ethical questions. Empirical studies have investigated such issues from the perspectives of health care professionals, researchers and patients. We synthesize the results from these studies in this review.

We used a systematic strategy to search, screen and assess the literature for eligibility related to our research question. The initial search for empirical studies in five data bases provided 665 different records and we selected 92 of these publications for inclusion in this review. Data were extracted in a spreadsheet and categorized into different topics representing the views on ethical issues in precision medicine.

Many patients and professionals expect high benefits from precision medicine and have a positive attitude towards it. However, patients and professionals also perceive some risks. Commonly perceived risks include: lack of evidence for accuracy of tests and efficacy of treatments; limited knowledge of patients, which makes informed consent more difficult; possible unavailability of access to precision medicine for underprivileged people and ethnic minorities; misuse of data by insurance companies and employers, potential of racial stigmatization due to genetic information; unwanted communication of incidental findings; changes in doctor-patient-relationship through focusing on data; and the problem that patients could feel under pressure to optimize their health.

Conclusions

National legislation and guidelines already minimize many risks associated with precision medicine. However, from our perspective some problems require more attention. Should hopes for precision medicine’s benefits be fulfilled, then the ethical principle of justice would require an unlimited access to precision medicine for all people. The potential for autonomous patients’ decisions must be greatly enhanced by improvements in patient education. Harm from test results must be avoided in any case by the highest possible data security level and communication guidelines. Changes in the doctor-patient relationship and the impact of precision medicine on the quality of life should be further investigated. Additionally, the cost-effectiveness of precision medicine should be further examined, in order to avoid malinvestment.

Peer Review reports

Precision medicine (PM) is a relatively new approach to individualize the prevention, diagnosis and treatment of various diseases. Since many diseases are caused by the interaction of genetic, lifestyle and environmental factors, and the outcome of treatments can also depend on genetic traits, the goal of an individualized therapy requires extensive data collection on the patient's genetic characteristics, lifestyle and environmental factors. Such an extensive data collection has become possible through digitization and the development of whole genome sequencing.

There are promising applications of PM, in particular for oncology, pharmacogenomics and various hereditary diseases [ 1 ]. However, PM is under development for many conditions. Since the authors are involved in a research consortium focused on the application of PM in chronic inflammatory diseases, we explain in the following the complexity of PM using the example of inflammatory bowel diseases (IBD).

Crohn's disease and ulcerative colitis appear to be the result of an interaction of the genome, exposome, microbiome and immunome. Genome-wide association studies have identified over 240 IBD susceptibility loci that potentially increase risk of disease. The studies present early life events, air pollution, smoking and diet as lifestyle and environmental factors. In addition, a reduced microbial diversity and a dysregulated immune response in the gut are also considered to be crucial factors in IBD development and progression [ 2 ].

A large number of treatment options are available for patients with IBD. PM pursues the goal of administering the right therapy to the right patient at the right time, while increasing the therapy response and reducing possible side effects. The reduction of side effects is particularly important in the therapy with thiopurines, as a NUDT15 gene variant can increase the risk of myelosuppression. The risk of pancreatitis as a result of treatment with thiopurines has also been described for certain genetic traits. To minimize the risk of side effects, therapeutic drug monitoring with biomarkers is required to optimize the drug dose [ 2 ].

Although some biomarkers are already available, the aim of future research is to develop further biomarkers in order to enable a personalized therapy based on "multi-omics data” [ 3 ] in IBD. This requires sharing data between research groups and the use of electronic health records (EHRs). To make sense of multiomics data, machine learning and algorithms will be necessary [ 2 ].

It is important to note that as precision medicine has evolved, other terms for this medical approach have emerged, such as personalized medicine , genomic medicine, systems medicine or individualized medicine . These terms emphasize different aspects: While the term genomic medicine emphasizes the use of gene sequencing technology, the term precision medicine fosters the, possibly unrealistic, expectation of a perfect fit to a patient outcome. In contrast, personalized medicine brings to the forefront the recognition that patients are more than their genes and interactions with the environment. They are influenced by their experiences, culture, education, and myriad other factors [ 4 ]. The concept of individualized medicine is quite similar to the concept of personalized medicine, as both emphasize the individual person. The term systems medicine , though, was derived from the theoretical concepts of systems biology and systems pharmacology and integrates these concepts into medical research and practice. Systems medicine accentuates the intensive collaboration between clinicians, biologists, pharmacologists, bioinformaticians and mathematicians, in which multidimensional sources of information are processed by computer modeling. Since this is also the case with precision medicine and personalized medicine, it seems that these concepts cannot be separated quite sharply. In fact, the terms precision medicine and personalized medicine have gained more popularity than the term systems medicine since 2000, as a simple search of the terms in the Pubmed database shows. The same analysis also reveals that the term precision medicine is used even more frequently than the term personalized medicine [ 5 ], which prompts us to mainly use the term precision medicine (PM) in this article.

Ethical issues in precision medicine

Many ethical challenges regarding PM have already been reported. In addition to ethical issues concerning the massive data storage and data sharing, these challenges include:

a possible discrimination by insurance companies and employers [ 6 ]

discrimination in access to PM [ 6 , 7 ]

incidental findings in genetic testing [ 8 ]

the lack of health literacy or “genetic literacy” for obtaining informed consent [ 9 ]

the lack of scientific evidence of the efficacy and tolerability of treatments [ 10 ]

the possibility of changing the patient-physician relationship by focusing on data [ 11 , 12 ]

and the increasing expectation on patients to contribute with data, time, effort and self-care [ 7 ].

Ethical issues in PM can be considered in light of various ethical theories. For our review, we choose Beachamps and Childress' common-sense based four-principles approach as a framework for our work, which includes beneficence, nonmaleficence, autonomy and justice [ 13 ]. These principles can be traced back to multiple ethical theories, e.g. the principle of autonomy to Mill’s Utilitarianism or to Kant’s Deontology, the principle of justice, for example, to the theories of Rawls [ 13 ]. But also other philosophers like Sen [ 14 ] or Nussbaum [ 15 ] have worked on justice. The principles of beneficence and nonmaleficence, both already contained in the Hippocratic Oath, have guided medical practice since antiquity [ 16 ]. Although respect for autonomy in multi-ethnic societies faces some challenges, as cultural, traditional or religious norms limit the autonomy of several groups [ 17 ], the principle of autonomy is also contained in the concept and declarations of human rights, which have been recognized by most nations. Here, the right to freedom is granted to all human beings [ 18 ] and the recognition of freedom is also the basis of the principle of autonomy in medicine [ 16 ]. In personalized medicine, the patient’s autonomy [ 19 ], justice [ 20 ] and nonmaleficence [ 21 ] provide a common framework for ethical reflection.

Objective and research question of this review

Besides the theoretical discourse in the literature, many empirical studies have examined how patients and professionals perceive PM and which expectations, concerns, values and attitudes related to PM they have. These perspectives are quite relevant to the solution of ethical issues in PM, as they establish context sensitivity for the ethicist. Since morality is realized in social practices, empirical studies illuminate the moral experience of those involved in that practice [ 22 ]. For the ethical discussion of PM, empirical studies of the attitudes, expectations and perspectives of patients and professionals can provide a starting point that would enrich ethical reflection as these studies include moral beliefs, intuitions and reasonings. For this reason, a review of existing empirical studies representing the perceptions of professionals and patients in the field of PM seems useful to researchers and practitioners.

Empirical investigations on this topic have often been conducted in specific medical fields, mainly in oncology. However, to date, there has not been a review that analyzes and synthesizes the results from studies in different medical fields with regard to ethical issues. Our mixed research synthesis is intended to close this gap and deepen the understanding of patients’ and professionals’ views on PM. We believe that the experience gained in various medical fields can provide important information for the further, ethically reflected development of PM. Among professionals, we are particularly interested in the views of those directly involved in patients’ care. This group has a significant impact on ethically relevant issues, such as access to PM, communication about test results, information about treatment options and participation in research. However, we are also interested in researchers’ viewpoint(s), as researchers might consider the risks of data security and machine learning. Our review also serves to prepare an empirical research project on PM in chronic inflammatory diseases. In this project we intend to study the views of patients and professionals more precisely. By reviewing the literature available to date, we will answer the following research question: What are patients’ and professionals’ expectations, concerns, values and attitudes related to PM, including their understandings of risks and benefits?

Because both quantitative and qualitative studies, as well as studies using a mixed method research design, are available to review the perspectives, views, or attitudes of patients and professionals regarding PM, we have chosen an integrated design of Mixed Research Synthesis as the methodology for our literature review. Mixed Research Synthesis integrates both quantitative and qualitative studies by transforming findings to combine them in one synthesis. Transformation can be performed in two different ways: (1) Qualitizing of findings means that quantitative findings are converted into qualitative form in order to combine them with other qualitative data; (2) Quantitizing of findings means converting qualitative findings in a quantitative form in order to combine them with quantitative data [ 23 ]. In this synthesis we mainly used the qualitizing approach since our epistemological interest is focused on the variance of perspectives, views or attitudes and respective justifications for certain positions of the interviewed persons. Results from quantitative studies were either presented with the numerical data from the original studies, or combined with findings from qualitative studies to form a meaningful and accurate statement for the thematic synthesis. By combining quantitative and qualitative data in this way, quantitative data can give more significance to qualitative findings and qualitative data can extend quantitative results.

To achieve the objectives of our review we defined inclusion and exclusion criteria for publications and designed our search strategy as follows:

Inclusion criteria

Content-related criteria

Publications dealing with patients’ and professionals’ expectations, concerns, values and attitudes to PM, including their understanding of risks and benefits

The above-mentioned publications must be relevant to the ethical discourse on PM

Patients are limited to people with diseases or disabilities and representatives of patient organizations, who are (potential) users of PM interventions

Professionals are limited to people who develop and implement PM interventions. These are physicians and other health care professionals (HCPs) in the clinical and outpatient context and researchers.

Empirical studies with qualitative and quantitative methods, reviews

Information sources

Journal articles, books, electronic databases

German and English

Year of publication

Origin of publications

Exclusion criteria

Articles in which the views, expectations, perceptions, values, attitudes or concerns of patients or professionals to PM are not in focus (e.g., clinical trials)

Evaluation studies of new PM curricula or learning models, e.g., for medical students, genetic counsellors

Articles in which the views of patients or professionals were not empirically investigated with scientific methods, but the authors merely presented their personal view or standpoint (commentaries, editorials, letters to the editor, normative analyses, journalistic individual interviews with experts, case studies, study protocols). Our intention was to include empirical studies that (1) were conducted with scientific methods and (2) went beyond providing the opinion of a single person, since we attribute a higher significance to such studies.

Newspaper or magazine articles with journalists as authors or without any author.

Users of direct-to-consumer genetic tests, economists, citizens, relatives, students, education providers, legal experts, representatives from regulatory authorities, reimbursement institutions, pharmaceutical industry, payer institutes, funding institutions, scientific associations, government officials, informatics, non-government-organizations, business experts and imprecisely identified stakeholders who do not meet the inclusion criteria explicitly.

Information sources and search strategy

An initial search in five databases with the search terms expectation, concern, value, attitude, risk, benefit, view and perspective in combination with patient, physician, doctor, stakeholder, expert, researcher and precision medicine, personalized medicine or genomic medicine limited to title resulted in relatively few records. For this reason, we tried to broaden the search by using different types of studies as search terms and combined them with precision medicine, personalized medicine and genomic medicine in the title. This search strategy resulted in 1004 records (Table 1 ).

Screening and eligibility assessment

One researcher screened the abstracts and assessed the full-texts for eligibility. First, the number of records was reduced from 1004 to 665 by removing duplicates. The abstract screening process resulted in 524 titles being excluded as irrelevant, with 141 articles remaining for full-text-screening. During full-text-screening we excluded 49 publications due to the content of the publication, the method, the population studied or the source of information as defined in our exclusion criteria. In some publications a very heterogeneous sample was examined: patients, health care professionals, but also representatives of other groups that did not meet our inclusion criteria, such as representatives of health insurance companies. In such cases, the publication was only included if a sufficient subgroup analysis allowed a separate evaluation of the data. The whole article selection process resulted in 92 publications for this review. Figure  1 visualizes the review process:

Review process

Data extraction

We listed the 92 publications in a data extraction spreadsheet and analyzed the data by identifying themes which related to our research question. During the analysis of professionals and patients’ expectations, perspectives, concerns, values and attitudes, topics that appeared to be irrelevant to the ethics of precision medicine also emerged. These topics included: the concept of PM, compatibility with personal values or professional beliefs, facilitators and needs, interprofessional communication or the changing role of scientists. Since our interest is only in ethically relevant themes, the final decision on whether to include a topic was based on the criterion of its relation to Beachamps and Childress' principles of biomedical ethics which are: beneficence, nonmaleficence, autonomy and justice [ 13 ]. We extracted relevant text passages from the publications and assigned them to the different themes. Finally, we summarized and discussed the identified themes iteratively.

While 62 studies investigated professionals’ perceptions on PM, 45 publications reported on the views of patients. Among the HCPs, many oncologists [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ] participated in the included studies, but also other medical specialists are represented: nephrologists [ 33 , 34 ], cardiologists [ 27 ], infectiologists [ 24 ], psychiatrists and clinical psychologists [ 35 ], pathologists [ 31 , 32 ], gastroenterology specialty trainees [ 36 , 37 ], geneticists and genetic counsellors [ 31 , 32 , 38 , 39 , 40 , 41 , 42 ], laboratory medicine professionals [ 43 ], pharmacists [ 44 , 45 ], critical care intensivists [ 38 ], physician assistants [ 46 ] and nurses [ 30 , 38 , 46 , 47 , 48 ]. From the outpatient sector, primary care providers [ 46 , 49 , 50 , 51 ] or family medicine providers [ 27 , 52 ] participated in some studies. In many studies, researchers [ 42 , 47 , 53 ] were also represented. Those mentioned explicitly were clinical researchers [ 54 , 55 , 56 ], bioinformaticians [ 31 , 32 ], laboratory scientists [ 31 ], experts from genome research [ 32 , 57 ] and, in general terms, representatives from basic [ 54 , 58 ] and translational research [ 58 ].

Since numerous studies have been conducted in oncology, the experiences and views of oncology patients are often found in studies selected for inclusion in our review [ 26 , 56 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ]. However, studies we include also focused on patients with other diseases or disabilities. These were patients with chronic inflammatory diseases [ 71 ], chronic kidney disease [ 72 ], patients without a diagnosis, but with conditions presumed to be genetic (“diagnostic odyssey”) [ 64 ], patients with a chronic condition such as diabetes mellitus, hypercholesterolemia or hypertension [ 73 ], drug users (heroin, crack, cannabis) [ 74 ], patients with rare diseases [ 75 ] and people with disabilities [ 76 , 77 , 78 ]. Some studies did not provide any information about the condition of the patients included in the study.

Regarding the methodology of the studies, a quantitative study design was chosen as frequently as a qualitative design (Fig.  2 ).

Methodology of included studies

We identified 13 topics related to the principles of biomedical ethics [ 13 ]. An overview of the association between the topics and the principles is shown in Table 2 . Although some topics can be assigned to several principles, only the most obvious assignment was given here.

Benefits of precision medicine

Various studies reveal that the majority of professionals have a positive attitude towards PM in general or towards interventions, which are mentioned under the label of PM [ 27 , 34 , 43 , 45 , 47 , 50 , 79 , 80 , 81 , 82 , 83 ]. In particular, the benefits of PM mentioned in the publications by professionals can be summarized as follows (Table 3 ).

Apart from these positive assessments, studies also demonstrate that some professionals are uncertain about the value of genetic testing [ 51 , 90 ] and doubt that all patients will benefit significantly from PM [ 30 , 31 , 42 , 49 , 54 , 57 ]. In addition, Kichko et al. found in their study differences in attitudes between physicians from Pennsylvania and Bavaria. The Bavarian physicians were less convinced of the effectiveness of personalized drugs and less confident that personalized drugs had fewer side effects. They were also more skeptical than their American colleagues that PM can reduce hospitalization days or health care costs. The authors explained these differences with different health care systems, a different culture and history [ 88 ].

According to the studies, most patients hold positive, hope-filled views of PM. The willingness and interest to undergo PM interventions and a positive attitude towards them is high among the majority of patients surveyed [ 29 , 63 , 65 , 69 , 73 , 74 , 77 , 91 , 92 , 93 ]. In the same way as the professionals, patients also combine PM with the possibility to tailor treatments or make them more effective [ 51 , 63 , 67 , 68 , 71 , 94 ], avoid unsuccessful attempts of treatment [ 95 , 96 ], improve drug prescribing [ 73 ] and reduce side effects [ 73 , 95 ]. From the patient perspective, PM has the potential to minimize disease impact [ 68 ], improve quality of life [ 71 ] and decrease chronic pain for patients with chronic inflammatory diseases [ 71 ]. By providing information about their condition and genes, PM can empower patients to “self-advocate” [ 64 ], especially when they have to make informed decisions [ 79 ]. Remarkable is the relevance that patients attach to PM in terms of prevention [ 51 , 94 , 97 ]. Genomic risk knowledge gives patients the opportunity to change their lifestyle for the purpose of health improvement, and in this way, they gain more control over their own health [ 61 ]. Additional benefits mentioned in this context are the possibility to learn about ancestry, help family members [ 51 ] and improve family planning [ 97 ]. Some patients hope that new treatments will be discovered [ 97 ], that future patients will benefit from research, and medical care in general will be improved [ 98 ].

Patients’ understanding and knowledge

Although many patients are convinced of the benefits of PM, their actual knowledge of its potential appears to be limited. Many professionals reported in the studies that patients have little or no awareness about the concept and potentials of PM [ 25 , 58 ] and these professionals are skeptical about whether patients have the ability to understand PM [ 47 , 56 , 96 ]. One reason for this could be that the terms "stratified," "precision" or "personalized" medicine are rarely used in medical consultations [ 59 ] and in discussions within patient organizations [ 96 ]. Some physicians tend to simplify the complex issues in their conversations with patients and use other terms like “evidence-based medicine” [ 56 ]. However, it seems that physicians sometimes underestimate patients’ knowledge. For example, Ciardiello et al. did not always show agreement between doctors and patients in assessment of patient knowledge. While 85% of patients felt well informed about their treatment after the explanation of the doctor, only 23% of doctors agreed that their patients were well informed [ 29 ].

Conversely, some professionals perceive patients as the "drivers" [ 49 ] of PM, especially when it comes to the availability of genetic testing [ 49 ]. Informed by the media [ 26 , 73 ], especially patients in oncology ask their physicians about PM more frequently than patients with other diseases [ 27 ]. For example, Tejpar et al. showed that the majority of patients in oncology are aware of genetic testing to determine which cancer treatment might be the best for an individual person [ 70 ]. However, as other studies have demonstrated, many patients generally have a limited knowledge about PM [ 65 , 71 , 99 , 100 , 101 , 102 ], genetic testing [ 62 , 69 , 95 ] and pharmacogenomics [ 73 , 101 ] and also express difficulties in understanding [ 51 ]. Patients seem to be more familiar with older terms like “gene” or “DNA” than with newer ones like “pharmacogenomics” or “biobank” [ 101 ]. Even if patients are aware of the phrase “personalized medicine,” 19% of them do not have the right idea of what PM really is and combine PM, for example, with a constant doctor-patient contact or with the participation of patients in medical decision-making [ 63 ]. In addition, representatives from patient organizations note that patients often have difficulties in understanding basic medical information and that patient education will be a major task for patient organizations [ 96 ]. Hellwig et al. pointed out that patients’ understanding is also in the interest of health care providers, as it facilitates the communication process [ 86 ].

Professionals’ knowledge and competence

Professionals also describe a lack in their own knowledge or a limited understanding, as quantitative studies demonstrate [ 36 , 37 , 44 , 81 , 82 , 92 , 103 , 104 ]. For example, in a study with 100 UK gastroenterology trainees, most of them believed that their training had not prepared them for practicing PM [ 36 , 37 ]. Carroll et al. surveyed 361 family physicians in Canada about their knowledge in genomic medicine and found a median knowledge score of 6 (on a scale from 0 to 10) with a wide range from 0 to 10. The self-reported level of confidence in practicing genomic medicine tasks was low in that study [ 85 ]. Alharbi et al. assessed the knowledge of 126 South Arabian physicians about PM in diabetes mellitus management and found that 82.5% of the participants had poor knowledge in this area [ 82 ]. These and other studies [ 37 , 44 , 45 , 46 , 47 , 58 , 86 , 105 , 106 , 107 , 108 ] reveal the need for further education of many health care providers, however, this requirement may vary between professionals in different fields [ 53 , 81 ]. An exception seems to be the skills of physicians in oncology. A comparative study with oncologists, cardiologists and family doctors concluded that oncologists felt better informed, were more able to interpret test results and more confident in discussing the results with their patients [ 27 ]. Nevertheless, even among oncologists there seems to be some uncertainty at times regarding the choice of the right treatment [ 28 , 31 ] and the interpretation [ 30 , 31 , 32 ] or explanation [ 31 ] of genomic data. Another exception of better knowledge was also demonstrated among clinical geneticists and genetic counsellors. As the study by Nisselle et al. highlighted, the majority of genetic counsellors (74.2%, n = 271) and clinical geneticists (87.0%, n = 83) had attended continuing professional development in genomics during the two previous years [ 41 ].

Access to precision medicine

Due to the high demand for research, resources and infrastructure required for PM, it is questionable whether access to it will be open for all patients in the world. For example, in Saudi Arabia, where healthcare is predominantly taxpayer-funded [ 109 ], 27.8% of physicians surveyed (n = 126) doubted that patients would have easy access to PM [ 82 ]. In Korea, a state where the percentage of out-of-pocket-payments in healthcare is 35% [ 110 ], most of the health care professionals surveyed believed that only a few patients would have access to PM and 84.8% of the participants (n = 542) were concerned that this would increase disparity in public health [ 47 ]. In Europe as well, where healthcare is primarily funded through government-regulated public health insurance systems, taxpayers and private insurance policies [ 111 , 112 ], PM is considered by researchers, healthcare providers and patient representatives to have limited availability. This is because only a few programs are already in place. [ 58 ]. In the US, where about 50% of healthcare funding is private [ 111 , 112 ], 78% of patients, HCPs and patient representatives surveyed (n = 72) expressed concern about the difficulty for patient advocates to help patients gain PM access [ 68 ].

One factor that could limit access to PM might be the lack of coverage by health insurance companies for medication [ 54 ], tests [ 29 , 80 ] or genetic counselling [ 90 ] and the inability of patients to pay for PM out of pocket [ 73 , 90 , 97 ]. Physicians are described as gatekeepers and their decisions on medical appropriateness, which may vary and be determined by guidelines [ 29 ], condition access to PM interventions. Patients perceive these variations as disparity in access and some patients also suspect that physicians withhold access because of the high cost [ 26 ]. In addition, age [ 54 , 79 ], the availability of tests [ 29 , 80 ], the clinic or hospital location, patient attitudes, norms and education, as well as social factors like discouragement by significant others are identified as barriers for PM [ 90 ]. Another factor could be the lack of communication to the public about the options PM offers. This possibility was mentioned by a member of the National Black Nurses Association in the USA, who questioned why the All of Us program is only communicated in English and Spanish [ 48 ]. That access to PM could be a question of ethnicity was shown by the Petersen et al. study. Only 38.5% of doctors surveyed (n = 104) in the US believed that PM is available to all ethnic groups. [ 103 ]. Ratcliff et al. cited a study which shows that ethnic minorities are less likely to embrace PM technology. Ethnic minorities and individuals with lower socioeconomic status seem to be less aware of technologies and less likely to use them [ 113 ]. The findings from a focus group study from Kraft et al. (2018) drew attention to the inability of US immigrants to navigate the healthcare system which can result in a lack of trust in the healthcare system [ 114 ]. The US-American survey from Diaz et al. (2014) revealed that for non-Hispanic, Black respondents disparity in access due to the inability to pay for PM is of greater concern [ 97 ]. But access to PM for ethnic minorities could also be limited for another reason. According to patient representatives, the availability of PM for certain groups could also depend on the profitability for the pharmaceutical industry of providing appropriate medicines for people with certain genetic characteristics [ 96 ].

Table 4 summarizes the factors which could limit access to PM from the perspective of professionals and patients. While factors limiting the offer and provision of PM prevail on the professional side, patients see limitations in their own possibilities to access it.

Discrimination and stigmatization

In addition to possible disparities in access to PM, professionals and patients fear discrimination based on the results of genetic testing [ 27 , 79 , 92 , 97 ], particularly by insurance companies or employers [ 46 , 51 , 54 , 63 , 86 , 94 , 95 , 96 , 98 , 115 ]. The majority of physicians interviewed in the USA and Germany would therefore not grant access to genetic information to employers and health insurance companies [ 88 ]. However, an American longitudinal study of 823 patients shows that none of the respondents had problems with health insurance after one year and only three patients reported problems with life insurance or long-term care insurance [ 92 ].

But genetic information also opens up the possibility of stigmatization of certain groups [ 57 ]. In a qualitative study, an African American participant explained this more precisely: this participant feared being put into a certain racially determined category because of genetic information which is much more present in that specific ethnic group [ 98 ].

From the perspective of professionals an additional unacceptable use of data would be to refuse someone an organ transplantation because of their genetic predisposition to organ rejection [ 33 , 34 ]. Additionally, health advocates in oncology have expressed concern that patients who are not suitable for personalized treatment will not receive appropriate support [ 68 ].

Privacy and confidentiality

Some professionals consider apprehension about data confidentiality not being guaranteed [ 45 , 88 , 116 ]. According to some researchers, the greatest danger here is in the sloppiness of people who work with the data and load it, for example, onto their laptop or flash drive [ 116 ]. As an indication that data security cannot be guaranteed, some patients mention the hacking of financial or online data and emphasize the need for harsh penalties [ 98 ]. People with a drug addiction history are concerned that medical practitioners cannot refuse information requests from courts [ 74 ]. A survey of medical practitioners and patients in India gives a somewhat more optimistic impression. Some of the respondents believe that data confidentiality is secured by advances in technology, while others are suspicious of whether the person entrusted with confidentiality can guarantee data security [ 105 ]. However, it seems that the human factor is the greatest weakness of the system.

Apart from data security, one publication raises another problem: the confidentiality of genetic information to families rather than to individuals, which means that information about a genetic disposition is shared with all at-risk family members. This “familial approach to confidentiality” [ 117 ] is conceptualized in UK genetic guidelines. In the study from Dheensa et al., 80 HCPs were interviewed in focus groups about their arguments for or against this approach to confidentiality. One of the respondents' arguments was that a familial approach could affect family relationships and the patient's trust in the health care system. A second argument concerned their resources for sharing information and the fear that sharing would make them more vulnerable to liability issues [ 117 ].

Harm from test results or the testing process

Although the benefits of genetic testing are not questioned by many patients and professionals, some agree that test results or the testing process itself can also cause harm. This harm can occur, for example, if patients or professionals misinterpret the results [ 90 ] and for this reason make the wrong therapeutic decision [ 32 , 92 ]. Some studies indicate that women who have been tested for breast cancer predisposition have undergone preventive surgery, even though the result was considered to be uninformative by their physician [ 40 , 92 ]. One professional reported that a patient committed suicide after receiving the diagnosis of Huntington disease on the phone [ 40 ]. Besides these adverse events, professionals and patients in some studies report psychological implications from knowing [ 31 , 32 , 46 , 51 , 63 , 113 ] or while waiting for the test results [ 26 ]. Incidental findings [ 79 , 96 ], variants of unknown significance [ 79 ] or results that indicate a high risk for an incurable disease can affect the well-being of patients [ 51 ]. In some studies, patients explicitly state that they do not want to know certain results, e.g., about a genetic predisposition to a disease [ 95 ], especially if it cannot be cured [ 61 , 118 ]. Not knowing enables people to keep hope, a positive self-perception and remain optimistic [ 113 ]. From the perspective of professionals during tumor board meetings, McGraw et al. in 2017 showed the potential harm from patients receiving information about test results, and also the harm when test results are withheld: the omission of findings can result in a missed opportunity to learn about a serious disease. Withholding test results may be useful in the future, but is an expression of “excessive paternalism” [ 32 ]. Therefore, some authors have argued that the preferences of each patient on receiving their test results need to be identified and addressed [ 57 ] and that professionals have the responsibility to protect patients and families from harm [ 42 ].

Although many of the above-mentioned studies indicate that patients can imagine possible harm from test results or the testing process, the review by McFarland et al. presents a slightly different picture. It found no evidence that patients have any concerns about tumor testing for the purpose of targeted therapy. The authors attributed this difference to the fact that this kind of testing is not a test for an inherited cancer risk, but rather a targeted therapy, similar to chemotherapy [ 100 ]. For patients, therefore, it seems to make a difference whether it is a question of identifying a risk for an illness or of searching for the right therapy for a severe disease that already exists.

Communication and informed consent

For various reasons, professionals perceive communication on PM research, tests and therapies and obtaining informed consent as being increasingly complex [ 31 , 79 ]. For example, in cancer PM the complexity and ambiguity of the evidence seems to make it difficult to decide which test results should be communicated to patients [ 32 ]. Challenges in obtaining informed consent include complex discussions about risks, the length of documents, the lack of understanding by professionals and the resources required [ 42 ]. Several quantitative studies indicate that patients want to be fully informed about tests [ 71 ], the results [ 51 , 71 , 72 , 78 ] and treatment options [ 29 ]. Further, they wish to be involved in the decision-making process [ 29 , 71 ]. However, genetic tests arouse suspicion among members of ethnic minorities who fear not being fully informed about the purpose and further use of the tests. Therefore, some patients are reluctant to use targeted therapies [ 74 ].

According to the professionals, another reason for the growing complexity of the discussions is the increasing presence of PM in the media. Some doctors feel that this increased media attention means discussions and decisions on testing are becoming more complicated and that they sometimes feel forced to order a test [ 26 ]. Clinicians and researchers also criticize that the media raises unrealistic expectations among patients, which cannot always be fulfilled [ 31 ].

With regard to participation in research, patients from different ethnic groups express skepticism about the consent process and are suspicious about whether all consented rules are actually followed. Many would therefore prefer to have the right to withdraw their consent at any time of the study [ 98 ]. Some participants would like to have separate consent for biospecimens and EHR data, as they see a risk for misuse of DNA in future research [ 115 ]. The Edwards et al. survey reveals that the majority of patients wish re-consent, if their data are used for a different, but related or unrelated health condition. A re-consent should also be granted when a child reaches the age of majority. Despite these preferences, the majority of respondents believe that the benefit of broad consent outweighs the harm, highlighting the feasibility and relevance of research [ 60 ].

Lack of evidence

For clinical practice, test results are only useful when they deliver reliable and actionable information that can be used for clinical decisions. However, interpreting multiomics, clinical and lifestyle data becomes complicated by inadequate validation of biomarkers and insufficient evidence of clinical utility, which leads to clinical uncertainty [ 58 ]. Many clinicians and researchers are well aware of the limited [ 27 , 30 , 31 , 35 , 42 , 79 , 84 ] or ambiguous evidence [ 32 ] for the meaning of test results and possible treatment outcomes and regard the lack of practice guidelines as a barrier for the implementation of tests [ 27 , 79 ]. Some patients also doubt the accuracy of the tests [ 63 , 73 ] or the value of the test results to influence their fate [ 61 ]. In summary, the problem of small samples in clinical trials caused by patient stratification leads to an awareness of unclear evidence among professionals and creates uncertainty about what conclusions can be drawn from the tests for therapeutic decisions.

Doctor-patient-relationship

Some publications indicate that professionals expect a change in the doctor-patient relationship through PM. In the studies by Dion-Labrie et al., some of the physicians interviewed expressed concern that behind the objective data, the human aspects of the doctor-patient relationship and the view on the whole person could be lost. The gain in objectivity carries the risk that less room is given to feelings in communication with the patient. [ 33 , 34 ]. Another study suggests that the availability of large amounts of data on the patient increases the knowledge lead of the doctor, with the risk that the doctor will use this knowledge to make paternalistic decisions [ 56 ]. From the patient perspective, inequalities in access to PM can put a negative strain on the doctor-patient relationship, for example, the patient knows about this option but their doctor has not offered it to them [ 26 ].

Patients’ data provision and health-related work

The willingness to participate in research and to provide genetic information, lifestyle, environmental or medical data was examined in a Korean study with 526 participants. The majority of the clinicians, researchers and health professionals surveyed showed a clear willingness to participate, although this willingness is higher when it comes to their own treatment as opposed to that of others [ 47 ]. Among patients there is also a high readiness to participate in trials and donate data or biospecimens [ 62 , 70 , 72 , 76 , 77 , 98 , 99 ], but trust in professionals [ 98 , 101 ], costs, receiving counseling about test results and privacy [ 101 ], as well as the donor’s religion and culture [ 98 ] seem to be important for the decision. Significantly fewer patients support the use of smartphone apps to track lifestyle, behavior or environmental influences [ 72 , 76 , 77 ]. Possible factors behind this reluctance could be that this type of self-monitoring continuously confronts patients with their illness and that they get tired by digital interactions. As a result, some patients evade health care provider expectations of recording data using smartphone apps or wearables [ 113 ]. In addition, several studies show that the large number of tests carried out in the course of PM is a considerable burden for patients [ 71 ], as it is associated with time spent waiting at a clinic [ 59 ] and possibly with the need to travel [ 61 ]. These burdens are factors that determine whether patients have PM tests or treatments performed [ 61 , 67 ].

Profiteering with patient s’ data/biospecimens

Although there is generally a high willingness among patients to donate data and biospecimens for precision medicine research, one study also suggests that patients expect a corresponding countervalue when pharmaceutical companies earn large sums of money from drug development that was made possible by patients' donations. In the study, patients refer to the case of Henrietta Lacks, in which her cells (taken from tumor biopsy) were cultured on 1951 without her knowledge/consent and resulted in the HeLa immortalized cell line that is still being used in research today. The Lacks case highlights the injustice that results from making an enormous profit from the biospecimens of unsuspecting patients. Some participants in the study argued that if biospecimens contributes to corporate profit, patients should be compensated [ 98 , 115 ].

Health care costs

In several studies, professionals and patients express concern about the high cost of PM. Although professionals expect a reduction of the overall health care costs in the long term [ 47 ], some health care professionals question the cost–benefit ratio of PM [ 30 , 54 , 84 , 105 ]. Compared to the costs of chronic diseases, PM costs are perceived as not only being massive, but also caused by a much smaller proportion of the entire patient population [ 30 ]. It is therefore questionable whether other care interventions would not have a greater benefit and deserve better funding [ 119 ]. But PM costs are also problematic for other reasons. For example, professionals [ 45 , 120 ], patients and representatives from patient organizations are concerned about whether health insurance companies will cover the costs [ 51 , 63 , 76 ] or patients can pay for them [ 61 , 95 , 96 ]. And indeed a study from Europe reports a certain reluctance on the part of health insurers to cover the costs for PM, as the evidence is insufficient and incentives too small [ 58 ]. The willingness of patients to pay for PM themselves is apparently higher when the purpose is to treat a serious illness, such as cancer [ 73 ]. Nevertheless, most American and German physicians agree that the costs for PM should not be covered by the patients themselves [ 88 ]. Some patients also worry that if a drug is not prescribed frequently enough, pharmaceutical companies will raise the price or stop its production [ 98 ]. This would make PM unavailable to small groups with certain traits, which could put these groups at a disadvantage compared to others.

Implications of the findings in context of existing research

Our review results show that many professionals and most patients expect high benefits from PM and have a positive attitude towards it. However, there is more doubt among professionals as to whether patients actually benefit from PM, which may be related to the fact that there is less evidence for positive effects of PM than in conventional medicine. The high specificity and costs of therapies mean that drugs are tested in smaller clinical trials rather than in large randomized controlled trials and even if several small trials have shown no risks, a level of uncertainty remains [ 10 ]. It is quite understandable that HCPs, whose training has so far been oriented on the ideal of evidence-based medicine, have more doubts here. In addition, many HCPs do not feel sufficiently trained and are uncertain about the interpretation of genomic data and the choice of the right therapy. Although machine learning systems can support the interpretation of data and develop therapy recommendations, doctors should not blindly rely on a machine without checking its results. Training programs on medical informatics for physicians will therefore become necessary [ 121 ]. In the meantime, it seems to be useful that different specialists, e.g., physicians, bioinformaticians, geneticists and genetic counselors work together in an interdisciplinary network, as is already the case in some places. Bioinformaticians in particular are considered to be of high importance [ 47 ]. The complexity of PM also confronts health care professionals with the challenge of communicating with patients and obtaining informed consent. Since geneticists and genetic counsellors seem to be better trained in genomics [ 41 ], their involvement appears to be essential.

However, improving professional competence and interdisciplinary cooperation alone cannot overcome patients’ lack of knowledge and understanding of PM. Rather, patient education about PM must be provided in an understandable way to ensure that patients can make autonomous decisions. By taking the level of health and genetic literacy into account, patient education must also be personalized. The concerns of ethnic minorities, who seem to have a stronger distrust of genetic testing [ 74 ] and the needs of people with disabilities [ 76 ] should be taken into consideration. The text-based education practiced in many places cannot meet these requirements [ 56 ]. Here, new formats need to be found and implemented. A promising approach could be short video sequences like those used by some researchers in their research projects [ 98 , 115 ]. Since 3.3 billion people by today own a smartphone [ 122 ], those videos could easily be downloaded and viewed with such devices. Here, too, patients’ digital competence must be considered. Today, not all patients, especially the elderly, are ready to meet the challenges of digitization or have the appropriate IT infrastructure. Transmission of videos in patient waiting rooms seems a possible alternative. However, these additional services should not replace a personal consultation with a professional, but only supplement it.

The results of the review reveal that many professionals doubt that all patients who would benefit will have access to PM. But if PM can actually fulfil the hopes for more targeted therapies, fewer side effects and an improved quality of life, then the ethical principle of justice requires access to PM for all people in need. The individual patient's insurance status, ethnicity, age or place of residence should not limit access to PM. This principle seems particularly difficult to realize both nationally and internationally, as many health care systems will not be able to cover the costs of a nationwide PM implementation, However, full cost coverage by health insurance companies would make access much easier. Since health care providers act as gatekeepers and control access to PM, the development of guidelines must keep pace with PM implementation. Physicians in the outpatient sector, patient organizations and the public should be kept continuously informed of current developments.

But even if everyone had access to PM—regardless of their personal characteristics—at this time, not everyone would benefit from it equally. The reason for this is a recruitment bias in PM, namely the fact that most genetic data are obtained from Northern Europeans. This means that for other population groups, there is an increased probability that the result of a genetic test will produce variants of unknown clinical significance. Thus, it is not known whether these variants can cause a disease, as they have not yet been researched. The result is that non-Northern Europeans benefit less from developments in PM [ 123 ]. In addition, social circumstances, such as having to travel long distances to a medical center, fear of sanctions for work absences or language and cultural barriers can also make access to PM difficult [ 7 ]. And if medical consultations can only be arranged online or even the meeting with the physician takes place via video consultation, people who do not have the digital competence or the appropriate equipment can thus be excluded from PM [ 7 ].

As our review reveals, the cost–benefit ratio of PM is not considered balanced by some professionals and the critical question is raised of whether other interventions promise greater benefits and deserve better funding. Rey-Lopez et al. in their 2018 study also criticized the exorbitant expenditure on PM and suggested instead that more resources should be invested in improving people’s living conditions and health-related behavior. For example, policies that counteract climate change, such as a meat-free diet, abandonment of motor vehicles in favor of more physical activity, would be equally or even more beneficial to health and reduce mortality. They argue that technology-based prevention programs like fitness apps for weight reduction, which are sometimes promoted by PM, have not had a better effect on health than conventional programs. Rather, the required behavioral changes could be achieved by addressing social, cultural, economic and environmental circumstances [ 124 ]. In addition to better prevention, there are probably many other areas of healthcare in some countries where more money needs to be invested. Considering the cost-effectiveness of PM, Kasztura et al. revealed in their scoping review that PM is to date at least as effective as conventional care. The authors conclude that the cost-effectiveness of PM should be further investigated with different research approaches [ 125 ].

Our findings show that patients and professionals are concerned that genetic information could end up in the hands of insurance companies and employers and thereby lead to “genetic discrimination” [ 126 ]. This concern is not unjustified. For use in healthcare, genetic information needs to be integrated into electronic health records and these are increasingly becoming a target for attacks by cybercriminals [ 127 ]. Furthermore, as one study reveals, human error represents an additional risk [ 116 ]. Data security must therefore be given high priority, but the collection of genetic, environmental or lifestyle data should also be limited to information that is really necessary. Before data collection, patients should be informed about the possibility of data breaches to enable patients to balance their personal benefits and risks.

As we have seen in the studies, the (unwanted) disclosure of test results can harm patients in many ways. Experiencing a potential risk of illness can be a considerable strain on people, especially if the illness is incurable. However, avoiding telling patients about their genetic disposition for a treatable disease can also be harmful, as it deprives patients and their families of the opportunity for prevention and screening [ 10 ]. Therefore, for each genetic test, the patient's preferences regarding the information about disease risks should be respected, as already contained in existing legislation in some nations [ 128 ]. In addition, incidental findings associated with genetic traits of unknown clinical significance or findings of misattributed parentage [ 8 ] are problematic, because communicating these findings to patients and their families can also trigger stress and affect family relationships. By obtaining informed consent, the information on possible incidental findings should be kept short and concise so that patients understand the complex issue and are not confused by the various options for decision [ 10 ]. The possibility of re-contacting the patient is seen as a moral obligation if findings of previously unknown clinical significance can be better understood in the future and linked to therapeutic options [ 9 ].

In merely two studies, professionals talked about the possibility of changes in the doctor-patient relationship. This concern is perhaps more likely to be expressed by ethicists. By restraining the focus of the physician on measurable parameters and bodily functions [ 119 ], the patient's personality, their history, values and ideas about life risk being lost [ 11 ]. Salari and Larijani’s work described the danger that the patient will be perceived by the physician only as "genetic material" [ 12 ]. The complete digitalization of human life with omics-based data could lead to alienation between physician and patient in which the individuality of the patient increasingly disappears behind the data and algorithms [ 129 ]. The question of whether such changes can actually be observed in practice should be the subject of further empirical research.

Our review shows that patients and professionals are highly willing to participate in clinical trials and donate data and biospecimens to PM. Nevertheless, some people would like to be compensated for their donation if pharmaceutical companies gain a high profit with it [ 98 , 115 ]. The question of whether patients should receive something in return for their donation or “gift” of data and biospecimens is one Lee discussed in a 2020 study [ 20 ]. In this work, Lee referred to anthropologist Marcel Mauss and considered the gift in the context of social relationships, where a gift is inextricable from obligations and reciprocity. The metaphor of the gift requires something in return, and some authors see the return of individual genetic information as a way to honor the gift in precision medicine research. However, such an approach must take into account that the value of the genetic information (e.g., actionable, non-actionable) is understood by the research participants [ 20 ] and also in their interest. The wish not to know individual genetic information should be respected.

In contrast to biospecimen donations, patients’ willingness to donate is less evident for the use of their data obtained from smartphone apps that collect lifestyle, behavioral or environmental information. A possible reason for this could be that these apps put patients under increasing pressure to continuously optimize their health condition, for example through physical exercise or practicing a certain diet. Prainsack argued in 2017 that PM is not possible without patients contributing data, time, effort and self-care, and describes this contribution as "patient work" [ 7 ]. Although patients have been encouraged to adopt a healthy lifestyle for a long time, the knowledge of the importance of the exposome and modern possibilities of constant data monitoring are leading to patients having a stronger responsibility for their health or recovery. This responsibility transforms patients into what Zwart calls, “bio-citizens [who] are expected to measure and monitor their bodies and their everyday lifeworld in real time, continuously and automatically” [ 130 ]. As the use of smartphone apps and wearables will continue to increase, the possible psychological impact of continuous self-optimization needs to be further researched.

Limitations of the study

Our study has the following limitations: First, our search strategy was limited to three terms in the title of the publications (precision medicine, personalized medicine and genomic medicine). These terms are all used synonymously for PM, but each emphasizes different aspects of PM. We omitted the terms individualized medicine and systems medicine to ensure the feasibility of the study in a limited time frame. Compared to the words we have used in our search, the term individualized medicine seemed to be too unspecific and the term systems medicine seemed to be less common [ 5 ]. The use of these search terms would certainly have resulted in an additional number of relevant studies. A second limitation relates to the screening and eligibility assessment, which could only be performed by one researcher. A second person would have increased the reliability of the eligibility assessment.

Main conclusion

National legislation and guidelines in many countries have already addressed and solved a number of problems associated with PM. However, from our perspective, some problems still require more attention. If we approve the four principles for biomedical ethics of Beauchamp and Childress as a basis for ethical decisions, which is common in the European and North American context, then respect for the autonomy of the individual must be interpreted in the contexts of PM and ensured in the first place. Autonomy in PM is realized primarily in free and informed decision-making, so respect for autonomy demands comprehensible education and support. Our results show, however, that patients have difficulties in understanding some of the underlying ideas of precision medicine, therefore an adaptation of information documents to make them more understandable and an improvement of patient education seems necessary. Respect for autonomy could be improved by taking into account individual health literacy when educating people about decisions to participate in tests, therapies, research or self-tracking. In more family or community-based societies where therapeutic decisions are not made on the basis of the patient's will alone, it would make sense to include the community in the development of PM educational activities, in order to learn how to improve the knowledge and understanding of those involved in decision making.

Should hopes for precision medicine’s benefits be fulfilled, then the ethical principle of justice would require an unlimited access to precision medicine for all people. As our results show, both patients and professionals, have considerable doubts about this. We agree with the view that justice is easier to realize in public health care systems, which are funded by taxpayers or by government-regulated public health insurance plans. The obvious reason is that in these health care systems access is not dependent on personal financial resources. However, it is questionable whether the cost–benefit ratio of precision medicine is advantageous and whether other patient groups that do not benefit from PM are not ultimately disadvantaged due to lack of financial resources. The cost-effectiveness of PM should therefore be further investigated.

The ethical principle of nonmaleficence requires that PM should in no case harm the patient. However, from the perspectives of professionals and patients, the collection of health data carries a high risk of misuse. Thus the principle of nonmaleficence requires data collection of genetic, environmental or lifestyle data to be limited to what is absolutely necessary. Data security must be given high priority.

The question of whether the physician–patient relationship is altered by the physician's focus on multi-omics data and whether the patient's subjective experience is thereby eclipsed also relates to the ethical principle of nonmaleficence. This too requires further investigation.

The most important question seems to be whether and under which conditions PM really has the potential to improve patients’ quality of life – the life they themselves would judge as “good” – or whether it is subjectively perceived as worse, due to an over-compliance with the imperative of self-optimization and the necessary constant work on one's own health. This question, which refers to the ethical principle of beneficence, also needs to be more closely explored in future research.

Explanation of the importance and relevance of the study

This is the first review that presents and analyzes results from qualitative and quantitative studies regarding the perception of patients and professionals on ethical issues in PM.

Availability of data and materials

The dataset used and analyzed during this study is available from the corresponding author on reasonable request.

Abbreviations

• Precision medicine

Inflammatory bowel disease

Electronic Health Record

Health Care Professional

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Erdmann, A., Rehmann-Sutter, C. & Bozzaro, C. Patients’ and professionals’ views related to ethical issues in precision medicine: a mixed research synthesis. BMC Med Ethics 22 , 116 (2021). https://doi.org/10.1186/s12910-021-00682-8

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Zelenskiy calls for operational changes to Ukraine military after sacking commander

President demands ‘new level of medical support for soldiers’ as questions mount over speed of counteroffensive against Russia

Volodymyr Zelenskiy has demanded rapid changes in the operations of Ukraine’s military and announced the dismissal of the commander of its medical forces.

The Ukrainian president’s move was announced on Sunday as he met defence minister, Rustem Umerov, and coincided with debate over the conduct of the 20-month-old war against Russia , with questions over how quickly a counteroffensive in the east and south is proceeding.

“In today’s meeting with defence minister Umerov, priorities were set,” Zelenskiy said in his nightly video address. “There is little time left to wait for results. Quick action is needed for forthcoming changes.”

Zelenskiy said he had replaced Maj Gen Tetiana Ostashchenko as commander of the medical forces.

“The task is clear, as has been repeatedly stressed in society, particularly among combat medics, we need a fundamentally new level of medical support for our soldiers,” he said.

This, he said, included a range of issues – better tourniquets, digitalisation and better communication.

Umerov acknowledged the change on the Telegram messaging app and set as top priorities digitalisation, “tactical medicine” and rotation of service personnel.

Ukraine’s military reports on what it describes as advances in recapturing occupied areas in the east and south and last week acknowledged that troops had taken control of areas on the eastern bank of the Dnipro River in southern Kherson region.

Ukrainian commander in chief, Gen Valery Zaluzhny, in an essay published this month, said the war was entering a new stage of attrition and Ukraine needed more sophisticated technology to counter the Russian military.

While repeatedly saying advances will take time, Zelenskiy has denied the war is headed into a stalemate and has called on Kyiv’s western partners, mainly the United States, to maintain levels of military support.

Ostashchenko was replaced by Maj Gen Anatoliy Kazmirchuk, head of a military clinic in Kyiv.

Her dismissal came a week after a Ukrainian news outlet suggested her removal, as well as that of others, was imminent after consultations with paramedics and other officials responsible for providing support to the military.

Meanwhile on Sunday, air defence units in Moscow intercepted a drone targeting the city, mayor Sergei Sobyanin said.

Sobyanin, writing on the Telegram messaging app, said units in the Elektrostal district in the capital’s east had intercepted the drone.

According to preliminary information, falling debris resulting from the operation had caused no casualties or damage, Sobyanin said.

• Volodymyr Zelenskiy

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