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“What Goes Around Comes Around”: Lessons Learned from Economic Evaluations of Personalized Medicine Applied to Digital Medicine

  • Kathryn A. Phillips
    Correspondence
    Address correspondence to: Kathryn A. Phillips, Department of Clinical Pharmacy, Center for Translational and Policy Research on Personalized Medicine (TRANSPERS), University of California at San Francisco, 3333 California Street, Room 420, Box 0613, San Francisco, CA 94143.
    Affiliations
    Department of Clinical Pharmacy, Center for Translational and Policy Research on Peronalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, CA, USA

    Philip R. Lee Institute for Health Policy, University of California San Francisco, San Francisco, CA, USA

    Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
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  • Michael P. Douglas
    Affiliations
    Department of Clinical Pharmacy, Center for Translational and Policy Research on Peronalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, CA, USA
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  • Julia R. Trosman
    Affiliations
    Department of Clinical Pharmacy, Center for Translational and Policy Research on Peronalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, CA, USA

    Center for Business Models in Healthcare, Chicago, IL, USA

    Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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  • Deborah A. Marshall
    Affiliations
    Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
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      Abstract

      Background

      The growth of “big data” and the emphasis on patient-centered health care have led to the increasing use of two key technologies: personalized medicine and digital medicine. For these technologies to move into mainstream health care and be reimbursed by insurers, it will be essential to have evidence that their benefits provide reasonable value relative to their costs. These technologies, however, have complex characteristics that present challenges to the assessment of their economic value. Previous studies have identified the challenges for personalized medicine and thus this work informs the more nascent topic of digital medicine.

      Objectives

      To examine the methodological challenges and future opportunities for assessing the economic value of digital medicine, using personalized medicine as a comparison.

      Methods

      We focused specifically on digital biomarker technologies and multigene tests. We identified similarities in these technologies that can present challenges to economic evaluation: multiple results, results with different types of utilities, secondary findings, downstream impact (including on family members), and interactive effects.

      Results

      Using a structured review, we found that there are few economic evaluations of digital biomarker technologies, with limited results.

      Conclusions

      We conclude that more evidence on the effectiveness of digital medicine will be needed but that the experiences with personalized medicine can inform what data will be needed and how such analyses can be conducted. Our study points out the critical need for typologies and terminology for digital medicine technologies that would enable them to be classified in ways that will facilitate research on their effectiveness and value.

      Keywords

      Introduction

      The growth of “big data” and the increasing emphasis on patient-centered health care and consumer engagement have contributed to the emergence of two key technologies: 1) personalized medicine (also known as precision or genomic medicine—the use of genetic information to target health care interventions) and 2) digital medicine (also known as mhealth—the digital transmission of information and various combinations of telecommunications, hardware, and software to deliver health care services). It has been said that we are entering the “information age” for health care, in which everything is connected and the integration of big data—characterized by high velocity, volume, and variety—is becoming increasingly important [
      • Marshall D.A.
      • Burgos-Liz L.
      • Pasupathy K.S.
      • et al.
      Transforming healthcare delivery: integrating dynamic simulation modelling and big data in health economics and outcomes research.
      ,
      • Phillips K.A.
      • Trosman J.R.
      • Kelley R.K.
      • et al.
      Genomic sequencing: assessing the health care system, policy, and big-data implications.
      ,

      Nelson H. What we talk about when we talk about digital healthcare. Available from: http://harrynelson.com/future-of-healthcare/digitalhealthcare/. [Accessed July 22, 2016].

      ]. Both personalized medicine and digital medicine are emerging in mainstream health care and away from being narrowly focused on only limited conditions (such as genetic testing for rare childhood disorders) or solely “entertainment” devices that are not intended to impact health outcomes (such as free phone applications [apps]).
      The emergence of personalized medicine and digital medicine in mainstream health care has accelerated in recent years because of the growing availability of these technologies, often at decreasing costs. There are now more than 60,000 genetic tests available for more than 4,000 disorders [

      GeneTests. Available from: https://www.genetests.org/. [Accessed July 7, 2016].

      ], and the cost of multigene panel tests such as whole-genome sequencing has dropped dramatically [

      National Human Genome Research Institute. The cost of sequencing a human genome. Available from: https://www.genome.gov/27565109/the-cost-of-sequencing-a-human-genome/. [Accessed July 7, 2016].

      ]. The use of smartphones is now almost ubiquitous in the United States—80% of US adults have a smartphone, and 30% of these phones have at least one health-related app [

      Rockhealth. The emerging influence of digital biomarkers on healthcare. Available from: https://rockhealth.com/reports/the-emerging-influence-of-digital-biomarkers-on-healthcare/. [Accessed July 7, 2016].

      ].
      The intersections between personalized medicine and digital medicine are increasing [
      • Haga S.B.
      Challenges of development and implementation of point of care pharmacogenetic testing.
      ]. Eric Topol, in his seminal book on how the digital revolution will create better health care, noted that personalized and digital medicine technologies are converging [
      • Topol E.J.
      The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care.
      ], and digital health has been defined as the “convergence of the digital and personalized revolutions with health, health care, living, and society” [

      Wikipedia. Digital health. Available from: https://en.wikipedia.org/wiki/Digital_health. [Accessed July 7, 2016].

      ]. A recent report noted that funding for digital health personalized medicine companies comprised half of overall genomics funding in three of the five years, and that delivering on the promise of genomics is dependent on the following factors that are within the purview of digital health: 1) ensuring broad access to diverse data sets used to deliver insights, 2) removing barriers to clinical workflow incorporation, and 3) advancing technology, both in the laboratory and in the cloud [

      Rockhealth. The genomics inflection point: implications for healthcare. Available from: https://rockhealth.com/reports/the-genomics-inflection-point-implications-for-healthcare/. [Accessed July 25, 2016].

      ]. Importantly, digital technologies will play a key role in the recently funded National Institutes of Health Precision Medicine Initiative, with data from mobile health devices and apps integrated with data from genetic tests, surveys, and electronic health records in what has been termed the “most ambitious medical research program in the history of American medicine” [

      The Scripps Research Institute. TSRI awarded $20M for first year of US Precision Medicine Initiative Cohort Program. Available from: http://www.scripps.edu/newsandviews/e_20160718/topol.html. [Accessed July 22, 2016].

      ].
      Nevertheless, for personalized medicine and digital medicine to be adopted more widely as a routine part of health care services and to be reimbursed by insurers, it will be essential to have evidence that these technologies have been evaluated for their accuracy, clinical effectiveness, economic value, and ethical implications [
      • Haddow J.E.
      • Palomaki G.E.
      ACCE: a model process for evaluating data on emerging genetic tests.
      ]. Many have noted the hope that personalized medicine and digital medicine will transform health care by improving outcomes and decreasing costs [
      • Steinhubl S.R.
      • Muse E.D.
      • Topol E.J.
      The emerging field of mobile health.
      ,
      • Collins F.S.
      • Varmus H.
      A new initiative on precision medicine.
      ]. Many, however, have also noted that more evidence on the value of these technologies will be needed, particularly for digital medicine given that it has more recently entered mainstream health care relative to personalized medicine [
      • Eapen Z.J.
      • Peterson E.D.
      Can mobile health applications facilitate meaningful behavior change? Time for answers.
      ,
      • Hostetter M.
      • Klein S.
      • McCarthy D.
      Taking Digital Health to the Next Level: Promoting Technologies That Empower Consumers and Drive Health System Transformation.
      ,
      • Aitken M.
      • Lyle J.
      Patient Adoption of mHealth: Use, Evidence and Remaining Barriers to Mainstream Acceptance.
      ,
      • Kuehn B.M.
      Is there an app to solve app overload?.
      ,
      • Gagnon M.P.
      • Ngangue P.
      • Payne-Gagnon J.
      • et al.
      m-Health adoption by healthcare professionals: a systematic review.
      ,
      • Bloss C.S.
      • Wineinger N.E.
      • Peters M.
      • et al.
      A prospective randomized trial examining health care utilization in individuals using multiple smartphone-enabled biosensors.
      ].
      Our objective was to examine the methodological challenges and future opportunities for assessing the economic value of digital medicine, using personalized medicine as a comparison and focusing specifically on digital biomarker technologies and multigene tests. We begin by identifying how these technologies share several characteristics that present similar challenges for economic evaluation. We then draw on previous work identifying methodological challenges for economic evaluation of complex technologies and assess how they are applicable to multigene tests and digital biomarker technologies. We follow with a structured review of cost and outcome studies of digital biomarkers. We conclude with an assessment of future steps needed to facilitate assessing the economic value of these new technologies.

      Characterizing and Comparing Personalized Medicine and Digital Medicine

      Before we can examine the economic issues, we need to first characterize personalized medicine and digital medicine and then describe how they are similar. Both personalized medicine and digital medicine include a wide range of technologies and thus comparing “personalized medicine” and “digital medicine” in their entirety would be too diffuse. We begin by defining the scope of personalized medicine and digital medicine and the focus of this article—digital biomarkers and multigene tests. We then compare the technologies in terms of challenges to economic evaluation
      Personalized medicine includes genetic tests and targeted interventions. These technologies can be used for a range of purposes (e.g., risk prediction, treatment decisions, and prenatal screening) and can be focused on either the individual’s genetic makeup or the genetic variation that is acquired (e.g., cancer tumors). Genetic tests also range from tests for a single gene to tests for the entire genome. The scope of personalized medicine is now often considered to include more than genetic information—to include any disease prevention or treatment approach that takes into account differences in people’s genes, environments, and lifestyles [

      US Food and Drug Administration. FDA’s role in the Precision Medicine Initiative. Available from: http://www.fda.gov/ScienceResearch/SpecialTopics/PrecisionMedicine/default.htm. [Accessed July 7, 2016].

      ]. (For the purposes of this study, we do not distinguish between genomic medicine, personalized medicine, and precision medicine.)
      Digital medicine includes a wide range of technologies ranging from consumer-oriented monitoring apps to telemedicine and electronic health records. Monitoring apps and devices range from simple activity trackers to more complex technologies such as respiratory monitors to monitor asthma, electrocardiograms to monitor heart conditions, and glucose monitors for diabetes control. An example of a complex, emerging digital technology is the “smart” contact lens with embedded sensors for conditions such as glucose monitoring being developed by Google’s Verily.
      One scheme classified digital medicine into the following categories [

      Rockhealth. Digital health funding 2015 midyear review. Available from: https://rockhealth.com/reports/digital-health-2015-midyear/. [Accessed July 7, 2016].

      ]:
      • 1.
        Wearables and biosensors—wearable or accessory devices that detect specific biometrics and are designed for consumers, with data transmission to providers as relevant;
      • 2.
        Analytics and big data—data aggregation and/or analysis to support a wide range of health care use cases;
      • 3.
        Health care consumer engagement—consumer tools for the purchasing of health care products and services or health insurance;
      • 4.
        Telemedicine—delivery of health care services (synchronous or asynchronous) through nonphysical means (e.g., telephone, digital imaging, and video);
      • 5.
        Enterprise wellness—services designed to improve general well-being of employees; and
      • 6.
        Electronic health record and clinical workflow—electronic health records and surround apps, including clinical workflow support/augmentation.
      Within these broad categories, two technologies that are most relevant for the purpose of this study are 1) multigene tests and 2) digital biomarker technologies. Multigene tests include “panels” (tests that analyze multiple genes including newly recognized genes and/or for multiple syndromes) and “whole-exome/whole-genome sequencing” (tests that analyze the exome or the whole genome). Digital biomarker technologies, which fall into the category of “wearables and biosensing devices,” use consumer-generated physiological and behavioral measures collected through connected digital tools that can be used to explain, influence, and/or predict health-related outcomes [

      Rockhealth. The emerging influence of digital biomarkers on healthcare. Available from: https://rockhealth.com/reports/the-emerging-influence-of-digital-biomarkers-on-healthcare/. [Accessed July 7, 2016].

      ]. These technologies may focus on measurements for consumer use only, or clinical measurements that are transmitted to clinicians for health care decision making. They may passively monitor ongoing activities (such as steps taken) or be used to actively collect specific measurements (such as blood glucose). These technologies are relevant because they both measure biomarkers, which is a general term for any physiological characteristic that is objectively measured and evaluated to indicate a disease state; both technologies can produce enormous amounts of data that have to be integrated to provide meaningful results, and both technologies are complex because they include multiple measures and results, which may include clinically actionable results as well as results that provide only information of personal utility to the consumer or that have no known significance.
      An example of the intersection between multigene tests and digital biomarker technologies was noted in a recent report [

      Rockhealth. The emerging influence of digital biomarkers on healthcare. Available from: https://rockhealth.com/reports/the-emerging-influence-of-digital-biomarkers-on-healthcare/. [Accessed July 7, 2016].

      ]. This report noted that the “most promising” consequence of digital biomarkers is the ability to create digital biomarker panels—and that a parallel is seen in the example of gene expression signatures that serve diagnostic, prognostic, and predictive roles. Health care panels with multiple measures have proven to be clinically useful in other areas of medicine; for example, 10-year cardiovascular risk is best predicted by a set of measurements including age, sex, cholesterol levels, smoking and medication status, and blood pressure [

      Rockhealth. The emerging influence of digital biomarkers on healthcare. Available from: https://rockhealth.com/reports/the-emerging-influence-of-digital-biomarkers-on-healthcare/. [Accessed July 7, 2016].

      ]. There are at present a limited number of technologies that directly integrate genomic data with digital technologies for consumer use. Examples are apps that combine behavioral/phenotypic data captured via an iPhone or Apple Watch and genetic data from 23andMe to identify novel genetic correlations [

      Rockhealth. The genomics inflection point: implications for healthcare. Available from: https://rockhealth.com/reports/the-genomics-inflection-point-implications-for-healthcare/. [Accessed July 25, 2016].

      ], and the Pathway Genomics OME™ app (San Diego, CA) that “merges cognitive computing and deep learning with precision medicine and genetics to enable Pathway Genomics to provide consumers with genomic wellness information” [

      Pathway Genomics. Pathway Genomics debuts first genomic wellness app powered by IBM Watson. Available from: https://www.pathway.com/debut-1st-genomic-wellness-app-ome/. [Accessed July 7, 2016].

      ].

      Methodological Challenges of Measuring the Value of Complex Technologies

      Our work and that of others have analyzed the challenges of examining the economic value of complex technologies such as personalized medicine [
      • Phillips K.A.
      • Ann Sakowski J.
      • Trosman J.
      • et al.
      The economic value of personalized medicine tests: what we know and what we need to know.
      ,
      • Bennette C.S.
      • Gallego C.J.
      • Burke W.
      • et al.
      The cost-effectiveness of returning incidental findings from next-generation genomic sequencing.
      ,
      • Phillips K.A.
      • Pletcher M.J.
      • Ladabaum U.
      Is the “$1000 Genome” really $1000? Understanding the full benefits and costs of genomic sequencing.
      ,
      • Buchanan J.
      • Wordsworth S.
      • Schuh A.
      Issues surrounding the health economic evaluation of genomic technologies.
      ,
      • Phillips K.A.
      • Sakowski J.A.
      • Liang S.
      • et al.
      Economic perspectives on personalized health care and prevention.
      ,
      • Annemans L.
      • Redekop K.
      • Payne K.
      Current methodological issues in the economic assessment of personalized medicine.
      ,
      • Fugel H.J.
      • Nuijten M.
      • Postma M.
      • et al.
      Economic evaluation in stratified medicine: methodological issues and challenges.
      ,
      • Rogowski W.
      • Payne K.
      • Schnell-Inderst P.
      • et al.
      Concepts of “personalization” in personalized medicine: implications for economic evaluation.
      ,
      • Phillips K.A.
      • Ladabaum U.
      • Pletcher M.J.
      • et al.
      Key emerging themes for assessing the cost-effectiveness of reporting incidental findings.
      ]. Because of the similarities between personalized medicine and digital medicine—particularly between multigene tests and digital biomarker technologies—reviewing the challenges identified for personalized medicine can provide insights into how similar challenges may be relevant to digital medicine.
      Table 1 presents a summary of the test characteristics that have been identified as presenting challenges to economic evaluations: multiple results, results with different types of utilities, secondary findings, downstream impact (including on family members), and interactive effects. For each of these characteristics, we noted the implications for conducting economic analyses, including a need for more complicated analyses and more in-depth analyses of utilities and impacts. The table then presents how multigene tests and digital biomarker technologies illustrate each of these challenges. For example, as noted earlier, a key advantage of multigene tests and digital biomarker technologies is their ability to integrate results from multiple biomarkers into panels in which the sum is greater than the parts. This, however, can present a challenge to economic evaluation because data on costs and effectiveness may be available only for each individual biomarker and thus the interactive effect would not be incorporated in value calculations. Similarly, both technologies produce large amounts of information that may not be clinically actionable and may produce unexpected harms such as unexpected results or results that produce anxiety or lead to unwarranted interventions.
      Table 1Characteristics of technologies, challenges for economic evaluations, and application to multigene tests and digital biomarker technologies
      Characteristics of technologiesChallenges for economic evaluationsMultigene testing examplesDigital medicine examples
      Measures multiple biomarkers, thus providing multiple resultsComplicated analyses are required that may be infeasible because of the large number of possible pathways and outcomesWhole-genome sequencing can provide multiple results, with multiple clinical pathways, costs, and outcomesActivity monitors can provide multiple types of data (steps, heart rate, sleep patterns, etc.) with multiple clinical pathways, costs, and outcomes
      Results have different utilities: clinically actionable, personal utility only, harmful, and/or unknown significancePersonal utility is difficult to value; costs of harmful results and/or results with unknown significance may not be incorporated into analysesMultigene tests may provide information with personal utility or disutility only (e.g., knowing that one is at risk for a nonpreventable condition) or that has unknown significance leading to unwarranted interventions (e.g., a genetic variation that has not been validated but leads to further testing)Activity monitors may provide information that is unlikely to be clinically actionable (e.g., whether you move during the night) and technologies that encourage physical activity such as pedometers may produce unexpected harms (e.g., joint injury)
      Results may include secondary findings (potentially actionable findings unrelated to the reason for using the technology)Complicated analyses required to capture potentially low probability events and associated utilities; often lack of data on costs and outcomes of secondary findingsMultigene testing for one inherited condition (e.g., cardiovascular risk) may reveal previously undiagnosed risk for another condition (e.g., BRCA1/2, which confers a high risk of breast and ovarian cancer)Technologies for measuring continuous blood pressure may provide results on heart disease but could also indicate unrelated findings (e.g., mood and emotion)
      Downstream impact on costs and outcomes, including impact on family membersComplicated analyses required to examine impact over time; impact on family members may not be incorporated into analysesCosts and outcomes for multigene panels for inherited conditions, such as Lynch syndrome, depend to a large extent on downstream follow-up by family members (e.g., increased colorectal cancer screening)Technologies used to diagnose AF may impact family members (30% of individuals with AF have a family member with the condition)
      Results may have interactive effects such that the “sum is greater than the parts”Complicated analyses required to estimate interactive effectsTumor profiling measures multiple genes that together may provide a more comprehensive assessment of a tumor and treatment options than if testing were done individuallyTechnologies such as smart watches provide multiple types of seemingly unrelated data (e.g., standing time, walking/steps, heart rate, weight) and the sum valuation of these on outcomes such as preventing obesity is likely greater than each individual measurement
      AF, atrial fibrillation.

      Comparison of Economic Evaluations

      We first conducted a structured review of economic evaluations of digital biomarker technologies to assess what is known about their economic value and discuss how these results illustrate some of the methodological challenges for measuring the value of complex technologies. We then compared these results with previously published reviews of economic evaluations of personalized medicine.

      Structured Review of Economic Evaluations of Digital Biomarker Technologies

      Because there are no specific Major Exact Subject Heading (MeSH) terms for “digital medicine,” we used a combination of keyword and MeSH terms to identify economic evaluation studies (conducted for the past 5 years till April 2016) of digital biomarker technologies:(((((((((fitbit) OR activity monitor) OR consumer-wearable) OR trackers) OR digital) OR ((((“Computers, Handheld”[Mesh] OR “Cell Phones”[Mesh] OR “Smartphone”[Mesh]) OR “Mobile Applications”[Mesh]) OR “Telemedicine”[Mesh])))) AND ((“Cost-Benefit Analysis”[Mesh]) OR “Costs and Cost Analysis”[Mesh]) NOT “telemedicine”)
      We included studies of technologies that met our definition of digital biomarkers and those that included a comparison of costs and outcomes (cost-consequence analysis, cost-effectiveness analysis, or cost-benefit analysis). We excluded studies of technologies that did not collect data from individuals but provided individuals with a one-way communication (e.g., text message) and studies of digital services such as telemedicine. We excluded studies that examined only costs or that used the term “cost-effectiveness” but did not calculate a cost-effectiveness ratio. We identified 281 studies in our initial search. We then excluded 258 studies on the basis of a review of their titles or abstracts and 18 studies on the basis of a review of the full text, leaving 5 included studies. Studies were coded by two authors.
      Two key findings emerge from our review (Table 2). First, we found only five relevant articles [
      • Cano Martin J.A.
      • Martinez-Perez B.
      • de la Torre-Diez I.
      • et al.
      Economic impact assessment from the use of a mobile app for the self-management of heart diseases by patients with heart failure in a Spanish region.
      ,
      • Leung W.
      • Ashton T.
      • Kolt G.S.
      • et al.
      Cost-effectiveness of pedometer-based versus time-based Green Prescriptions: the Healthy Steps Study.
      ,
      • Lowres N.
      • Neubeck L.
      • Salkeld G.
      • et al.
      Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study.
      ,
      • Ryan D.
      • Price D.
      • Musgrave S.D.
      • et al.
      Clinical and cost effectiveness of mobile phone supported self monitoring of asthma: multicentre randomised controlled trial.
      ,
      • Shaw R.
      • Fenwick E.
      • Baker G.
      • et al.
      “Pedometers cost buttons”: the feasibility of implementing a pedometer based walking programme within the community.
      ]. None of these studies was conducted in the United States, which is surprising given that digital medicine is a major focus in the country. These results suggest that digital biomarker technologies are only beginning to be formally evaluated for their costs/outcomes. Second, we found that only two of the five studies concluded that the digital intervention was cost-effective or that the costs were reasonable relative to the outcomes, with two more studies concluding that the results were equivocal.
      Table 2Economic evaluations of digital biomarker technologies
      ConditionIntervention (what is tool and what it is used for)ComparatorPopulation included (sociodemographic characteristics, N)Type of cost analysis and resultsKey economic conclusions from articles (direct quote from article)Did authors conclude that it was cost-effective or had reasonable costs?Source
      AFScreening for AF using iPhone iECG by pharmacists for stroke preventionDiagnosis of AF in an unscreened populationGeneral population (65–84 y)(Australia, N = 1000)Cost-utility analysis“Screening with iECG for AF in pharmacies with an automated algorithm is both feasible and cost-effective.”Yes
      • Lowres N.
      • Neubeck L.
      • Salkeld G.
      • et al.
      Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study.
      $4,066 per QALY gained; $20,695 for preventing one stroke
      Heart failureCardioManager App to allow heart disease patients to self-manage their conditionsNo usePatients with heart failure (Spanish communities [Castile and Leon], N = 2000)Cost-utility analysis“CardioManager may generate 33% reduction in cost of management and treatment… may be able to save more than $10,940 per patient to the local Health Care System.”Yes
      • Cano Martin J.A.
      • Martinez-Perez B.
      • de la Torre-Diez I.
      • et al.
      Economic impact assessment from the use of a mobile app for the self-management of heart diseases by patients with heart failure in a Spanish region.
      $11,300 per QALY gained
      Asthma controlt+ Asthma App for monitoring and transmission of symptoms, drug use, and peak flow with immediate feedback to improve asthma controlStandard paper-based monitoring strategiesPatients with asthma (United Kingdom, N = 288)Cost-consequence analysis“The t+ Asthma App was more expensive because of the expenses of telemonitoring and was not cost-effective.”No
      • Ryan D.
      • Price D.
      • Musgrave S.D.
      • et al.
      Clinical and cost effectiveness of mobile phone supported self monitoring of asthma: multicentre randomised controlled trial.
      Telemonitoring cost difference was significant ($108 per patient); mean cost of care $382 intervention group vs. $380 comparison group
      Physical activity and health-related quality of lifePedometer-based activity instructions to increase daily number of stepsTime-based instructions (initial clinical consultation, written advice with time-based personal activity goals, 3 telephone sessions)Low physical activity, adults aged 65 y and older (Auckland, NZ, N = 330)Cost-utility analysis“There were no significant between-group differences in costs. Outcomes suggest intervention may be cost-effective in increasing physical activity and health-related quality of life over 12 months.”Maybe
      • Leung W.
      • Ashton T.
      • Kolt G.S.
      • et al.
      Cost-effectiveness of pedometer-based versus time-based Green Prescriptions: the Healthy Steps Study.
      Intervention vs. comparator, per 30 min of weekly walking/per QALY:
      1) community care costs $115/$3,105; 2) exercise and community care costs $130/$3,500; 3) all costs $185/$4,999
      Physical activityTwo interventions:Normal walking behaviorLow physical activity individuals (Glasgow, Scotland, N = 79)Cost-effectiveness analysis“Pedometer based walking interventions may be considered cost-effective and suitable for implementation within the wider community.”Maybe
      • Shaw R.
      • Fenwick E.
      • Baker G.
      • et al.
      “Pedometers cost buttons”: the feasibility of implementing a pedometer based walking programme within the community.
      1) Minimal (normal walking with minimal instruction)QALY $143 (minimal) and $917 (maximal) per person achieving 15,000 steps/wk
      2) Maximal (using pedometer to increase walking to 15,000 steps)
      Note. An iECG is an instrument that attaches to an iPhone that is used to take an electrocardiogram; the CardioManager App is a disease management app for patients with heart disease that includes sections for disease information, for recording the user’s activities and health measurements, and for registering the user’s medications and the hours that they should have them; the t+ Asthma App enables twice-daily recording and transmission of symptoms, drug use, and peak flow. The recorded peak flow was displayed within the traffic light zones and the patient was prompted to follow the agreed action plan. Incursion into the red or amber zones triggered contact by an asthma nurse; a pedometer is an instrument for estimating the distance traveled on foot by recording the number of steps taken.
      AF, atrial fibrillation; iECG, iPhone electrocardiogram; QALY, quality-adjusted life-year.
      This review suggests several ways in which the measurement of the economic value of digital biomarker technologies is likely to be challenging. The included analysis of a digital technology for atrial fibrillation (AF) [
      • Lowres N.
      • Neubeck L.
      • Salkeld G.
      • et al.
      Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study.
      ] illustrates several of the challenges noted in Table 1. One of the similar challenges found in personalized medicine and digital medicine is the method of addressing the downstream impact on costs and outcomes, including impact on family members that the technologies may present. For example, recent studies suggest that up to 30% of people with AF may have familial AF and thus have a higher chance of having a relative with the condition [
      • Lubitz S.A.
      • Yin X.
      • Fontes J.D.
      • et al.
      Association between familial atrial fibrillation and risk of new-onset atrial fibrillation.
      ]. Because AF can be inherited, an AF diagnosis can result in a cascade of costs and outcomes not only for the individual (e.g., warfarin therapy) but also for their family members (e.g., risk/diagnostic testing and possible warfarin therapy). The analysis included in our review focused on detecting AF using an electrocardiogram; it, however, did not consider the fact that AF can be inherited and did not address downstream costs such as risk/diagnostic testing of family members or treatment for afflicted family members.

      Comparison of Economic Evaluations of Digital Biomarker Technologies with Those of Personalized Medicine

      There are few published cost-effectiveness analyses specifically focusing on multigene tests [
      • Bennette C.S.
      • Gallego C.J.
      • Burke W.
      • et al.
      The cost-effectiveness of returning incidental findings from next-generation genomic sequencing.
      ,
      • Phillips K.A.
      • Ladabaum U.
      • Pletcher M.J.
      • et al.
      Key emerging themes for assessing the cost-effectiveness of reporting incidental findings.
      ,
      • Gallego C.J.
      • Shirts B.H.
      • Bennette C.S.
      • et al.
      Next-generation sequencing panels for the diagnosis of colorectal cancer and polyposis syndromes: a cost-effectiveness analysis.
      ,
      • Li Y.
      • Bare L.A.
      • Bender R.A.
      • et al.
      Cost effectiveness of sequencing 34 cancer-associated genes as an aid for treatment selection in patients with metastatic melanoma.
      ,

      Doble B, John T, Thomas D, et al. Cost-effectiveness of precision medicine in the fourth-line treatment of metastatic lung adenocarcinoma: an early decision analytic model of multiplex targeted sequencing. Lung Cancer (published online ahead of print June2, 2016). doi:10.1016/j.lungcan.2016.05.024.

      ]. We thus used previous reviews of personalized medicine more generally for comparisons. In our previous review of cost-utility analyses of personalized medicine published between 1998 and 2011 [
      • Phillips K.A.
      • Ann Sakowski J.
      • Trosman J.
      • et al.
      The economic value of personalized medicine tests: what we know and what we need to know.
      ], we found that 80% of studies (N = 59) concluded that genetic testing had favorable cost-effectiveness ratios (cost per quality-adjusted life-year gained <$100,000 or cost saving). In a review covering studies of personalized medicine published between 2010 and 2014, 84% of studies (N = 38) reported that their findings indicated favorable cost-effectiveness [
      • Berm E.J.
      • Looff M.
      • Wilffert B.
      • et al.
      Economic evaluations of pharmacogenetic and pharmacogenomic screening tests: a systematic review (second update of the literature).
      ]. These results are similar to those for other medical interventions [
      • Phillips K.A.
      • Ann Sakowski J.
      • Trosman J.
      • et al.
      The economic value of personalized medicine tests: what we know and what we need to know.
      ]. In comparison, our review of digital biomarker technologies suggests that these technologies may less likely be cost-effective than personalized medicine or other technologies although the small number of studies found precludes any definitive conclusions.

      Conclusions

      We found only a few economic evaluations of digital biomarker technologies, consistent with reports suggesting that few digital medicine technologies have been evaluated for their costs/outcomes. This is not surprising given that economic value is difficult to examine without first establishing the effectiveness of the technology in improving outcomes, and effectiveness data are generally lacking for digital medicine technologies. For example, authors of a recent prospective, randomized trial of individuals using smartphone-enabled biosensors for chronic disease management noted that this was the first randomized trial to examine costs as well as outcomes [
      • Bloss C.S.
      • Wineinger N.E.
      • Peters M.
      • et al.
      A prospective randomized trial examining health care utilization in individuals using multiple smartphone-enabled biosensors.
      ]. This study found no evidence of differences in health care utilization or costs although it found some limited evidence that the use of the technology improved the perception of control over health status. On the one hand, such results assuage concerns that digital monitoring will lead to unwarranted health care utilization and costs; on the other hand, they provide little evidence that such technologies will improve health outcomes.
      The present lack of effectiveness evidence will be a hindrance to conducting economic evaluations of digital medicine. The experience with personalized medicine, however, suggests how economic analyses can be useful even when such evidence is lacking, for example, by identifying variables that are particularly important for data collection, estimating the range of possible conclusions, and developing innovative modeling approaches [
      • Phillips K.A.
      • Trosman J.R.
      • Kelley R.K.
      • et al.
      Genomic sequencing: assessing the health care system, policy, and big-data implications.
      ,
      • Phillips K.A.
      • Ann Sakowski J.
      • Trosman J.
      • et al.
      The economic value of personalized medicine tests: what we know and what we need to know.
      ,
      • Bennette C.S.
      • Gallego C.J.
      • Burke W.
      • et al.
      The cost-effectiveness of returning incidental findings from next-generation genomic sequencing.
      ,
      • Phillips K.A.
      • Pletcher M.J.
      • Ladabaum U.
      Is the “$1000 Genome” really $1000? Understanding the full benefits and costs of genomic sequencing.
      ,
      • Phillips K.A.
      • Ladabaum U.
      • Pletcher M.J.
      • et al.
      Key emerging themes for assessing the cost-effectiveness of reporting incidental findings.
      ].
      Our list of challenges suggests what type of data may be needed to conduct economic analyses, such as the interactive effect across multiple measures. Given the small number of economic evaluations of digital biomarker technologies identified, we did not attempt to assess their quality. Nevertheless, in searching for these studies, we found many instances in which standard methodologies and terminology were not used, for example, a study was described as being a “cost-effectiveness analysis” even when there was no incremental cost-effectiveness analysis ratio presented.
      Our study points out the critical need for typologies of digital medicine technologies that would enable them to be classified in ways that will facilitate research on their effectiveness and value. We were unable to locate any detailed categorizations or taxonomies of digital medicine, even in the gray literature. Taxonomies would enable better identification of technologies and their relevant comparators, costs, and outcomes.
      A similar need is for standardized subject heading terms in PubMed for digital medicine. There is at present no MeSH term for digital or digital medicine and thus there is variability in how studies are coded and it is difficult to locate relevant studies. It is not surprising that a rapidly developing field such as digital medicine requires an evolution in terminology, but given that smartphones have been available for a decade, there is an urgent need to develop consistent and timely terminology and categorizations of studies.
      Our study has limitations that should be addressed in future research. Given that this is the first study to our knowledge that has begun to lay out the challenges for economic evaluation of digital medicine, this should be considered an initial overview of the topic. Our review of economic evaluations focused only on one specific type of digital medicine and we may have missed some studies because PubMed coding is not yet well-standardized, but we think that our illustrative analyses portend what we would have found with a broader, more comprehensive search. Last, we did not attempt to derive inferences from cost/outcome studies of multigene tests, given that few have been published.
      We have described an initial approach to considering how the economic value of digital medicine can be examined. We suggested several steps that could facilitate these needed analyses. Digital medicine offers great potential to improve outcomes and increase patient engagement, but evidence on its value is needed.

      Acknowledgment

      We are grateful to TRANSPERS (Center for Translational and Policy Research on Personalized Medicine) team members for their advice on the manuscript.
      Source of financial support: This study was partially funded by a National Human Genome Research Institute grant to K.A. Phillips (grant no. R01HG007063), a National Cancer Institute grant to the UCSF Helen Diller Family Comprehensive Cancer Center (grant no. 5P30CA082013-15), and the UCSF Mount Zion Health Fund. D.A. Marshall is supported by a Canada Research Chair, Health Services and Systems Research, and the Arthur J.E. Child Chair in Rheumatology Outcomes Research.

      References

        • Marshall D.A.
        • Burgos-Liz L.
        • Pasupathy K.S.
        • et al.
        Transforming healthcare delivery: integrating dynamic simulation modelling and big data in health economics and outcomes research.
        Pharmacoeconomics. 2016; 34: 115-126
        • Phillips K.A.
        • Trosman J.R.
        • Kelley R.K.
        • et al.
        Genomic sequencing: assessing the health care system, policy, and big-data implications.
        Health Aff. 2014; 33: 1246-1253
      1. Nelson H. What we talk about when we talk about digital healthcare. Available from: http://harrynelson.com/future-of-healthcare/digitalhealthcare/. [Accessed July 22, 2016].

      2. GeneTests. Available from: https://www.genetests.org/. [Accessed July 7, 2016].

      3. National Human Genome Research Institute. The cost of sequencing a human genome. Available from: https://www.genome.gov/27565109/the-cost-of-sequencing-a-human-genome/. [Accessed July 7, 2016].

      4. Rockhealth. The emerging influence of digital biomarkers on healthcare. Available from: https://rockhealth.com/reports/the-emerging-influence-of-digital-biomarkers-on-healthcare/. [Accessed July 7, 2016].

        • Haga S.B.
        Challenges of development and implementation of point of care pharmacogenetic testing.
        Expert Rev Mol Diagn. 2016; 16: 949-960
        • Topol E.J.
        The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care.
        Basic Books, New York, NY2012
      5. Wikipedia. Digital health. Available from: https://en.wikipedia.org/wiki/Digital_health. [Accessed July 7, 2016].

      6. Rockhealth. The genomics inflection point: implications for healthcare. Available from: https://rockhealth.com/reports/the-genomics-inflection-point-implications-for-healthcare/. [Accessed July 25, 2016].

      7. The Scripps Research Institute. TSRI awarded $20M for first year of US Precision Medicine Initiative Cohort Program. Available from: http://www.scripps.edu/newsandviews/e_20160718/topol.html. [Accessed July 22, 2016].

        • Haddow J.E.
        • Palomaki G.E.
        ACCE: a model process for evaluating data on emerging genetic tests.
        in: Khoury M. Little J. Burke W. Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease. Oxford University Press, New York, NY2003
        • Steinhubl S.R.
        • Muse E.D.
        • Topol E.J.
        The emerging field of mobile health.
        Sci Transl Med. 2015; 7: 283rv3
        • Collins F.S.
        • Varmus H.
        A new initiative on precision medicine.
        N Engl J Med. 2015; 372: 793-795
        • Eapen Z.J.
        • Peterson E.D.
        Can mobile health applications facilitate meaningful behavior change? Time for answers.
        JAMA. 2015; 314: 1236-1237
        • Hostetter M.
        • Klein S.
        • McCarthy D.
        Taking Digital Health to the Next Level: Promoting Technologies That Empower Consumers and Drive Health System Transformation.
        The Commonwealth Fund, New York, NY2014
        • Aitken M.
        • Lyle J.
        Patient Adoption of mHealth: Use, Evidence and Remaining Barriers to Mainstream Acceptance.
        IMS Institute for Healthcare Informatics, Parsippany, NJ2015
        • Kuehn B.M.
        Is there an app to solve app overload?.
        JAMA. 2015; 313: 1405-1407
        • Gagnon M.P.
        • Ngangue P.
        • Payne-Gagnon J.
        • et al.
        m-Health adoption by healthcare professionals: a systematic review.
        J Am Med Inform Assoc. 2016; 23: 212-220
        • Bloss C.S.
        • Wineinger N.E.
        • Peters M.
        • et al.
        A prospective randomized trial examining health care utilization in individuals using multiple smartphone-enabled biosensors.
        Peer J. 2016; 4: e1554
      8. US Food and Drug Administration. FDA’s role in the Precision Medicine Initiative. Available from: http://www.fda.gov/ScienceResearch/SpecialTopics/PrecisionMedicine/default.htm. [Accessed July 7, 2016].

      9. Rockhealth. Digital health funding 2015 midyear review. Available from: https://rockhealth.com/reports/digital-health-2015-midyear/. [Accessed July 7, 2016].

      10. Pathway Genomics. Pathway Genomics debuts first genomic wellness app powered by IBM Watson. Available from: https://www.pathway.com/debut-1st-genomic-wellness-app-ome/. [Accessed July 7, 2016].

        • Phillips K.A.
        • Ann Sakowski J.
        • Trosman J.
        • et al.
        The economic value of personalized medicine tests: what we know and what we need to know.
        Genet Med. 2014; 16: 251-257
        • Bennette C.S.
        • Gallego C.J.
        • Burke W.
        • et al.
        The cost-effectiveness of returning incidental findings from next-generation genomic sequencing.
        Genet Med. 2015; 17: 587-595
        • Phillips K.A.
        • Pletcher M.J.
        • Ladabaum U.
        Is the “$1000 Genome” really $1000? Understanding the full benefits and costs of genomic sequencing.
        Technol Health Care. 2015; 23: 373-379
        • Buchanan J.
        • Wordsworth S.
        • Schuh A.
        Issues surrounding the health economic evaluation of genomic technologies.
        Pharmacogenomics. 2013; 14: 1833-1847
        • Phillips K.A.
        • Sakowski J.A.
        • Liang S.
        • et al.
        Economic perspectives on personalized health care and prevention.
        Forum Health Econ Policy. 2013; 16: 57-86
        • Annemans L.
        • Redekop K.
        • Payne K.
        Current methodological issues in the economic assessment of personalized medicine.
        Value Health. 2013; 16: S20-S26
        • Fugel H.J.
        • Nuijten M.
        • Postma M.
        • et al.
        Economic evaluation in stratified medicine: methodological issues and challenges.
        Front Pharmacol. 2016; 7: 113
        • Rogowski W.
        • Payne K.
        • Schnell-Inderst P.
        • et al.
        Concepts of “personalization” in personalized medicine: implications for economic evaluation.
        Pharmacoeconomics. 2015; 33: 49-59
        • Phillips K.A.
        • Ladabaum U.
        • Pletcher M.J.
        • et al.
        Key emerging themes for assessing the cost-effectiveness of reporting incidental findings.
        Genet Med. 2015; 17: 314-315
        • Cano Martin J.A.
        • Martinez-Perez B.
        • de la Torre-Diez I.
        • et al.
        Economic impact assessment from the use of a mobile app for the self-management of heart diseases by patients with heart failure in a Spanish region.
        J Med Syst. 2014; 38: 96
        • Leung W.
        • Ashton T.
        • Kolt G.S.
        • et al.
        Cost-effectiveness of pedometer-based versus time-based Green Prescriptions: the Healthy Steps Study.
        Aust J Prim Health. 2012; 18: 204-211
        • Lowres N.
        • Neubeck L.
        • Salkeld G.
        • et al.
        Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study.
        Thromb Haemost. 2014; 111: 1167-1176
        • Ryan D.
        • Price D.
        • Musgrave S.D.
        • et al.
        Clinical and cost effectiveness of mobile phone supported self monitoring of asthma: multicentre randomised controlled trial.
        BMJ. 2012; 344: e1756
        • Shaw R.
        • Fenwick E.
        • Baker G.
        • et al.
        “Pedometers cost buttons”: the feasibility of implementing a pedometer based walking programme within the community.
        BMC Public Health. 2011; 11: 200
        • Lubitz S.A.
        • Yin X.
        • Fontes J.D.
        • et al.
        Association between familial atrial fibrillation and risk of new-onset atrial fibrillation.
        JAMA. 2010; 304: 2263-2269
        • Gallego C.J.
        • Shirts B.H.
        • Bennette C.S.
        • et al.
        Next-generation sequencing panels for the diagnosis of colorectal cancer and polyposis syndromes: a cost-effectiveness analysis.
        J Clin Oncol. 2015; 33: 2084-2091
        • Li Y.
        • Bare L.A.
        • Bender R.A.
        • et al.
        Cost effectiveness of sequencing 34 cancer-associated genes as an aid for treatment selection in patients with metastatic melanoma.
        Mol Diagn Ther. 2015; 19: 169-177
      11. Doble B, John T, Thomas D, et al. Cost-effectiveness of precision medicine in the fourth-line treatment of metastatic lung adenocarcinoma: an early decision analytic model of multiplex targeted sequencing. Lung Cancer (published online ahead of print June2, 2016). doi:10.1016/j.lungcan.2016.05.024.

        • Berm E.J.
        • Looff M.
        • Wilffert B.
        • et al.
        Economic evaluations of pharmacogenetic and pharmacogenomic screening tests: a systematic review (second update of the literature).
        PLoS One. 2016; 11: e0146262