Can mHealth Revolutionize Evidence-Based Practice in Diabetes Care?

ABSTRACT

Mobile health (mHealth) is a branch of the digital health market that specifically uses mobile technologies. Data from mHealth can inform, assess, anticipate, and aid in interventions while monitoring and coordinating patient health status and care. The vast majority of American adults own cell phones, and innovators in mHealth have been developing platform-agnostic, validated instruments for patientcentric realtime mobile data capture. With high technology access in place and mHealth tools emerging, the potential exists to revolutionize the way health services are delivered and experienced. To date, mHealth applications and devices have been used within the areas of epidemiology, general public health, and clinical trials. As mHealth can collect and analyze multifaceted data in near real time, these technologies may dramatically alter the speed with which evidence-based practice can be customized toward achieving the triple aim of high-quality care, improved out-comes, and lower costs. Challenges to achieving this revolution are seen in the complexities of integrating new technologies into the existing health service record systems, the needs of multiple and diverse healthcare stakeholders, and the research burden of producing high-quality evidence to demonstrate the clinical and economic outcomes enabled by mHealth.

This article reviews mHealth’s promises and challenges in the context of multiple US healthcare system stake-holders. We include specific commentary and examples for mHealth application in evidence-based diabetes care.INTRODUCTION

Digital health devices, as defined by Accenture, are “Internet-connected devices or software created for detection or treatment of a medical indication.”¹ Mobile health (mHealth) is a form of digital health that specifically includes the use of mobile technologies within healthcare. Data from mHealth can be used to inform, assess, anticipate, and aid in in-terventions while monitoring and coordinating patient health status and care. This use of real-time patient data will facilitate evidence-based practice, which is a research-informed, interdisciplinary ap-proach to coordinating clinical care.

This paper is the result of a multiple stakeholder issue panel on the promise of mHealth presented at the 19th Annual International Society of Pharmacoeconomics and Outcomes Research (ISPOR) congress. The authors reflect the panel membership encompassing the perspectives of US decision makers in the life science industry, health service providers, and regulatory and payer communities. This review was augmented with specific mHealth examples relevant to diabetes care. To the best of our knowledge, there has been no comprehensive survey of where and how mHealth is being used to execute health services research and deliver evidence-based practice. This paper includes expert opinion and information gathered from a non-systematic scan of the peer-reviewed literature, government reports, news releases, and intelligence gathered at or after our issue panel. No attempt was made to include a comprehensive list of citations and sources.

THE STATE OF THE ART: 2015

The field of mHealth is experiencing rapid growth, with the market expected to reach $26 billion by 2017.² Overall, the digital health industry is projected to save $30 billion from improvements in medication adherence, behavior modifications, and fewer emergency department visits within the US healthcare system.¹ Multiple stakeholders are engaged in realizing this promise, and a majority (59%) of physicians and insurers view mHealth as an integral piece of the future of healthcare, believing that widespread adoption of such technologies and applications is unavoidable.³

While some healthcare applications may require dedicated mobile devices, the more common forms of mHealth require only modest hardware augmentations or application downloads using general purpose devices. In fact, the adoption of the types of enabling technologies required for mHealth applications is currently nearly universal in the United States, where 90% of American adults own a cell phone, followed closely by access to tablets and personal computers.⁴ Building off of this broad technology access, innovators in the mHealth field have been engaged in developing location- and platform-agnostic validated instruments for patient-centric real-time data capture using mobile technology. Captured data can be integrated with existing patient and guidelines information, and system-atically analyzed via clinical algorithms, to provide timely and customized evi-dence-based care, often self-directed, to the patient.^5-6 This is noteworthy because many stakeholders believe that savings in the healthcare system can only be realized by taking providers out of the equation where possible and appropriate.

Arguably, the well-developed applica-tions of mHealth have been in epidemi-ology and general public health—often in remote areas where traditional means of disease monitoring, response, and in-person examinations are lacking.⁷ Diabetes is a disease area in which mHealth applications may be particularly relevant given the demographics, the public health challenge, and the fact that it is a condition that relies largely on self-management. This space is rife with potential for realizing the health efficiency promise, as $176 billion is spent annually on diabetes care, based on 2012 estimates.⁸ Further, mul-tiple researchers have documented an inverse relationship between diabetes pa-tient adherence to treatment and annual healthcare costs.^9,11

Increasing access to healthcare may highlight an even greater need for care coordination support; for instance, in the past year, researchers observed that 23% more Medicaid patients were diagnosed with diabetes in states that adopted Medicaid expansion as permitted under the Affordable Care Act, compared with 0.4% in states that did not expand Medicaid programs.¹⁰ The prevalence of diabetes is especially high in rural areas—estimated to be about 17% greater than in urban areas.¹¹ Consequently, remote monitoring technologies, telehealth, and other digital health tools are increasingly being used in the attempt to improve diabetes care in these communities^.12 Although some evi-dence exists that these tools are improv-ing health outcomes and reducing costs among persons with diabetes, limited data and uncertain findings persist. Further, research specifically calls for opti-mal care coordination in this population, which mHealth may support.^13,14

Multiple stakeholders may hold a key to unlocking the potential of mHealth to revolutionize evidence-based practice in critical areas such as diabetes care, including the life sciences industry, providers, regulators, payers, and policymakers. The following commentary will explore each of these stakeholder perspectives in terms of the mHealth promise and challenge as it relates to the US health system’s ambitious goals and evidence-based practice.

LIFE SCIENCES INDUSTRY

Evidence for Registration and Marketing

Life sciences industry stakeholders often consider using mHealth applications to augment the disease monitoring and tracking required in clinical trials. Much of the documentation associated with clinical trials can be transmitted by electronic, if not mobile, means. Automation facilitates the potential of lower-cost and higher-quality trials through enhanced data elements, superior accuracy, reduced risk of bias, and other technical improvements to research techniques. Although mHealth is still in its infancy, most fre-quently cited opportunities include in-corporation of patient reported outcomes (PROs) into pre-, peri-, and/or post clini-cal research (within or outside of regis-tries). Examples of PRO measures include symptoms, quality of life, health states, patient experience, patient satisfaction, perceived “value” of treatment, activity improvement or limitations, and therapy adherence tracking.¹⁵

FIGURE 1

Using the medical product development process (as adapted in), mHealth can be applied to research and marketing in at least 8 different aspects of a product life cycle.

In addition to registration requirements, industry stakeholders have a strong mandate to market the effective-ness of healthcare technologies (traditionally drugs, devices, and diagnostics) from both a clinical outcomes and and an economic point of view. Toward this end, longitudinal data series are be-ing created as additional “extensions” of current warehouse data sets become available through mobile device facilitated transmissions. In selected instances, data professionals are reporting “cyber” comparative effectiveness based on mobile transmissions of real-world evidence.¹⁶ As an example, in single data warehouses, researchers are compiling clinical and administrative data, clini-cal trials and research information, mo-lecular and biological data, and PROs. Professionals also report that they can connect processed information in the warehouse to clinical care outputs transmitted by mobile means to the patient. For instance, the algorithms developed to provide patients with diabetes or their providers with specific and relevant in-formation related to a device, treatment, or recommended intervention are based on patterns seen in large public data sets coupled with proprietary patient-level information facilitated by mHealth tools and data linkages.

Various applications of this real-time information are now emerging in the dia-betes care and population health space. For example, mHealth technologies and tools have been incorporated into patient wellness and disease prevention plans. These include smartphone apps that record food intake or exercise activ-ity as well as text message services with healthy living reminders.¹⁷ For patients with type 2 diabetes mellitus, a mobile application can be prescribed that contin-uously monitors the patient’s key health metrics and behaviors in real time and adapts prescription therapy intervention recommendations based on these data. This demonstrates a potential place for mHealth in patient care as well as the advancement of knowledge on best practices in abetting self-care. ¹⁸ Another mHealth device that measures the pa-tient’s key health metrics is the continuous glucose monitor. This device provides the capability for patients to actively and continuously monitor their own health status, and some have the added capability of sharing data with their caretakers and physicians through connected mobile applications, supporting care integra-tion (see stories, SP374, SP378).^19,20 With the growing innovations and possibilities in mHealth technologies, life science and other consumer product companies can explore opportunities to market solutions that inform, assess, anticipate, and aid in interventions with the purpose of deliv-ering evidence-based management, driving better outcomes, and delivering value to various customers.

PROVIDERS

Evidence-Based Practice Considerations

Healthcare providers are critical stake- holders in the integration of mHealth into evidence-based practice.

Health services research is a multidisciplinary field that examines patient access to health services, cost of care, and patient outcomes following care delivery. Evidence-based practice can take years to transition from academic health service research to widespread application in clinical guidelines. Leveraging mHealth’s access to real world data (data collected outside conven-tional randomized controlled trials or other clinical studies), researchers can identify gaps in population health and areas of focus for intervention; collect data (including resource use) during implementation in different settings (with and without intervention); moni-tor health outcomes and compliance to interventions over time as well as costs; and evaluate policy changes for screen-ing or other key questions. The use of mHealth has the potential to revolu-tionize the uptake of evidence-based medicine by providers due to rapid in-sights and the ability to integrate health services research analytics into care algorithms. For example, mHealth applica-tions can align prompts for patient be-havior modification and clinical decision making with best practice guidelines. These prompts hold the promise of optimizing patient outcomes and streamlining care services in near real time.

Information Systems and Privacy

Health system stakeholders often contend that the triple aim promises of high-quality care, improved outcomes, and lower costs will be unattainable until mHealth can integrate into the existing technology infrastructure. Taken literally, this challenge means that mHealth has to be interoperable with existing technologies such as electronic health records and similar tools—which it must be. The diversity of existing electronic hardware, software, and tele-communication systems in developed countries lead to interface and interop-erability difficulties within mHealth programs, as there is a seemingly unlimited number of platforms to choose from, each claiming a unique value-add to the patient care continuum. Despite the small-scale progress in developing platform-agnostic software to circumvent interface difficulties, interoperability will continue to pose challenges until more uniform information tech-nology standards, “open systems,” or other work-around solutions emerge.

As mHealth becomes more “personal,” challenges of privacy, security, and data control increase. At minimum, mHealth devices must protect patient privacy while retaining the data quality and accessibility for research. Patients must have control over their data, regardless of the device. An extensive discussion of patient privacy and data security is beyond the scope of this paper. Of note, the Office of the National Coordina-tor for Health Information Technology (ONC) released a Privacy and Security Framework in December 2008, outlin-ing principles that should be incorpo-rated into the mHealth architecture: openness and transparency; individual choice; collection, use, and disclosure limitation; data quality and integrity; safeguards; and accountability.²¹ Public and private sector entities alike will be challenged to consider all of these prin-ciples as they create mHealth systems that will assure patient privacy and data security.

REGULATORY

Frameworks for mHealth Technology Approval

The FDA regulatory framework for medical devices is applicable to many mHealth products. The FDA requires clearance (through a process known as the “510k” process) or pre-market approval (PMA) of mHealth products that meet the definition of a medical device. The FDA uses historical precedent and paradigms to approach the regulation of mHealth, while also incorporating concepts of “enforcement discretion” for low-risk products to encourage innovation in this area. The agency is grappling with regulating novel mHealth tech-nologies that do not fit squarely into the existing regulatory pathways, yet providing guidance documents to industry that communicate the FDA’s policy and enforcement priorities. Examples in-clude the FDA’s January 2015 guidance documents: “General Wellness: Policy for Low Risk Devices”²² and “Medical Device Accessories: Defining Accessories and Classification Pathway for New Acces-sory Types.^”23 Other movements include strides to “down-classify” or deregu-late products with low risk, such as the Medical Device Data Systems category of products that are now essentially unregulated after release of the FDA’s February 2015 final guidance.^24,25 Assessing the regulatory classification and the level of regulation that will apply to mHealth products will continue to pose challeng-es for the evolving FDA policy.

The safety and efficacy of regulated products continue to be the chief con-cern of the FDA. However, new models of regulation and adaptive regulatory policy will become necessary, as innovation in technology always seems to outpace the ability of a regulatory framework to adapt. The FDA has available an alterna-tive product approval pathway, referred to as the “de novo” process, whereby an innovative product that fits a “less than high risk” profile can be reviewed and approved without a predicate device yet without a full PMA. In order to deal with innovation, the FDA does at times take an approach that is customized to the product. This novel approach can be extremely frustrating to mHealth devel-opers due to a lack of predictability in evidentiary standards and the uncertain nature of the regulatory pathway.

Many issues around gaining regula-tory approval for mHealth products are the result of the FDA’s very limited experience with the broad portfolio of devices (many targeted at the consumer market), mobile applications, and other novel technologies.²⁶ A broad range of regulatory bodies are responsible for regulating mobile medical applications, including the Federal Trade Commission (FTC), the ONC, and the Federal Commu-nications Commission. And while the April 2014 FDASIA Health IT Report pro-posed a strategy for risk-based regulation of mHealth,²⁷ the recommendations are being implemented by each independent agency and full Federal coordination has not yet been achieved. As a result, there are circumstances where the jurisdiction and authority of one agency may overlap with (or compete with) another. For example, the FTC has required removal of some mobile medical applications from the market for false and misleading medical claims. Most recently, the FTC targeted a mobile app for which the manufacturer had made unsupported claims that it could help diagnose melanoma or assess consumers’ melanoma risk.²⁸

Frameworks for Research

In terms of data and research methods used in mHealth approvals, the FDA has also largely held to historical precedent on the type of information accepted for product clearance. As the field evolves, product developers may be challenged to convince the FDA that new data collection methods produce data that is standard-compliant. Of great interest to researchers and health providers may be whether any of the “randomized clinical trial alternatives” and data derived from them will be acceptable for product approval. Within or outside of clinical trials, the variability of file types, formats, and other particulars will con-tinue to pose challenges to integrating mHealth data with those collected by other means in health services research projects. Another challenge is the vol-ume of data and the ability for reference databases to be dynamic, collecting new data regularly and adjusting software analysis of the data accordingly. This dynamic nature of data and software algorithms presents a challenge to re-searchers and regulators alike.

PAYERS

Most healthcare stakeholders agree that payers will not advance coding, coverage, and payment policies in support of mHealth until higher-quality evidence shows clearer clinical utility. Research-ers are calling for stronger evidence for mHealth to support integration into public health programs, particularly in developing countries where the need for infusions of funding is also depen-dent on amassing the right evidence.²⁹ A systematic review and meta-analysis of mHealth interventions concluded that even well-designed studies of healthcare delivery process interventions showed only “modest benefits” and called for more high-quality trials to measure clinical outcomes.³⁰ This lack of real-world evidence is a common criticism of medi-cal device technologies, as the regulatory frameworks often dictate greater focus on efficacy and clinical evidence rather than the effectiveness outcomes payers demand. With the growth and increased demand for improved technology, further research will be required to support payer decision making.

In considering the path forward, the authors posit that evidence of the benefits of mHealth technologies in diabe-tes care and remote patient monitoring through telehealth technologies creates a precedent for reimbursement in the digital health space. As an example, CMS’ October 2014 physician fee schedule expands coverage of procedures that can be reimbursed for telehealth, specifically for activities such as remote patient monitoring, reviewing patient data, and live video consultations.31 Recently, a mobile prescription platform documented reduced average blood sugar levels in patients, which resulted in reimbursement for this specific plat-form from several self-insured compa-nies26; the platform developer is cur-rently in negotiations with pharmacy benefit managers.²¹

POLICY MAKERS

Even with the recent success of select diabetes care mHealth products in gaining FDA approval and payer reimburse-ment, when pundits look at the skills and expertise needed to develop end-to-end patient care solutions that can provide desired population-level impact on outcomes and costs, it is clear that bringing these solutions to market and integrating them into care requires a diverse team. A coordinating body has been proposed that could lead the way to overcome existing obstacles and pro-vide standards in an intense and unified fashion. Others disagree, claiming that even the federal government (if it were inclined to do so) could not play the role. As the field of mHealth applications evolves, it is essential that the multiple stakeholders work together to ensure a robust body of evidence that can inform health policies and reimbursement ap-propriate to these new tools, in addition to regulatory and clinical practice requirements. Most agree that a policy framework combined with increased cooperation among companies and po-litical or regulatory decision makers, among other stakeholders, is needed to realize mHealth’s potential. For high-burden diseases, we can imagine a call to action across multiple stakeholders that brings together the evidence, data systems, clinical policies, and collabora-tion frameworks (including patients) to accelerate integration of mHealth into evidence-based practice.

CONCLUSION

Reasons often cited as barriers to mHealth realizing its potential in the US healthcare system include:

1. Industry. This is a complex, tightly regulated market with various stake-holders and conflicting interests.

2. Providers. Clinical practice changes at a slow pace, and the application of information technology and focus on interoperability have been dispropor-tionately slow and small in healthcare in general.

3. Regulatory. The legal or regulatory framework surrounding mHealth services remains ill-defined.

4. Payers. There is limited evidence of mHealth’s benefits, hampering remuneration from third-party payers and causing some skepticism about the fraudulent substitution of mHealth for in-person provider services.

5. Policy makers. Incentives for multiple stakeholders to work effectively toward a common goal are lacking.

As more mHealth products come to market, challenges will undoubtedly arise in regulation, implementation, and adaptability of mHealth technologies. In pondering the future of mHealth in delivering evidence-based care, specifically in diabetes care, it is useful to think about its 4 aspects: regulatory pathway, software and hardware interoperability, payer policies, and reporting analytics standards.³⁵ On a more global level, mHealth cannot be seen as an end unto itself; it is a tool that can and should be used to coordinate or improve the delivery of higher-quality care for more people at an affordable price.

In summary, mHealth has great potential, particularly in diabetes, where there is a high clinical and cost burden, a need to accelerate efficient care in ur-ban areas, and a need to supplement care for patients in rural areas who have limited access to healthcare. It is imperative that stakeholders across the conatinuum, from consumers to providers to regulators to payers, collaborate to build the infrastructure and policies that will drive high-quality personalized healthcare and improve patient outcomes through evidence-based practice. Today, we begin to see some of this potential realized with emerging technologies such as mobile medication therapies. Diabetes stakeholders are pioneers in adopting mHealth into the entire patient care pathway, and one could argue that mHealth’s promise is poised to deliver in this complex disease area where patient engagement is critical to achieving better outcomes at a lower cost to the healthcare system.

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