Kathleen Hughes, MBA

In the United States, Medicare is the single largest purchaser of clinical laboratory tests, paying approximately $9.7 billion in 2012. Mostly driven by volume increases, Medicare spending on clinical laboratory testing rose by approximately 5.6% between 2003 and 2012.

 Coding changes for molecular diagnostic and novel tests in recent years have provided CMS and other payers with more clarity regarding which tests are actually being included on claims. This has led to a greater ability to focus on and scrutinize the medical necessity of testing. Recent legislative changes and forthcoming CMS regulation will soon fundamentally change the methodology Medicare uses to determine payment rates for clinical laboratory tests paid under the Medicare Clinical Laboratory Fee Schedule (CLFS). We review the past, present, and future of Medicare payment for clinical laboratory services, which will impact all payers and providers in coming years.  

Evolution of the Clinical Laboratory Fee Schedule 

Under Medicare Part B, CMS covers laboratory tests that are considered reasonable and necessary when they are furnished in a Medicare participating laboratory and ordered by a physician or qualified non-physician practitioner who is treating the patient. Clinical laboratory tests (excluding most pathology services) are paid for by Medicare under the CLFS, which was created under the Deficit Reduction Act of 1984 (DRA).

 The CLFS was established for laboratory tests on a regional, state, or carrier basis and was based on what local laboratories charged at that time. The DRA also mandated that the Consumer Price Index for All Urban Consumers (CPI-U) be used annually to adjust for inflation.

Following the DRA, the Consolidated Omnibus Budget Reconciliation Act of 1985 mandated that Congress establish national limit amounts (NLAs) on laboratory payments.

 Initially capped at 115% of the median carrier rate for each test, the NLA was subsequently lowered by Congress from 1988 to 1998 to 74% of the median carrier rate for each lab test.

 The payment amount for each test is determined based on the lowest of the provider’s charge, carrier rate, or NLA. In order to establish rates for new tests, CMS uses 1 of 2 methods: cross-walking and gap-filling. Cross-walking is used when a new test is determined to be clinically or technologically similar to existing test(s) on the fee schedule. The exact payment amount of the test(s) used in the cross-walk becomes the NLA for the new code. Gap-filling is used when no comparable test is available. Under this method, each Medicare Administrative Contractor (MAC) is instructed to ascertain a payment amount for its geographical area(s) for use in the first year; this subsequently serves as a benchmark for CMS to set an NLA based on the median of MAC rates.

Until recently, apart from various legislative actions—reducing payments across the entire CLFS, adjustments for inflation and productivity, and the addition of new codes&mdash;the CLFS has received little attention since the 1980s.

 However, Section 216 of the Protecting Access to Medicare Act of 2014 (PAMA) will significantly change how Medicare determines payment rates for clinical laboratory tests reimbursed for under the CLFS. All rates for tests on the CLFS will eventually be valued on market-based payment and volume data, somewhat similar to the Average Sales Price (ASP) methodology used for outpatient drugs and biologics. While we are still awaiting the details of implementation from CMS, the law will require applicable laboratories to report private payer (ie, commercial, Medicare Advantage, and Medicaid Managed Care) payment rates, including discounts, and volumes for existing tests beginning January 1, 2016, with those rates to be used to establish a weighted median for payment starting in January 2017. Collection of these data will be driven by unique Healthcare Common Procedural Coding System (HCPCS) codes, although many tests will map to the same code or be reported commonly with unlisted or unclassified codes. The law also allows for tests meeting certain criteria to obtain temporary codes until permanent codes are established. PAMA includes some protections to prevent reimbursement rates from dropping too low: for the years 2017 through 2019, payment amounts cannot be reduced by more than 10% per year; from 2020 through 2022, payments cannot be reduced by greater than 15% per year compared with the preceding year. Other adjustments, such as geographic, budget neutrality, or annual update adjustments, will no longer be applicable.

Following the initial collection and reporting for existing tests, the law separates new tests entering the market on or after January 2016 into 2 categories: advanced diagnostic laboratory tests (ADLTs) and non-advanced diagnostic laboratory tests (non-ADLTs).

Tests that meet the narrow definition of ADLT include those that are offered by a single laboratory and that are either:

 (a) an analysis of multiple biomarkers or proteins combined with a unique algorithm to yield a single patient-specific result;

(c) a test of another type based on other criteria established by the HHS.

To create financial incentives for access to the marketplace, ADLTs will be reimbursed based on the list charge for the first 3 quarters following market entry. At the beginning of the fourth quarter, CMS will transition reimbursement to a weighted median of private payer rates, with the potential for clawback if the list price is greater than 130% of the market-based fee. Reporting for ADLTs will be required on an annual basis, as opposed to every 3 years for non-ADLTs.

Non-ADLTs, which are new tests that do not meet the narrow definition of ADLT, currently account for a majority of the tests on the fee schedule. They will continue to be priced per the gap-fill and cross-walk methodologies until payment rates are established for the tests using private payer data.

In addition, PAMA creates several other provisions to assist with the overhaul of the fee schedule. PAMA calls for an Expert Advisory Panel to be created to comment on payment and coverage processes, and the secretary of HHS may designate 1 or more (not to exceed 4) MACs to establish coverage policies rather than leaving coverage decisions for clinical laboratory tests up to each individual Part A/B MAC. The Congress also directs the Government Accountability Office and HHS Office of the Inspector General to conduct studies to ensure that Medicare cost savings with the new payment methodologies do not harm beneficiary access or clinical decision making.

So, why does this matter to payers other than Medicare? Ultimately, the goal of PAMA is to lower laboratory reimbursement rates based on competitive pricing and contracting to improve Medicare cost savings. This is a significant paradigm shift from current practice where subsequent to tests being reimbursed for based on an antiquated Medicare fee schedule, Medicare often forms the basis of reimbursement for private payers. While PAMA will force the industry to prioritize contract negotiations with private payers, the specific approach by private payers and laboratories to adjust their future negotiating and contracting to account for the changes in Medicare payment rates remains undetermined. In the short term, this payment shift may create a volatile marketplace for laboratories and manufacturers due to the uncertainty of consistent or adequate payment, as well as create a hefty reporting burden on laboratories. Most likely, payment changes will vary by test code and we could see downward, neutral, and upward adjustments to future laboratory test payment rates.

The Rise of Molecular Testing Has Increased Payer Scrutiny

In the current healthcare environment, diagnostic developers face ever-growing demands by payers to establish the value of their product to gain favorable coverage. With the rise in the number of multipanel genetic tests and the introduction of newer technologies such as next-generation sequencing (NGS), both public and private payers have established processes and criteria to aid in making coverage decisions. Establishing the clinical utility, namely, changes in clinical decision making and outcomes based on the information obtained by the test, has been a significant hurdle faced by industry stakeholders when introducing new diagnostics into the market.

Under Medicare, one requirement for determining that a diagnostic test is medically reasonable and necessary is that the results of the test will change the management of a beneficiary’s condition. This could be shown by changing treatment pathways or determining eligibility for a unique drug or drugs based on the test’s results. While there have been several Medicare Evidence Development & Coverage Advisory Committee meetings on molecular diagnostics, coverage determinations have largely been developed at the local level. In 2011, Palmetto GBA (Palmetto), the Part A/B MAC for Jurisdiction 11 (North Carolina, South Carolina, Virginia, and West Virginia), launched the MolDX Program, which was designed to produce evidence-based coverage policies for molecular diagnostic tests. As part of the new program, all manufacturers and laboratories seeking coverage for their tests must provide Palmetto with robust clinical evidence demonstrating not only the analytic validity but also the clinical validity and utility of their test. To date, Palmetto has issued 18 local coverage determinations (LCDs) detailing limitations and indications for coverage of certain molecular tests, although they have also made de facto coverage decisions for a large number of analyte-specific molecular tests outside of the LCD process.

 While the Palmetto MolDX Program is only active in certain MAC jurisdictions at present, it could grow to the national level in the future, particularly with the new authority delegated to the secretary of HHS under PAMA. However, CMS has not yet indicated that it will move to a consolidated coverage process for laboratory tests.

Private payers are also active today in determining whether molecular testing used in the clinic is experimental, investigational, or medically necessary. While most large private payers have internal health technology assessment committees, the levels of focus and expertise in the technical aspects of complex testing methodologies vary. While Medicare policies are relied on as a basis for determining private payer coverage policies for certain tests, such as in oncology for companion diagnostics, many tests are more appropriate for populations outside of the Medicare population, or are excluded by statute from the Medicare program (eg, susceptibility or screening tests).

 This means that payers will need to evaluate coverage and payment on their own terms for a wide range of new tests. Payers generate coverage policies based on peer-reviewed literature, external technology assessments, and evidence-based guidelines produced by medical specialty societies or other groups such as the National Comprehensive Cancer Network.

As payers demand robust evidence demonstrating the clinical utility of diagnostic testing, it is critical that manufacturers provide adequate evidence on how changes in clinical action based on test results lead to an improvement in patient outcomes.

What Does the Future Hold for Laboratory Test Reimbursement?

The number of clinical laboratory tests—in particular molecular diagnostics&mdash;on the market will continue to rise, and that growth will impact the various players in the healthcare industry in several ways. Payers will likely see a continued steady increase in the volume of claims entering their systems as well as the sustained need for medical review of novel diagnostics. Due to increased payer scrutiny and efforts to control costs, as well as greater market competition, manufacturers and laboratories will need to be prepared with higher levels of evidence, in particular strong clinical utility. Manufacturers and clinical laboratories should invest in establishing this higher bar of evidence during test development and prepare for pricing and reimbursement pressures to continue, especially in the time leading up to and after the changes in Medicare payment rates, outlined in PAMA, take effect in 2017.

As private payer rates will drive future Medicare reimbursement, contracting and price negotiation will form an increasingly important piece of the puzzle for both laboratory providers and payers. If pricing pressures are too great to bear for some smaller laboratories, we could see further consolidation or narrow laboratory networks come into play even more than today. In addition, we will see a greater payer focus on managing laboratory testing benefits through shifts in cost-sharing or benefit management services, such as United Healthcare’s use of Beacon Laboratory Benefit Solutions, Inc (Beacon LBS) for commercial members in Florida.

 It is uncertain if the program will be expanded geographically or if other payers will look to manage laboratory benefits through a third party—as many do today for prescription drugs through pharmacy benefit managers&mdash;but the potential exists.

Laboratory testing is often a gatekeeper to, or influencer of, other healthcare services. As we await regulations around the Medicare payment changes under PAMA, and as laboratory testing continues to rise as an area of focus for payers and providers, the next 2 years will prove crucial for testing. All stakeholders should monitor the ongoing changes to Medicare payment and coverage for clinical laboratory testing, as the impact will be felt throughout the healthcare industry.

Adam Borden, MHA, is director, Avalere Health, LLC; Danielle Showalter, MPH, is senior manager, Avalere Health, LLC; Geoffrey Storchan, PhD, is manager, Avalere Health, LLC; Kathleen Hughes, MBA, is vice president, Avalere Health, LLC.

1.      Medicare Payment Advisory Committee. A data book: health care spending and the Medicare program. http://www.medpac.gov/documents/publications/jun14databookentirereport.pdf?sfvrsn=1. Published June 2014. Accessed June 19, 2015.

2.      H.R.4170 - Deficit Reduction Act of 1984. Congress.gov website. https://www.congress.gov/bill/98th-congress/house-bill/4170. Accessed July 1, 2015.

3.      H.R.3128 - Consolidated Omnibus Budget Reconciliation Act of 1985. Congress.gov website. https://www.congress.gov/bill/99th-congress/house-bill/3128. Accessed July 1, 2015.

4.      Social Security Act §1833(h)(4)(B). CMS website. http://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNMattersArticles/Downloads/MM8132.pdf. Accessed July 1, 2015.

5.      42 CFR § 414.508 Payment for a new clinical diagnostic laboratory test. Cornell University Law School website. https://www.law.cornell.edu/cfr/text/42/414.508. Accessed July 1, 2015.

6.      Medicare Prescription Drug, Improvement, and Modernization Act of 2003. U.S. Government Publishing Office website. http://www.gpo.gov/fdsys/pkg/PLAW-108publ173/pdf/PLAW-108publ173.pdf. Accessed July 1, 2015.

7.      Medicare Improvements for Patients and Providers Act of 2008. CMS website. http://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNMattersArticles/downloads/MM6042.pdf. Accessed July 1, 2015.

8.      Protecting Access to Medicare Act of 2014. Congress.gov website. https://www.congress.gov/bill/113th-congress/house-bill/4302/text. Accessed June 17, 2015.

9.      MolDX Program website. http://www.palmettogba.com/palmetto/MolDX.nsf/DocsCatHome/MolDx. Accessed June 23, 2015.

10.      Social Security Act §1862(a). Social Security Administration website. http://www.ssa.gov/OP_Home/ssact/title18/1862.htm. Accessed July 1, 2015.

11.       UnitedHealthcare Laboratory Benefit Management Program. UnitedHealthcare website. https://www.unitedhealthcareonline.com/b2c/CmaAction.do?channelId=9cc7b96891e22410VgnVCM2000002a4ab10a____ . Accessed June 24, 2015.

Articles

The basis for determining Medicare payment rates for clinical diagnostic laboratory tests is changing. These changes will be important for all payers and providers to follow for future reimbursement and contract negotiations. 

The Diagnosis for Diagnostics: Changes to Medicare Payment and Coverage of Clinical Laboratory Tests 

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.

Digital health devices, as defined by Accenture, are “Internet-connected devices or software created for detection or treatment of a medical indication.”

 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 field of mHealth is experiencing rapid growth, with the market expected to reach $26 billion by 2017.

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.

 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.

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.

 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.

 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.

 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.

Further, mul-tiple researchers have documented an inverse relationship between diabetes pa-tient adherence to treatment and annual healthcare costs.

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.

 The prevalence of diabetes is especially high in rural areas—estimated to be about 17% greater than in urban areas.

 Consequently, remote monitoring technologies, telehealth, and other digital health tools are increasingly being used in the attempt to improve diabetes care in these communities

 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.

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 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.

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.

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.

 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. 

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).

 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.

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.

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.

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.

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”

and “Medical Device Accessories: Defining Accessories and Classification Pathway for New Acces-sory Types.

 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.

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.

 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,

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.

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.

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.

 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.

 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.

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.

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.

 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.

1. Cornstock J. FDA-cleared digital health devices to save healthcare $100B by 2018. Mobihealthnews website. http://mobihealthnews. com/44107/fda-cleared-digital-health-devices-to-save-healthcare-100b-by-2018/. Published June 4, 2015. Accessed June 19, 2015.

2. Terry K. Mobile health market to reach $26B by 2017. InformationWeek website. http://www. informationweek.com/mobile/mobile-health-market-to-reach-$26b-by-2017/d/d-id/1110964? Published July 30, 2013. Accessed June 19, 2015.

3. Emerging mHealth: paths for growth. PwC web-site. http://www.pwc.com/en_GX/gx/health-care/mhealth/assets/pwc-emerging-mhealth-full. pdf. Published 2014. Accessed June 2015.

4. Mobile technology fact sheet. PewResearch-Center website. http://www.pewinternet.org/fact-sheets/mobile-technology-fact-sheet/. Published 2014. Accessed June 2015.

5. Samsa G. Webinar: What is “learning health care”? Academy Health website. http://www. academyhealth.org/Training/ResourceDetail. cfm?ItemNumber=10101. Broadcast November 5, 2012. Accessed June 2015.

6. Hirsch B. Webinar: Chordoma Patient Portal (Potential Applications of mHealth tools; mHealth to support epidemiology and registry stud-ies). Academy Health website. http://www. academyhealth.org/Training/ResourceDetail. cfm?ItemNumber=10440. Broadcast January 10, 2013. Accessed June 2015.

Data from mHealth can inform, assess, anticipate, and aid in interventions while monitoring and coordinating patient health status and care. 

Kathleen Hughes, MBA

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