Introduction and context: German policy innovation and international expertise
which, among other things, created a regulatory and reimbursement pathway for digital health applications in the German market.
The “Fast-Track” pathway
establishes market access for certain categories of digital health applications (known by their German acronym, DiGA)—namely those that meet the definition of lower risk medical devices and are primarily used by patients rather than physicians. When such products meet prespecified requirements related to safety, functionality, quality, data protection, data security, and interoperability, they are eligible for regulatory review and subsequent entry into a directory of regulated, reimbursable DiGA maintained by the German Federal Agency for Drugs and Medical Devices (BfArM).
Against this backdrop, the Digital Medicine Society (DiMe) and the Health Innovation Hub of the German Federal Ministry of Health (hih) convened a set of roundtable discussions in 2020 and 2021 to bring together experienced international experts in evidence generation for digital medicine products, broadly defined to include tools driven by high-quality hardware and software that support the practice of medicine broadly, including treatment, recovery, disease prevention, and health promotion for individuals and across populations. The expert roundtables included regulators and public servants, practicing physicians, health policy researchers, clinical trialists, digital medicine experts, epidemiologists, health-care economists, decision scientists, industry representatives from companies working on RWE (both for their own products as well as technical consultants to such companies), non-profit organisations in the health-care and entrepreneurship sectors, and representatives from both public and private health insurance providers.
Real-world data and real-world evidence
Both concepts are highly relevant to the new German regulation, which explicitly provides for their use. RWD can be collected through a variety of sources and tools as part of routine care or as digitally enabled add-ons—eg, using digital tools to collect patient-reported outcome measures (PROMs) or patient-reported experience measures (PREMs). Many PROMs use risk-adjusted instruments to turn qualitative symptoms into a numerical score.
This makes them actionable for triage to orient patients towards the most appropriate care pathway.
and has collaborated with academic researchers in its use,
and public–private partnerships have laid out a roadmap for developing study endpoints in real-world settings.
Other organisations have specifically developed patient-facing resources on the subject.
However, the use of RWE in Europe has been limited to a handful of promising, but still to a large extent exploratory, initiatives.
Digital health applications in Germany and beyond
which fully came into force in May, 2021) that have a primarily digital mechanism of action, do more than just collect data, are used primarily by the patient, and support “the recognition, monitoring, treatment or alleviation of diseases or the recognition, treatment or alleviation or compensation of injuries or disabilities.”
Furthermore, the Fast-Track allows for studies that are “clinical or epidemiological studies” as well as those “using methods from other scientific fields such as healthcare research, social research or behavioural research”,
laying a clear path for the presentation of evidence collected outside of traditional randomised controlled trials (RCTs).
In the case of reSET, real-world evidence observational studies have been used to examine efficacy and product usage. Other examples include the use of BlueStar (Welldoc; Columbia, MD, USA) for people with diabetes and EaseVRx (AppliedVR; Los Angeles, CA, USA) for treating pain.
The opportunity: novel approaches for evidence generation to support broad acceptance of digital health applications
yet have rarely been used in practice. This might, in part, be due to the real or perceived risks of regulatory uncertainty in pursuing such approaches. If RWE is facilitated by regulatory policy, but there is little or no track record for success, it is potentially riskier or costlier for a manufacturer to have an unsuccessful RWE attempt and then invest in a more traditional study design. Indeed, regulatory uncertainty has been discussed as a disadvantage for first-in-class products in traditional medical device markets in the past.
and the FDA’s recent Data Modernization Action Plan
focuses on creating the infrastructure necessary within the FDA to embrace new approaches to science-based regulation of evolving technologies by interacting with data in new ways. In addition, the FDA’s Digital Health Center of Excellence is working to “strategically advance science and evidence for digital health technologies that meets the needs of stakeholders.”
is focused on “facilitating the adoption and implementation of RWE in health care decision-making in Europe”, while specific initiatives such as Mobilise-D
are focusing on best practices for using RWE to generate digital mobility endpoints in chronic obstructive pulmonary disease, Parkinson’s disease, multiple sclerosis, hip fracture recovery, and heart failure. Germany, in particular, has also begun to collect RWD for certain pharmaceutical products (such as gene therapies) through newly established registries, indicating an ongoing interest in such initiatives.
What is needed? Articulating best practices and methodological challenges
Indeed, the International Consortium for Health Outcomes Measurement defines relevant outcomes as “the results of treatment that patients care about most…They’re real-world results, like physical functioning or level of pain”,
a reminder that focusing on the things that matter most to patients is a core goal of providing high-value health care.
The expansion of open access tools like this will create important public goods for RWE researchers in the context of app evaluation and beyond.
which is building a diverse database to inform thousands of studies on a multitude of health conditions, represent important steps towards building real-world datasets that include otherwise under-represented groups and account for heterogeneity in individuals and their environments. Such projects will support both equity and generalisability in the application of RWD to both digital health applications and medical innovation more broadly.
Fit for purpose
in nature, requiring new approaches to HTAs for digital products. The concept of dynamic HTAs also includes the possibilities of flexible reimbursement based on ongoing assessment of a technology’s performance.
), as well as operational challenges such as establishing digital formularies for applications (although early examples have emerged in the USA).
In addition, there must be a cultural shift in academic and industry research as well as within both the payer and provider communities to embrace rigorous use of RWE and its ability to generate high-quality evidence in certain contexts. Myriad examples of how RWE has been used for evidence generation in medical device
validation and indication expansion provide a number of cases of how transparency and methodological rigour can accompany RWE in practice.
In the case of externally controlled trials, such methods have already been shown to reduce inflated false positive error rates of standard single-arm trials in other settings, such as cancer research.
Public agencies have also begun to issue guidance on technical issues related to causal inference; for example, the National Institute for Health and Care Excellence in the UK has issued guidance on estimating treatment effects, with a particular focus on mitigating selection bias at the design stage of evidence generation.
Industry thought leaders must approach evidence generation with rigour and transparency, holding themselves to the highest possible standards. In many cases, there will be clear opportunities to take the lead from regulators in a number of areas where clear guidance and policy are already established. At the same time, regulators and other public bodies must continue to show leadership and more clearly communicate with product companies and investigators.
and managed access models, whereby patients can access therapeutics earlier while studies are still ongoing. Vitally, the patient’s perspective must be considered as new forms of data are collected and established for research purposes. Those collecting RWD must prioritise patient preferences regarding the information captured and considered during decision making. They must also ensure that data capture poses the least possible burden to patients and that patient data and information are appropriately protected.