The FDA recently released guidance documents on select provisions of the 21st Century Cures Act (the Cures Act). Digital health products and services providers are most concerned about guidance on the use of clinical decision support (CDS) and medical software. For healthcare organizations working with these fast-changing technologies and constantly pushing digital innovation, federal guidance that hints at changing the regulation of their pioneering efforts may send up warning flags.
However, there is little cause for alarm. The FDA regularly issues clarification on how it is going to enforce statutes and regulations to provide useful interpretive information to the industry. Although the FDA digital health guidance documents are not regulation (they are non-binding, informational communications), digital health executives and developers can still use the FDA’s guidance to understand the current approach it is taking toward regulation, and how this approach impacts CDS and medical software development, as well as provider workflows.
Healthcare organizations use CDS to reduce errors and improve efficiency, standardization, and cost savings. The FDA guidance on CDS (the CDS Guidance) describes what types of software tools the FDA considers to be CDS, and which CDS is excluded from FDA regulation as a medical device.
The CDS Guidance states that software functions must meet the following three criteria to be considered CDS:
Having met these three criteria, CDS must then meet a fourth criterion to fall outside the FDA’s definition of a medical device and be exempt from regulation:
The CDS Guidance provides examples of CDS software functions that do and do not meet these criteria. Two key elements are provided in the guidance to further define the criteria for exemption:
These two elements beg the question: what about using algorithms and machine learning as the basis for CDS recommendations?
Healthcare machine learning models produce information that improves patient care. This information may not always be based on published literature, but its reliability is correlated to significant historical patient data and actual outcomes. Providers will exercise clinical judgment before taking any action on the output of machine learning, which is often in the form of recommendations, alerts, and risk prioritizations.
What’s concerning is that an algorithm could operate on unspecified health data within a “black box” and deliver a result, the basis of which a provider might not understand. The FDA is appropriately addressing this issue by requiring disclosure of the inputs used in order for software to be excluded from regulation. Health Catalyst agrees with the principle that any applications using algorithms should have functionality that allows users to view the input features and variables, including the clinical guidelines or findings that helped shape the predictive model.
It is also important to note that a clinical intermediary will always be part of machine learning-enabled CDS to impose judgement and connect any recommendations (produced by the machine learning model) to relevant clinical guidelines.
Health Catalyst and other organizations will seek clarification from the FDA on the use of algorithms and machine learning as the basis for providing CDS recommendations, in lieu of, or in addition to, publicly available clinical guidelines.
The use of the term “providing recommendations” in the CDS Guidance could also be clarified. Many CDS systems, including Health Catalyst’s analytics applications, provide prioritizations and alerts, but they don’t always recommend a course of action. The FDA’s CDS Guidance only addresses recommendations, but innovative healthcare systems may need more nuance to address their advanced clinical environments. The FDA is expected to review and respond to the public comments when it issues its final guidance documents.
The CDS Guidance provides informative examples of CDS functionality that fall into the following three categories:
Where the FDA “does not intend to enforce compliance” means that the decision to not regulate non-medical-device software is based on the FDA’s discretion. In the future, the FDA could modify its policy, which could result in a more significant impact on vendors that use CDS with machine learning functionality. While the FDA’s approach introduces uncertainty, it is our opinion that, because there have been enough policies issued over a sustained period, radical shifts in enforcement policy are unlikely.
The CDS Guidance also addresses patient decision support (PDS) software, regarding functionality for use by patients rather than clinicians. This guidance has similar criteria to CDS and indicates that PDS will not be regulated as a device. The FDA appears to be drawing the distinction because PDS likely applies to a different audience (patients). For example, PDS guidance applies to patient-accessed modules and functions within Health Catalyst’s Care Coordination application in the Care Management suite.
The FDA guidance on changes to existing medical software policies (the Medical Software Guidance) provides clarification on policy changes made by the Cures Act. The Medical Software Guidance builds upon several previous FDA guidance documents and clarifies that software functions classified into any of four areas are exempt from regulation as a medical device:
The Cures Act had already exempted these software functions, so this guidance simply provides more detail related to the Cures Act provisions, as well as prior guidance on mobile medical applications. Software, like Health Catalyst’s team-based fitness and wellness application, falls under this guidance, which excludes software for maintaining or encouraging a healthy lifestyle, and which is unrelated to the diagnosis, cure, mitigation, prevention, or treatment of a disease or condition.
As long as software vendors comply with the FDA guidance, any cost impact should be minimal, provided that their software does not seem to fall within a category that the FDA indicates is a medical device. If vendors aren’t already providing transparency through their applications, then workflow processes could be impacted as they move from a black box approach to one in which data inputs used by algorithms are disclosed to users. Providing these disclosures could potentially involve more time and effort by providers because it could add an input review phase with an internal clinician panel, although this would be discretionary.
The CDS Guidance and Medical Software Guidance were issued in draft form to allow the FDA to consider public comments. While possible, it is not likely that the exemptions will change significantly in the final documents. Vendors might expect to see clarification on how to disclose the basis for recommendations or conclusions derived by CDS, and where machine learning fits into these disclosures. The FDA will also likely include a few more examples of what functionality it considers to be exempt from, or subject to, regulation when it issues its final guidance later in 2018.
There are no major surprises with the new FDA guidance documents in terms of what will be regulated or not. The CDS Guidance reassures practitioners that CDS functions that meet the non-device criteria will either be excluded from regulation or the FDA will not enforce regulation. This approach can help promote innovation by ensuring a quicker time to market—and value—for developers and CDS system users.
In its own words, the FDA’s intent is to foster, not inhibit, innovation, and to encourage the development of tools that can help people be more informed about their health. Though these documents don’t establish rules, they should be taken seriously because they contain useful information about how the FDA will enforce its rules, and how it interprets the Cures Act.
The industry response to these new guidance documents should be positive. The policies provide appropriate oversight, promote software transparency, and let digital health developers “know where they stand relative to the FDA’s regulatory framework.” Most importantly, the guidance supports the success of vendors and healthcare organizations as they continue working to improve the features and functionality of their CDS systems and medical software.
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