FDA Outlines Updated Approach to Regulating Digital Health Technologies

On December 8, FDA addressed the agency’s evolving approach to digital health by issuing two new draft guidance documents: “Clinical and Patient Decision Support Software” (the “CDS Draft Guidance”) and “Changes to Existing Medical Software Policies Resulting From Section 3060 of the 21st Century Cures Act” (the “Software Policies Draft Guidance”). These draft guidances announce the agency’s initial interpretation of the health software provisions enacted as part of last year’s 21st Century Cures Act (the “Cures Act”).

Given the rapid pace of digital health innovation across the life sciences, technology and health care sectors, FDA guidance on these topics is critical. Here are a few key takeaways from the draft guidances:

  • FDA’s initial interpretation of the Cures Act provision related to clinical decision support (CDS) software may lead to a fairly narrow carve-out—in other words, many cutting-edge CDS software functions could remain subject to FDA regulation.
  • FDA’s draft guidances do not directly address dynamic digital health solutions, such as those that incorporate machine learning, artificial intelligence (AI), or blockchain.
  • FDA has proposed an enforcement discretion approach for decision support software aimed at patients that generally parallels the regulatory approach for CDS software aimed at clinicians, even though patient decision software was not addressed directly in the Cures Act.
  • Consistent with the Cures Act, FDA’s draft guidances reflect that many of the software functions that were previously subject to FDA enforcement discretion (i.e., not actively regulated as devices) no longer meet the definition of “device.”
  • Significant for pharmaceutical companies, CDER joined one of the draft guidances, and that draft guidance makes clear that other FDA requirements may apply to digital health products disseminated by or on behalf of a drug sponsor beyond those outlined in the draft guidance.

FDA’s regulatory approach has a significant impact on the investment in and development of digital health solutions across the digital health ecosystem. Stakeholders should consider submitting comments to the agency to help shape the direction of FDA’s final guidances on these topics.

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The Evolving FDA and EU Equivalent Regulation of Digital Health: A Device Perspective

On November 14, lawyers from Teva Pharmaceuticals and Covington & Burling discussed digital health innovation from a medical device regulation perspective in the U.S. and the EU. The presentation by Rachel Turow, Executive Counsel – Regulatory Law, Teva Pharmaceuticals, and Grant Castle, Scott Danzis, Sarah Cowlishaw, and Christina Kuhn of Covington, covered topics such as which digital health products the FDA’s Center for Devices and Radiological Health (CDRH) regulates, how “device” is defined by the recent 21st Century Cures Act, and the relationship between medical devices and software under EU law. The group also discussed how digital health associated with pharmaceuticals may implicate regulatory considerations under FDA’s drug authorities — a topic to be more fully explored at an upcoming Covington Digital Health webinar in early 2018.

Some of the key takeaways the panel discussed are:

  • Understanding a digital health product’s intended uses and functionalities is critical to whether product will be regulated.
  • The CDRH’s approach to digital health is evolving, and CDRH has adopted a more flexible approach to digital health as compared to other product areas.
  • The FDA isn’t the only regulator to consider — other regulators such as FTC, CPSC, state AGs, and DOJ are becoming more engaged in this area.
  • Companies marketing or expecting to market products in the EU should design new software medical devices with the EU’s Medical Device Regulation in mind.

This is the second of a series of webinars Covington is offering to help companies navigate the laws, regulations, and policies that govern the evolving Digital Health sector. The webinars are aimed at:

  • Legal, regulatory, and policy teams at life sciences and technology companies involved in the development and marketing of digital health technologies
  • Legal, regulatory, and policy professionals with backgrounds in the “traditional” pharma-biotech and medical device space, who are looking to move into the digital health space

If you would like to view a recording of this one hour webinar, please contact Jordyn Pedersen at jpedersen@cov.com.

 

Digital Health Checkup (Bonus): Product Liability and Insurance Coverage

Digital Health

In this bonus edition of our checkup series, Covington’s global cross-practice Digital Health team considers some additional key questions about product liability and insurance coverage that companies across the life sciences and technology sectors should be asking as they seek to fit together the regulatory and commercial pieces of the complex digital health puzzle.

1. What are the key questions when crafting warnings and disclosures?

If your product is regulated, your warnings and disclosures will need to comply with any relevant regulations. In the case of a product not regulated by the FDA or equivalent regulatory body, first consider how your warnings and disclosures will be incorporated into the use of the product.

Some disclosures, like an explanation of the data source used by software, may fit best in terms and conditions that a user sees before using the product. Key warnings, however, may be more appropriately placed as part of the user experience.

Example: A warning that patients should consult their doctors if necessary may need to be placed in proximity to specific medical content.

Best Practice: Consider your intended audience: are you writing warnings for doctors, patients, or institutions? The appropriate types of disclosures will vary across populations. Patient-directed warnings may also need to be written in simplified language.

Best Practice: Consider whether it is appropriate for your product to have users to accept or otherwise be required to agree to the warnings and disclosures.

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U.S. FCC Repeals 2015 Net Neutrality Rules; Impact on Digital Health Solutions Debated

Today, as expected, the U.S. Federal Communications Commission (“FCC”) adopted an order repealing the agency’s 2015 net neutrality rules and changing the legal framework that governs Internet Service Providers (“ISPs”). The vote split along party lines, with the agency’s three Republicans voting in favor and its two Democrats dissenting.

Once today’s order goes into effect, ISPs will no longer be subject to rules or FCC oversight as to what they can or cannot do in delivering online traffic to and from consumers at home and on their mobile devices. The FCC did, however, retain a requirement that ISPs publicly disclose whether they engage in certain practices, such as accepting consideration in exchange for prioritizing some sites and services over others (a practice known as “paid prioritization”).

Life sciences, technology, and health care companies developing and marketing digital health solutions should be aware that some supporters of the FCC’s action have argued that the repeal of restrictions on paid prioritization will allow ISPs to partner with digital health applications for optimized network performance within the U.S. As an example, the FCC has cited a commenter expressing the view that paid prioritization could provide improved access to “remote health-care monitoring” and “health service delivery by mobile networks.” Opponents, however, claim that the FCC’s action will allow ISPs to act in ways that could limit the ability of some online applications—whether in digital health or other sectors—to survive and thrive online.

The final text of the FCC’s order is not yet available, but it is not expected to deviate significantly from a draft released last month. Opponents are expected to challenge it in court in early 2018, and debates over net neutrality will continue in Congress as well. Covington’s Digital Health team will continue to follow these FCC developments given the potential impact on certain digital health products and services.

Digital Health Checkup (Part Three): Key Questions About AI, Data Privacy, and Cybersecurity

In the third installment of our series, Covington’s global cross-practice Digital Health team considers some additional key questions about Artificial Intelligence (AI), data privacy, and cybersecurity that companies across the life sciences and technology sectors should be asking to address the regulatory and commercial pieces of the complex digital health puzzle.

AI, Data Privacy, and Cybersecurity

1. Which data privacy and security rules apply?
There currently is not a specific law or regulation governing the collection, use, or disclosure of data for AI or the cybersecurity of AI technologies. As a result, digital health companies must assess how existing privacy and security rules will be interpreted and applied in the context of AI.

The applicable laws and regulations governing data privacy and security depend on a variety of criteria, including where you are located and where you are offering the AI technology.

Here are a few regional considerations for AI in the U.S. and data privacy and cybersecurity in the EU and China:

United States
Because large datasets of information typically are necessary to train and test AI technologies, digital health companies that are developing or utilizing AI should consider whether individuals receive adequate notice and are provided appropriate choices over how their information is collected, used, and shared for such purposes. For example, a person might have different expectations about how their information is being collected and used depending on whether they are communicating with a digital health AI assistant provided by a hospital, pharmaceutical company, or running shoe manufacturer. Consequently, providers of such technologies should consider clearly and prominently explaining who is operating the assistant and the operator’s information practices.

Depending on whether and to what extent you have a business relationship with or obtain information from a healthcare provider or other covered entity in order to develop or implement your AI, you may need to comply with the more specific privacy and data security requirements contained in HIPPA and state medical privacy laws in California and Texas.

Similarly, the collection and use of genetic information, biometric identifiers and information (based, for example, on facial recognition or scans, fingerprints, or voiceprints) trigger a patchwork of other federal and state laws.

The United States also regulates the security of connected products and the networks and systems on which they rely. The FTC historically has been the primary enforcement agency responsible for ensuring the “reasonableness” of product, system, network, and data security under Section 5 of the FTC Act. The FDA also has published pre- and post-market guidance on cybersecurity expectations with respect to connected medical devices. Both the FTC and the FDA recognize that responsibility for ensuring consumers against cyber threats applies to the entire product lifecycle—from initial design through vulnerability assessments, patching, and end-of-life considerations.

European Union
If you have a presence in the EU, offer services or goods there, or monitor the behavior of individuals there, you may be subject to the new EU General Data Protection Regulation (“GDPR”; see our checklist on this topic)—a complex law backed by fines of up to 4 percent of global annual turnover (or €20,000,000), obligations to appoint local representatives and data protection officers, etc. It contains strict limits and conditions on the collection, use, and sharing of health data, genetic data, and biometric data, and requires extensive internal policies, procedures, and even the building of “data portability” features allowing individuals to export their data to rival services.

The EU’s “cookie rule” also prohibits most storage of data to, or reading of data from, Internet-connected devices without prior informed consent. Finally, many EU countries also have confidentiality rules that further restrict the collection and use of patient data, plus detailed health cybersecurity rules, such as a French law that requires all services hosting patient data to have first obtained Ministry of Health accreditation.

China
Healthcare data is also considered sensitive in China, and will soon be subject to more stringent requirements under the Information Security Technology – Personal Information Security Specification, in addition to existing data protection and cybersecurity obligations imposed by China’s Cybersecurity Law (see our recent post on this topic).

China also has regulations governing medical records and population health information, such as the Medical Institution Medical Records Administrative Rules and the Administrative Measures for Population Health Information.

Best Practice: Identify the jurisdictions that you operate in or offer your services, and those that present the highest risk to your company. Then assess what data you collect and the purposes for which you use it to identify which specific laws and regulations apply.

2. How do you ensure that you have the necessary rights to collect, use, and disclose data in connection with your AI technologies?
When collecting information directly from users of the AI, you should be transparent about the types of information you collect, how you use it, whether and to whom you disclose it, and how you protect it. It is critical that these disclosures be accurate and include all material information.

When developing, training, and testing AI technologies, companies also look to existing data sources. If the company is using personal data that it previously collected, it should consider whether individuals had sufficient notice that the information would be used for purposes of developing and improving new kinds of digital health solutions. When obtaining this information from third-party sources, the company should consider contractual representations and warranties that ensure all necessary notices, consents, rights, and permissions were provided and obtained to permit the company to use the data as contemplated in the agreement.

In some cases, it also might be appropriate to provide users choice over how their information is collected, used, and shared. In the EU, for example, the GDPR outlaws consent statements that are buried in small print: for digital health purposes, consent will need to be clear, granular, and specifically opted into in order to be valid. In the EU, regulators also are starting to hold recipients liable for inadequate due diligence—merely obtaining contractual assurances from data sources may not be enough.

Best Practice: Notice typically is provided through a privacy policy, but the interactive nature of AI technologies mean that innovative just-in-time disclosures and privacy choices might be possible.

3. What are the fairness and ethical considerations for AI and data analytics in digital health solutions?
To maximize the potential of artificial intelligence and big data analytics, it is important to ensure that the data sets that are used to train AI algorithms and engage in big data analytics are reliable, representative, and fair.

Example: Some diseases disproportionately impact specific populations. If the data sets used to train the AI underlying your digital health offerings are not representative of these populations, the AI might not be effective. It also is critical that the data sets underlying your AI and data analytics are secured against unauthorized access or misuse.

In its report on “big data,” the FTC cautions companies to consider whether data sets:

  • are representative;
  • whether data models account for biases;
  • whether predictions based on big data are accurate; and
  • whether reliance on big data raises other ethical or fairness concerns.

Best Practice: Some companies are forming internal committees to ensure that their use of AI and data analytics is ethical and fair.

The EU also has detailed privacy rules impacting big data and AI. For instance, it grants all individuals a right to human review of fully automated decisions based on analysis of their data—and in many cases prohibits the basing of such decisions on sensitive data, such as their health data, ethnicity, political opinions, genetics, etc. It also outlaws any disclosure or secondary use of data originally collected for a different purpose, unless certain conditions are met, including that it meets the conditions to be considered “compatible” with the original uses (e.g., the new use must be within the individuals’ reasonable expectations).

Of note, if the digital health solution is potentially regulated by the FDA or an equivalent regulatory body, there may be additional pre-market and post-market considerations (e.g., validation of clinical recommendations using AI, adverse event reporting; see our earlier checkup on this topic).

Pharmaceutical Digital Health Innovators Take Note: FDA Public Hearing on an Innovative Approach to Devices Referencing Drugs

On November 16, 2017, the Food and Drug Administration (“FDA” or the “Agency”) will hold a public hearing on a proposed approach for sponsors seeking to market devices referencing drugs (“DRDs”) when the drug sponsor does not wish to collaborate with the sponsor of the device. FDA will accept comments to the docket until January 15, 2018. Continue Reading

Three Questions You Need to Ask When Negotiating Digital Health Deals

According to a distinguished panel of lawyers from MSD and Covington & Burling, companies involved in Digital Health deals need to ask themselves the following questions:

  • What data is required to develop and deliver the Digital Health solution, and does your company have sufficient expertise in-house to analyze the data?
  • What happens if your technology vendor becomes unable or unwilling to support or further develop software used in your Digital Health solution?
  • How do you structure a contract to develop and deliver a Digital Health service when the ultimate composition of the service, the customer base, and reimbursement model are all uncertain at the outset?

David Boyko, Division Counsel for MSD’s Healthcare Services and Solutions, and Nigel Howard, Daniel Pavin, and David Wildman of Covington addressed these key issues in an October 10, 2017 webinar on “Commercial and IT issues in Digital Health Deals.” This is the first of a series of webinars Covington is offering to help companies navigate the laws, regulations, and policies that govern the evolving Digital Health sector. These webinars are aimed at:

  • Legal and business teams involved in structuring and negotiating arrangements in the digital health space.
  • Legal and business teams with a background in “traditional” pharma-biotech collaborations who are looking to move into the digital health space.

If you would like to view a recording of this one hour webinar, please contact Jordyn Pedersen at jpedersen@cov.com.

Top Tips and Traps for Cyber Insurance Buyers

Although the National Cybersecurity Awareness Month of October has come to a close, it is not too late for corporate counsel and risk managers to be thinking about cyber-risk insurance — an increasingly essential tool in the enterprise risk management toolkit. But a prospective policyholder purchasing cyber insurance for the first time may be hard put to understand what coverage the insurer is selling and whether that coverage is a proper fit for its own risk profile. With little standardization among cyber policies’ wordings, confusing labels for their covered perils, and little interpretive guidance from case law to date, a cyber insurance buyer trying to evaluate a new proposed policy may hardly know where to focus first.

After pursuing coverage for historically major cyber breaches and analyzing scores of cyber insurance forms over the past 15 years, we suggest the following issues as a starting point for any cyber policy review: Continue Reading

CHMP Adopts Guideline on Genomic Sampling and Management of Genomic Data

On 14 September 2017, the Committee for Human Medicinal Products (“CHMP”) of the European Medicines Agency adopted ICH Guideline E18 (the “Guideline”) on genomic sampling and the management of genomic data.  The Guideline takes effect on 28 February 2018.

The International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (“ICH”) developed the Guideline in acknowledgement of the growing awareness of, and interest in, genomic data generated from clinical studies.  The ICH suggested that the absence of a harmonized guideline made it more difficult to conduct genomic research consistently in global studies.  The fact that the CHMP has adopted the Guideline means that EU guidance on this subject is now aligned with the ICH standard.

The Guideline provides general principles for the collection and handling of genomic samples and management of genomic data.  It also affirms broader principles, such as the need for informed consent and the protection of subjects’ privacy etc.  The Guideline applies to both interventional and non-interventional clinical studies, irrespective of when the genomic research is carried out and whether it was envisaged in the study protocol.  The ICH/CHMP intend the Guideline to be interpreted in accordance with the law and policies in each jurisdiction where genomic research takes place. Continue Reading

Digital Health Checkup (Part Two): Key Commercial Questions When Contracting for Digital Health Solutions

Digital Health

In the second of a three-part series, Covington’s global cross-practice Digital Health team considers some additional key questions that companies across the life sciences, technology, and communications industries should be asking as they seek to fit together the regulatory and commercial pieces of the complex digital health puzzle.

Key Commercial Questions When
Contracting for Digital Health Solutions

1. Will you own or have rights to use the data that is collected and generated, and any insights, models, and algorithms that are developed?
If, as part of your digital health business model, you are partnering with a provider of big data analytics services (for example to develop therapeutic models to then incorporate into an app), you should ensure that you, your business partners, and any other third parties who are necessary for the implementation of that model, are permitted to use the output data to the extent needed. This requires careful attention to the data terms in your contract with the provider as well as appropriate due diligence of the terms under which the input data were obtained, which may limit downstream use of that output data.

The data outputs from data analytics conducted in digital health projects may represent new “insights.” For example, these insights could relate to the effectiveness of different treatments, comparative outcomes achieved with different delivery models, or predictive models for diagnosing, treating, and delivering care. Securing ownership of these insights and intellectual property rights in these insights can be difficult. First, under various legal systems, it may not be possible for the insights to be “owned” as such, and patent, copyright, or trade secret protection may not be available or viable. Secondly, there may be competing ownership interests among the collaboration partners. For example, if your data scientists discover an insight using your own proprietary algorithms, but those algorithms were applied to patient data and rely on advanced analytics tools provided by a service provider’s data processing platform, should the owner of the insight be you, the source of the data (e.g., a hospital), or the service provider?

In addition, competition authorities, particularly in Europe, are increasingly focusing on the circumstances in which data, including output data, can confer an anti-competitive advantage. Recent cases and statements from certain European competition authorities suggest that there may be a risk that entities controlling output data that (a) cannot be replicated (or obtained from another source), (b) is necessary for the development of new products, and (c) will not quickly become outdated, may be required to provide access to third parties developing such new products.(1)

2. Do you have commitments from your suppliers to provide functions at service levels suitable for the health sector and designed to maintain patient/user trust?
If you are responsible for delivering a digital health service to customers, it is critical for that service to be provided in accordance with service levels that are suitable for the health sector and that are designed to build and maintain patient/user trust.

Service components such as availability of user support, call response times, “uptimes”/permissible downtimes, and problem resolution time frames will all typically be governed by service level arrangements between you and your customers, and between you and your suppliers. If one or more components of the digital health service are supplied to you, or on your behalf, by third party sub-contractors, then you will want to ensure that you have in place with those sub-contractors appropriately robust service level arrangements. These will need to be sufficient to ensure that you can provide to your customers the level of service for the overall digital health service that they expect and that will maintain your competitiveness in the market.

Prior to contracting, you should carry out due diligence of your potential suppliers to determine whether they are in turn dependent on other suppliers (for example a cloud storage platform provider), and if so, whether the service levels at each link in the chain are adequate having regard to customer expectations.

3. When you are structuring strategic collaborations to develop and deliver a digital health service, have you taken into account uncertainties as to the ultimate composition of the service, its customers, and its reimbursement model?
If you are entering into strategic, long-term collaborations to develop and market a digital health service, a significant challenge is that it is often unclear at the outset what the precise route to market will be, including who, in principle, will be the customer. It is often similarly unclear what all the elements of the resulting service will ultimately be, and so it is not possible to determine the cost of providing the service until a later stage in the collaboration. Further, the reimbursement model may initially be uncertain and your collaboration partners might desire to conduct one or more pilot phases with healthcare providers in order to demonstrate the service’s value proposition and refine a reimbursement model.

As a result, you must consider whether you wish to agree to financial terms at the outset or at a later stage in the course of the collaboration. Agreeing to financial terms at the outset has the benefit of certainty, but there is a risk that those terms become inappropriate or uneconomical in the event that the underlying basis for the financial terms changes. On the other hand, deferring agreement of the financial terms to a point at which there is more clarity ensures flexibility, but it will be essential for you and your collaboration partners to work through, and document, the consequences of failing to reach agreement at that later stage. For example, you will likely have invested considerably in the collaboration prior to that point, and may have a third party such as a healthcare system looking to move from a pilot phase to full commercial implementation.

  1. See, for example, Facebook/WhatsApp Case N.7217; Keynote speech, G. Loriot, 7 June 2017, GCR Live 6th Annual Telecoms, Media & Technology conference.
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