Digital health companies are producing innovative products at a rapidly accelerating pace and experiencing a boom in investments and demand as the regulatory environment becomes more supportive of digital health services to both improve patient care and stay profitable. Protecting intellectual property (IP) and building a feasible data strategy to support the research and development

On January 7, 2020, the Director of the US Office of Management and Budget (OMB) issued a Draft Memorandum (the Memorandum) to all federal “implementing agencies” regarding the development of regulatory and non-regulatory approaches to reducing barriers to the development and adoption of artificial intelligence (AI) technologies. Implementing agencies are agencies that conduct foundational research, develop and deploy AI technologies, provide educational grants, and regulate and provide guidance for applications of AI technologies, as determined by the co-chairs of the National Science and Technology Council (NSTC) Select Committee. To our knowledge, the NTSC has not yet determined which agencies are “implementing agencies” for purposes of the Memorandum.

Submission of Agency Plan to OMB

The “implementing agencies” have 180 days to submit to OMB their plans for addressing the Memorandum.

An agency’s plan must: (1) identify any statutory authorities specifically governing the agency’s regulation of AI applications as well as collections of AI-related information from regulated entities; and (2) report on the outcomes of stakeholder engagements that identify existing regulatory barriers to AI applications and high-priority AI applications that are within the agency’s regulatory authorities. OMB also requests but does not require agencies to list and describe any planned or considered regulatory actions on AI.

Principles for the Stewardship of AI Applications

The Memorandum outlines the following as principles and considerations that agencies should address in determining regulatory or non-regulatory approaches to AI:

  1. Public trust in AI. Regulatory and non-regulatory approaches to AI need to be reliable, robust and trustworthy.
  2. Public participation. The public should have the opportunity to take part in the rule-making process.
  3. Scientific integrity and information quality. The government should use scientific and technical information and processes when developing a stance on AI.
  4. Risk assessment and management.A risk assessment should be conducted before determining regulatory and non-regulatory approaches.
  5. Benefits and costs. Agencies need to consider the societal costs and benefits related to developing and using AI applications.
  6. Flexibility. Agency approaches to AI should be flexible and performance-based.
  7. Fairness and nondiscrimination. Fairness and nondiscrimination in outcomes needs to be considered in both regulatory and non-regulatory approaches.
  8. Disclosure and transparency. Agencies should be transparent. Transparency can serve to improve public trust in AI.
  9. Safety and security. Agencies should guarantee confidentiality, integrity and availability of data use by AI by ensuring that the proper controls are in place.
  10. Interagency coordination. Agencies need to work together to ensure consistency and predictability of AI-related policies.


Continue Reading

In April 2019, the US Food and Drug Administration (FDA) issued a white paper, “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device,” announcing steps to consider a new regulatory framework to promote the development of safe and effective medical devices that use advanced AI algorithms. AI, and specifically ML, are “techniques used to design and train software algorithms to learn from and act on data.” FDA’s proposed approach would allow modifications to algorithms to be made from real-world learning and adaptation that accommodates the iterative nature of AI products while ensuring FDA’s standards for safety and effectiveness are maintained.

Under the existing framework, a premarket submission (i.e., a 510(k)) would be required if the AI/ML software modification significantly affects device performance or the device’s safety and effectiveness; the modification is to the device’s intended use; or the modification introduces a major change to the software as a medical device (SaMD) algorithm. In the case of a PMA-approved SaMD, a PMA supplement would be required for changes that affect safety or effectiveness. FDA noted that adaptive AI/ML technologies require a new total product lifecycle (TPLC) regulatory approach and focuses on three types of modifications to AI/ML-based SaMD:


Continue Reading

As part of the 21st Century Cures Act, Congress gave the US Food and Drug Administration (FDA) the authority to establish a Breakthrough Devices Program intended to expedite the development and prioritize the review of certain medical devices that provide for more effective treatment or diagnosis of life-threatening or irreversibly debilitating disease or conditions. In December 2018, FDA issued a guidance document describing policies FDA intends to use to implement the Program.

There are two criteria for inclusion in the Breakthrough Device Program:

  1. The device must provide for a more effective treatment or diagnosis of a life-threatening or irreversibly debilitating human disease or condition; and
  2. The device must (i) represent breakthrough technology, (ii) have no approved or cleared alternatives, (iii) offer significant advantages over existing approved or cleared alternatives, or (iv) demonstrate that its availability is in the best interest of patients.


Continue Reading

Investment in artificial intelligence (AI) and digital health technologies has increased exponentially over the last few years. In the United Kingdom, the excitement and interest in this space has been supported by NHS policies, including proposals in the NHS Long Term Plan, which set out ambitious aims for the acceleration and adoption of digital health and AI, particularly in primary care, outpatients and wearable devices.

Although these developments are encouraging to developers, there is still no clear framework for reimbursement or tariffs for digital health tools and AI.

At the same time, the plethora of new technologies has led to increased calls for regulation and oversight, particularly around data quality and evaluation. Many of these concerns may be addressed by the new Medical Device Regulation (MDR) and other regulatory developments. In fact, there is some risk that while regulatory landscape is moving quickly, the pricing environment is still a way behind.

In May 2020, the new MDR will change the law and process of certification for medical software. The new law includes significant changes for digital health technologies which are medical devices. In March 2019, the National Institute for Health and Care Excellence (NICE) also published a new evidence standards framework for digital health technologies. The Care Quality Commission (CQC) already regulates online provision of health care, and there are calls for wider and greater regulation. The government has also published a code on the use of data in AI.

Digital Health Technologies and the MDR

The new MDR will mean a significant change to the regulatory framework for medical devices in the European Union.

As with the previous law, the MDR regulates devices through a classification system.

The new regime introduces new rules for medical software that falls within the definition of device. This will mean significant changes for companies that develop or offer medical software solutions, especially if their current certification has been “up-classed” under the MDR.

Key Takeaways for Investors in Digital Health Tools

Companies and investors in digital health should:

Continue Reading

Join McDermott next Wednesday for a live webinar on the unique considerations in developing and procuring AI solutions for digital health applications from the perspective of various stakeholders. We will discuss the legal issues and strategies surrounding:

  • Research and data mapping essential to the development and validation of AI technologies
  • Protecting and maintaining intellectual property

Fortune’s April 2018 cover story, “Tech’s Next Big Wave: Big Data Meets Biology,” conveys loudly and clearly that technological innovation is transforming the health care continuum—changing the way care is delivered, as well as how patients manage their ongoing health—and as patient demand for health innovation increases, more companies seem eager to hop on