Apple watch and fit bit.

Beyond HIPAA: A Proposed Self-Policing Framework for Digital Health Products

By Vrushab Gowda

As digital health products proliferate, app developers, hardware manufacturers, and other entities that fall outside Health Insurance Portability and Accountability Act (HIPAA) regulation are collecting vast amounts of biometric information. This burgeoning market has spurred patient privacy and data stewardship concerns.

To this end, two policy nonprofits – the Center for Democracy and Technology (CDT) and the eHealth Initiative (eHI) – earlier this month jointly published a document detailing self-regulatory guidelines for industry. The following piece traces the development of the “Proposed Consumer Privacy Framework for Health Data,” provides an overview of its provisions, and offers critical analysis.

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AI concept art.

A Closer Look at FDA’s Newly Released AI/ML Action Plan

By Vrushab Gowda

The U.S. Food and Drug Administration (FDA or “the Agency”) recently issued its long awaited AI/ML (Artificial Intelligence/Machine Learning) Action Plan.

Announced amid the closing days of Stephen Hahn’s term as Commissioner, it takes steps toward establishing a dedicated regulatory strategy for AI products intended as software as a medical device (SaMD), versus those embedded within physical hardware. The FDA has already approved a number of such products for clinical use; however, AI algorithms’ self-learning capabilities expose the limitations of traditional regulatory pathways.

The Action Plan further outlines the first major objectives of the Digital Health Center of Excellence (DHCoE), which was established to much fanfare but whose early moves have remained somewhat unclear. This document presents a policy roadmap for its years ahead.

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Washington, USA- January13, 2020: FDA Sign outside their headquarters in Washington. The Food and Drug Administration (FDA or USFDA) is a federal agency of the USA.

Weaknesses in Medical Device Regulation Worsened by Trump Administration

By Jacob Howard

In the waning days of the Trump administration, a final push was made to fundamentally weaken regulation of medical devices.

Lambasted as a “full frontal assault on public health” by U.S. Food and Drug Administration (FDA) officials, key policy changes include proposed emergency exemptions to bring a multitude of devices to market without the necessary scientific backing. Justified as a strategy to expedite the delivery of life-saving products, this speed comes at a risk to millions of patients.

As the third most prevalent cause of death in the U.S., medical error continues to be a critical issue that is exacerbated by weakening integrity of the regulatory process. This issue is further compounded by the fact that past regulatory failures in the medical device sphere have not been adequately addressed. The surgical stapler offers an illustrative example.

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Person typing on computer.

Lessons Learned from Deep Phenotyping Patients with Rare Psychiatric Disorders

By Catherine A Brownstein and Joseph Gonzalez-Heydrich

Given the potential sensitivities associated with describing (i.e., phenotyping) patients with potentially stigmatizing psychiatric diagnoses, it is important to acknowledge and respect the wishes of the various parties involved.

The phenotypic description and depiction of a patient in the literature, although deidentified, may still be of great impact to a family.

By way of example, a novel genetic variant was identified as a likely explanation for the clinical presentation of a patient in a large cohort of individuals with neurodevelopmental and/or psychiatric phenotypes, a finding of great medical interest. The research team elected to further study this candidate and collected samples for functional evaluation of the gene variant and preparation of a case report.

Because the patient had a complicated phenotype, several physicians from various specialties were involved in the patient’s care. The paper draft was circulated amongst the collaborating clinicians and researchers and ultimately shared with the patient’s family by one of their involved caregivers. This is typically not a requirement of such studies, as the informed consent process includes the subjects’ understanding and consent for dissemination of deidentified results in the scientific literature. But as a general practice, families are informed about manuscripts in process, and in this case the family had requested to be kept abreast of ongoing developments.

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A calculator, a stethoscope, and a stack of money rest on a table.

In Defense of Medicare Coverage of Innovative Technologies

By Abe Sutton

On January 12th, the Centers for Medicare & Medicaid Services (CMS) finalized a prior proposal to establish Medicare coverage for breakthrough medical devices approved by the U.S. Food and Drug Administration (FDA).

While some have expressed concerns about the proposal, I believe it is a balanced attempt to encourage innovation and CMS was right to finalize it.

In this post, I give an overview of the general regulatory standards, walk through what the Medicare Coverage of Innovative Technologies (MCIT) proposal does, and lay out a case for why it deserved to be finalized.

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Code on computer.

How to Secure Our Digital Health Infrastructure Against Cyber Attacks

By Vrushab Gowda

Our health information infrastructure is highly susceptible to cyber attacks. At the time of writing, the Department of Health and Human Services (HHS) is actively investigating over 700 major breaches over the past 24 months alone.

It is incumbent upon our institutions to proactively guard against these threats, with our federal government leading the charge.

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Photograph of a stack of magazines on a chair

Monthly Round-Up of What to Read on Pharma Law and Policy

By Ameet SarpatwariBeatrice Brown, Neeraj Patel, and Aaron S. Kesselheim

Each month, members of the Program On Regulation, Therapeutics, And Law (PORTAL) review the peer-reviewed medical literature to identify interesting empirical studies, policy analyses, and editorials on health law and policy issues.

Below are the citations for papers identified from the month of December. The selections feature topics ranging from an analysis of potential approaches for evaluating novel SARS-CoV-2 vaccine candidates after other vaccines have already been authorized; to an examination of social, cultural, and economic aspects of microbial resistance; to a study on clinical evidence supporting FDA clearance of novel therapeutics devices via the de novo pathway. A full posting of abstracts/summaries of these articles may be found on our website.

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Medicine doctor and stethoscope in hand touching icon medical network connection with modern virtual screen interface, medical technology network concept

Data Talking to Machines: The Intersection of Deep Phenotyping and Artificial Intelligence

By Carmel Shachar

As digital phenotyping technology is developed and deployed, clinical teams will need to carefully consider when it is appropriate to leverage artificial intelligence or machine learning, versus when a more human touch is needed.

Digital phenotyping seeks to utilize the rivers of data we generate to better diagnose and treat medical conditions, especially mental health ones, such as bipolar disorder and schizophrenia. The amount of data potentially available, however, is at once both digital phenotyping’s greatest strength and a significant challenge.

For example, the average smartphone user spends 2.25 hours a day using the 60-90 apps that he or she has installed on their phone. Setting aside all other data streams, such as medical scans, how should clinicians sort through the data generated by smartphone use to arrive at something meaningful? When dealing with this quantity of data generated by each patient or research subject, how does the care team ensure that they do not miss important predictors of health?

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Society or population, social diversity. Flat cartoon vector illustration.

Bias, Fairness, and Deep Phenotyping

By Nicole Martinez

Deep phenotyping research has the potential to improve understandings of social and structural factors that contribute to psychiatric illness, allowing for more effective approaches to address inequities that impact mental health.

But, in order to build upon the promise of deep phenotyping and minimize the potential for bias and discrimination, it will be important to incorporate the perspectives of diverse communities and stakeholders in the development and implementation of research projects.

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Picture of doctor neck down using an ipad with digital health graphics superimposed

Symposium Introduction: Ethical, Legal, and Social Implications of Deep Phenotyping

This post is the introduction to our Ethical, Legal, and Social Implications of Deep Phenotyping symposium. All contributions to the symposium will be available here.

By Francis X. Shen

This digital symposium explores the ethical, legal, and social implications of advances in deep phenotyping in psychiatry research.

Deep phenotyping in psychiatric research and practice is a term used to describe the collection and analysis of multiple streams of behavioral and biological data, some of this data collected around the clock, to identify and intervene in critical health events.

By combining 24/7 data — on location, movement, email and text communications, and social media — with brain scans, genetics/genomics, neuropsychological batteries, and clinical interviews, researchers will have an unprecedented amount of objective, individual-level data. Analyzing this data with ever-evolving artificial intelligence (AI) offers the possibility of intervening early with precision and could even prevent the most critical sentinel events.

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