illustration of person tracking his health condition with smart bracelet, mobile application and cloud services.

Should We Regulate Direct-to-Consumer Health Apps?

By Sara Gerke and Chloe Reichel

According to one estimate, over 318,000 health apps are available in app stores, and over 200 health apps are added each day. Of these, only a fraction are regulated by the U.S. Food and Drug Administration (FDA); those classified as “medical devices,” which typically pose a moderate to high risk to user safety.

In this final installment of our In Focus Series on Direct-to-Consumer Health Apps, we asked our respondents to reflect on this largely unregulated space in health tech.

Specifically, we asked: How can/should regulators deal with the assessment of health apps? For apps not currently regulated by the FDA, should they undergo any kind of review, such as whether they are helpful for consumers?

Read their answers below, and explore the following links for their responses to other questions in the series.

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Close up of a computer screen displaying code

Mitigating Bias in Direct-to-Consumer Health Apps

By Sara Gerke and Chloe Reichel

Recently, Google announced a new direct-to-consumer (DTC) health app powered by artificial intelligence (AI) to diagnose skin conditions.

The company met criticism for the app, because the AI was primarily trained on images from people with darker white skin, light brown skin, and fair skin. This means the app may end up over-or under-diagnosing conditions for people with darker skin tones.

This prompts the questions: How can we mitigate biases in AI-based health care? And how can we ensure that AI improves health care, rather than augmenting existing health disparities?

That’s what we asked of our respondents to our In Focus Series on Direct-to-Consumer Health Apps. Read their answers below, and check out their responses to the other questions in the series.

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Hand holding smartphone with colorful app icons concept.

Who Owns the Data Collected by Direct-to-Consumer Health Apps?

By Sara Gerke and Chloe Reichel

Who owns the data that are collected via direct-to-consumer (DTC) health apps? Who should own that data?

We asked our respondents to answer these questions in the third installment of our In Focus Series on Direct-to-Consumer Health Apps. Learn about the respondents and their views on data privacy concerns in the first installment of this series, and read their thoughts on consumer access to DTC health app data in the second installment.

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Illustration of multicolored profiles. An overlay of strings of ones and zeroes is visible

Should Users Have Access to Data Collected by Direct-to-Consumer Health Apps?

By Sara Gerke and Chloe Reichel

Should consumers have access to the data (including the raw data) that are collected via direct-to-consumer (DTC) health apps? What real-world challenges might access to this data introduce, and how might they be addressed?

In this second installment of our In Focus Series on Direct-to-Consumer Health Apps, that’s what we asked our respondents. Learn about the respondents and their views on data privacy concerns in the first installment of this series. Read on for their thoughts on whether and how consumers should gain access to the data that direct-to-consumer health apps collect.

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hands hold phone with app heart and activity on screen over table in office

Perspectives on Data Privacy for Direct-to-Consumer Health Apps

By Sara Gerke and Chloe Reichel

Direct-to-consumer (DTC) health apps, such as apps that manage our diet, fitness, and sleep, are becoming ubiquitous in our digital world.

These apps provide a window into some of the key issues in the world of digital health — including data privacy, data access, data ownership, bias, and the regulation of health technology.

To better understand these issues, and ways forward, we contacted key stakeholders representing a range of perspectives in the field of digital health for their brief answers to five questions about DTC health apps.

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lady justice.

Computational Psychiatry for Precision Sentencing in Criminal Law

By Francis X. Shen

A core failing of the criminal justice system is its inability to individualize criminal sentences and tailor probation and parole to meet the unique profile of each offender.

As legal scholar, and now federal judge Stephanos Bibas has observed, “All too often … sentencing guidelines and statutes act as sledgehammers rather than scalpels.”

As a result, dangerous offenders may be released, while offenders who pose little risk to society are left behind bars. And recidivism is common — the U.S. has an astounding recidivism rate of 80% — in part because the current criminal justice system largely fails to address mental health challenges, which are heavily over-represented in the justice system.

Advances in computational psychiatry, such as the deep phenotyping methods explored in this symposium, offer clinicians newfound abilities to practice precision psychiatry. The idea behind precision psychiatry is both simple and elusive: treat individuals as individuals. Yet advancing such a program in practice is “very ambitious” because no two individual brains — and the experiences those brains have had over a lifetime — are the same.

Deep phenotyping offers the criminal justice system the tools to improve public safety, identify low-risk offenders, and modify decision-making to reduce recidivism. Computational psychiatry can lead to what can be described as precision sentencing.

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phone camera

Deep Phenotyping Could Help Solve the Mental Health Care Crisis

By Justin T. Baker

The United States faces a growing mental health crisis and offers insufficient means for individuals to access care.

Digital technologies — the phone in your pocket, the camera-enabled display on your desk, the “smart” watch on your wrist, and the smart speakers in your home — might offer a path forward.

Deploying technology ethically, while understanding the risks of moving too fast (or too slow) with it, could radically extend our limited toolkit for providing access to high-quality care for the many individuals affected by mental health issues for whom the current mental health system is either out of reach or otherwise failing to meet their need.

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Pen hovering over words "I agree" with check box next to it.

Unique Challenges to Informed Consent in Deep Phenotyping Research

By Benjamin C. Silverman

Deep phenotyping research procedures pose unique challenges to the informed consent process, particularly because of the passive and boundless nature of the data being collected and how this data collection overlaps with our everyday use of technology.

As detailed elsewhere in this symposium, deep phenotyping in research involves the collection and analysis of multiple streams of behavioral (e.g., location, movement, communications, etc.) and biological (e.g., imaging, clinical assessments, etc.) data with the goal to better characterize, and eventually predict or intervene upon, a number of clinical conditions.

Obtaining voluntary competent informed consent is a critical aspect to conducting ethical deep phenotyping research. We will address here several challenges to obtaining informed consent in deep phenotyping research, and describe some best practices and relevant questions to consider.

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