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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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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