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.
Ideally, this could one day include bringing interventions to patients where they are in the real world in a convenient, efficient, effective and timely way. For example, deep phenotyping technology could map the movement and behavior of dementia patients through GPS location monitoring, actigraphy, and sleep patterns. This data can reflect important behavioral changes, such as pacing, restlessness, and agitation, which may prompt further intervention.
Yet the road to this innovative future is fraught with ethical dilemmas — dilemmas explored in this symposium.
And though the pressing bioethics issues associated with computational phenotyping are receiving increased recognition, there is not a comprehensive roadmap to provide the types of practical advice and resources that researchers and clinicians need.
To help fill this gap, this symposium raises questions on the interrelated issues of consent, privacy, transparency, autonomy, data sharing and security, return of research results, incidental findings, direct-to-consumer ethics, and possible bias in predictive algorithms.
The symposium emerges out of a National Institutes of Health Bioethics Administrative Supplement award (NIH 1U01MH116925-01, Justin Baker and Scott Rauch, Project PIs; Francis Shen & Benjamin Silverman Supplement PIs).
We are grateful to the NIH Department of Bioethics, the McLean Institute for Technology in Psychiatry, the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics, and the Shen Neurolaw Lab for collaborating on this work.
For more resources, please consult other work resulting from this grant, including the Ethics of Deep Phenotyping website, and the video recording from our October 2019 public event, Computational Justice: How Artificial Intelligence and Digital Phenotyping Can Advance Social Good, co-sponsored by the MGH Center for Law, Brain, and Behavior and the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics.