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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Life preserver on boat.

Incidental Findings in Deep Phenotyping Research: Legal and Ethical Considerations

By Amanda Kim, M.D., J.D., Michael Hsu, M.D., Amanda Koire, M.D., Ph.D., Matthew L. Baum, M.D., Ph.D., D.Phil.

What obligations do researchers have to disclose potentially life-altering incidental findings (IFs) as they happen in real time?

Deep phenotyping research in psychiatry integrates an individual’s real-time digital footprint (e.g., texts, GPS, wearable data) with their biomedical data (e.g., genetic, imaging, other biomarkers) to discover clinically relevant patterns, usually with the aid of machine learning. Findings that are incidental to the study’s objectives, but that may be of great importance to participants, will inevitably arise in deep phenotyping research.

The legal and ethical questions these IFs introduce are fraught. Consider three hypothetical cases below of individuals who enroll in a deep phenotyping research study designed to identify factors affecting risk of substance use relapse or overdose:

A 51-year-old woman with alcohol use disorder (AUD) is six months into sobriety. She is intrigued to learn that the study algorithm will track her proximity to some of her known triggers for alcohol relapse (e.g., bars, liquor stores), and asks to be warned with a text message when nearby so she can take an alternative route. Should the researchers share that data?

A 26-year-old man with AUD is two years into sobriety. Three weeks into the study, he relapses. He begins arriving to work inebriated and loses his job. After the study is over, he realizes the researchers may have been able to see from his alcohol use surveys, disorganized text messages, GPS tracking, and sensor data that he may have been inebriated at work, and wishes someone had reached out to him before he lost his job. Should they have?

A 35-year-old man with severe opioid use disorder experiences a near-fatal overdose and is discharged from the hospital. Two weeks later, his smartphone GPS is in the same location as his last overdose, and his wearable detects that his respiratory rate has plummeted. Should researchers call EMS? Read More

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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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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doctor holding clipboard.

“Actionability” and the Ethics of Communicating Results to Study Participants

By Patrick Monette

To what end does a physician have a responsibility toward a research participant? Specifically, what data may be considered “actionable” for the physician to disclose to the patient, and when and how might this be done?

In the clinical setting, contemporary medical ethics address a physician’s “fiduciary responsibility.” That is, there is a well-established professional expectation that the physician will place the patient’s interests above their own and advocate for their welfare. This post focuses on an alternative dyad, that of physician and research participant, to explore how the field has broached the topic of actionability in the setting of clinical research. Read More

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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Centers for Disease Control and Prevention. Georgia, Atlanta USA March 6, 2020.

The Politics of CDC Public Health Guidance During COVID-19

A version of this post first ran in Ms. Magazine on October 28, 2020. It has been adapted slightly for Bill of Health. 

By Aziza Ahmed

In recent months, public health guidance from the U.S. Centers for Disease Control and Prevention (CDC) has become a site of political reckoning.

The agency has taken an enormous amount of heat from a range of institutions, including the executive and the public, during the COVID-19 pandemic. The former has sought to intervene in public health guidance to ensure that the CDC presents the President and administration’s response to COVID-19 in a positive light. The latter consists of opposed factions that demand more rigorous guidance, or, its opposite, less stringent advice.

Importantly, these tensions have revealed how communities experience the pandemic differently. CDC guidance has produced divergent consequences, largely depending on demographics. These differences have been particularly pronounced along racial lines.

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