Passive Monitoring of Mental Health Status in the Criminal Forensic Population.

Abstract

Current approaches to monitoring patients' mental status rely heavily on self-reported symptomatology, clinician observation, and self-rated symptom scales. The limitations inherent in these methodologies have implications for the accuracy of diagnosis, treatment planning, and prognosis. Certain populations are particularly affected by these limitations because of their unique situations, including criminal forensic patients, who have a history of both criminal behavior and mental disorder, and experience increased stigma and restrictions in their access to mental health care. This population may benefit particularly from recent developments in technology and the growing use of mobile devices and sensors to collect behavioral information via passive monitoring. These technologies offer objective parameters that correlate with mental health status and create an opportunity to use Big Data and machine learning to refine diagnosis and predict behavior in a way that represents a marked shift from current practices. This article reviews the approaches to and limitations of psychiatric assessment and contrasts this with the promise of these new technologies. It then discusses the ethics concerns associated with these technologies and explores their potential relevance to criminal forensic psychiatry and the broader implications they carry for health and criminal justice policy.