Philip Henson1, Hannah Wisniewski1, Charles Stromeyer Iv2, John Torous3. 1. Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA. 2. Consumer Advisory Board, Massachusetts Mental Health Center, Boston, MA, 02115, USA. 3. Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA. jtorous@bidmc.harvard.edu.
Abstract
PURPOSE OF REVIEW: This review aims to examine relapse definitions and risk factors in psychosis as well as the role of technology in relapse predictions and risk modeling. RECENT FINDINGS: There is currently no standard definition for relapse. Therefore, there is a need for data models that can account for the variety of factors involved in defining relapse. Smartphones have the ability to capture real-time, moment-to-moment assessment symptomology and behaviors via their variety of sensors and have high potential to be used to create prediction and risk modeling. While there is still a need for further research on how technology can predict and model relapse, there are simple ways to begin incorporating technology for relapse prediction in clinical care.
PURPOSE OF REVIEW: This review aims to examine relapse definitions and risk factors in psychosis as well as the role of technology in relapse predictions and risk modeling. RECENT FINDINGS: There is currently no standard definition for relapse. Therefore, there is a need for data models that can account for the variety of factors involved in defining relapse. Smartphones have the ability to capture real-time, moment-to-moment assessment symptomology and behaviors via their variety of sensors and have high potential to be used to create prediction and risk modeling. While there is still a need for further research on how technology can predict and model relapse, there are simple ways to begin incorporating technology for relapse prediction in clinical care.