Electronic Health Record Phenotyping for Case Detection and Prediction of Emergency Department Visits for Child and Adolescent Suicide Attempts

University Of California Los Angeles, Los Angeles CA

Investigators

Linked publications & trials

Abstract

PROJECT SUMMARY/ABSTRACT The candidate requests support for a five-year program of training and research to better understand how electronic health record phenotyping and other computational methods can bolster detection and prediction of suicide attempts by youth ages 10 to 17 using existing medical record data. In the proposed training plan, the candidate will build upon her previous experiences in social psychology, clinical informatics, and clinical child and adolescent psychiatry to perform a multidisciplinary project at the University of California, Los Angeles Health System. Her training plan includes developing skills and knowledge in 1) analysis of natural language data, 2) development of risk algorithms in healthcare settings to improve suicide prevention, 3) basic qualitative research skills including modified Delphi Panel approach, and 4) the responsible conduct of research. Suicide is the second leading cause of death of young people over 10 years old in the United States and suicide attempts among children are common, costly and preventable. There is a need to better detect children who have received medical care for suicidal behavior, discover children at high risk for future attempts who warrant specialized clinical attention, and connect suicide attempt detection and risk prediction algorithms with clinically-useable tools that can inform medical decision-making for children and families. This study proposes that electronic health record phenotyping, a method of standardizing case detection using clinical note text and structured medical record data, may offer improved detection and personalized risk prediction for children and complement existing suicide prevention efforts. In the proposed research, using a cross-sectional design, Aim 1 will focus on adaptation of a method (electronic health record phenotyping) to detect emergency department visits for suicide attempts by children using electronic health records. Then, using a case-control design, Aim 2 will focus on development of risk prediction models of emergency department visits for suicide attempts by children using longitudinal electronic health records over two years. Aim 3 will focus on assessment of the validity, acceptability, usability, feasibility, and overall utility of applying electronic health record phenotyping to detection and risk prediction of attempts using a modified Delphi panel approach. This plan will parallel a training plan building skills and knowledge to bridge informatics, computational methods, and clinical child psychiatry. The broader aim for this research is to better understand the developmentally- varied, transdiagnostic path toward suicidal behavior in children. By applying computational advancements to mine existing healthcare data, this research is an initial step to enhance pediatric-specific signal detection and support personalized prediction of suicidal behavior, in turn, setting the stage for deployment of these approaches in clinical settings where providers, youth, and families may directly benefit.