The goal of this study is to improve management of depressed patients in primary care settings through creating a web-based decision aid for selection of antidepressant. Clinicians are surprisingly poor in selecting the right antidepressant: the majority of depressed patients, more than 60%, do not benefit from their first antidepressant. The task of choosing an antidepressant is complex, and is affected by many factors, including the patients’ medical history. For example, patients with cognitive disorders, substance use disorders, obesity, insomnia, cerebrovascular diseases, hormone imbalances, cancer, or post-traumatic stress disorder are known to benefit from different antidepressants. Using OptumLabs Data for years 1993 to 2017, the researchers will comprehensively examine how various illness affects response to antidepressants (or combination of antidepressants).  Since data are observational, causal methods of analysis will be used to remove confounding in the data. As a result of the analysis, the researchers will develop and disseminate the first artificial intelligence guided decision aid for selection of antidepressants based on illness/phenotype history. Deliverables will include a project work plan and final narrative report. The researchers will also produce paper(s) suitable for publication and present findings at national research meetings and to other stakeholder audiences as appropriate, including policymakers at the federal, state, and local levels and other key stakeholders, as part of the deliverables for this grant.

Principal Investigators:

Farrokh Alemi's headshot
Researcher

Farrokh Alemi, Ph.D.

Professor of Health Informatics - George Mason University Department of Health Administration and Policy

Farrokh Alemi, Ph.D. is the Professor of Health Informatics at George Mason University Department of Health Ad... Read Bio

 

Grant: #76786
Grantee Institution: George Mason University
Grant Period: 9/15/19 – 9/14/20