Funding Source: NIH-NIDA
Principal Investigator: Majid Afshar (Pulmonary & Critical Care Medicine); Randall Brown Co-I
Description: Documentation of substance use is common and occurs in 97% of provider admission notes, but their free text format renders them difficult to mine and analyze. Natural Language Processing (NLP) and machine learning are subfields of artificial intelligence (AI) that provide a solution to analyze text data in the EHR to identify substance misuse. Version of an NLP and machine learning tool, our opioid and alcohol misuse classifiers successfully used data from clinical notes collected in the first 24 hours of admission. We aim to train and test our substance misuse classifiers in a retrospective dataset of over 35,000 hospitalizations that have been manually screened with the universal screen, AUDIT, and DAST. The top performing classifier will then be tested prospectively to: (1) externally validate its screening performance in a hospital without established screening; and (2) test its effectiveness against usual care at a hospital with questionnaire-based substance misuse screening.