JMIR Publications

ABSTRACT

Background:

Mental illness and substance use are prevalent among people living with HIV (PLWH) and often lead to poor health outcomes. Electronic medical record (EMR) data are increasingly being utilized for HIV-related clinical research, but mental illness and substance use are often under-documented in structured EMR fields. Natural language processing (NLP) of unstructured text of clinical notes in the EMR may more accurately identify mental illness and substance use among PLWH than structured EMR fields alone.

Objective:

To utilize NLP of clinical notes to detect mental illness and substance use among PLWH and to determine how often these factors are documented in structured EMR fields.

Methods:

We collected both structured EMR data (diagnosis codes, social history, Problem List) as well as the unstructured text of clinical HIV care notes for adult PLWH. We developed NLP algorithms to identify words and phrases associated with mental illness and substance use in the clinical notes. The algorithms were validated base on chart review. We compared numbers of patients with mental illness or substance use identified by structured EMR fields vs. those identified by the NLP algorithms.

Results:

The NLP algorithm for detecting mental illness had PPV of 98% and NPV of 98%. The NLP algorithm for detecting substance use had a PPV of 92% and NPV of 98%. The NLP algorithm for mental illness identified 54.0% (420/778) of patients as having documentation of mental illness in the text of clinical notes. Among the patients with mental illness detected by NLP, 58.6% (246/420) had documentation of mental illness in at least one structured EMR field. 63 patients had documentation of mental illness in structured EMR fields that was not detected by NLP of clinical notes. The NLP algorithm for substance use detected substance use in the text of clinical notes in 22.0% (141/778) of participants. Among patients with substance use detected by NLP, 73.8% (104/141) had documentation of substance use in at least one structured EMR field. 77 patients had documentation of substance use in structured EMR fields that was not detected by NLP of clinical notes.

Conclusions:

Among patients in an urban HIV care clinic, NLP of clinical notes identified high rates of mental illness and substance use that were often not documented in structured EMR fields. This finding has important implications for clinical and epidemiologic research among PLWH.