Original Article| Volume 64, ISSUE 4, P400-409, October 2022

Natural Language Processing for Computer-Assisted Chart Review to Assess Documentation of Substance use and Psychopathology in Heart Failure Patients Awaiting Cardiac Resynchronization Therapy


      • This article describes a study of 965 patients with heart failure who were awaiting cardiac resynchronization therapy.
      • Using natural language processing, a branch of artificial intelligence, we found that clinicians are under documenting assessments and plans pertaining to mental health and substance use.
      • Despite clinical recommendations.



      Advanced heart failure (HF) patients often experience distressing psychological symptoms, frequently meeting diagnostic criteria for psychological disorders, including anxiety, depression, and substance use disorder. Patients with device-based HF therapies have added risk for psychological disorders, with consequences for their physiological functioning, including adverse cardiac outcomes.


      This study used natural language processing (NLP) for computer-assisted chart review to assess documentation of mental health and substance use in HF patients awaiting cardiac resynchronization therapy (CRT), a device-based HF therapy.


      We applied NLP to clinical notes from electronic health records (EHR) of 965 consecutive patients, with 9821 total clinical notes, at two academic medical centers between 2004 and 2015. We developed and validated a keyword library capturing terms related to mental health and substance use, while balancing specificity and sensitivity.


      Mean age was 71.6 years (SD = 11.8), 78% male, and 87% non-Hispanic White. Of the 544 patients (56.4%) with documentation of mental health history, 9.7% had their mental health assessed and 6.6% had a plan documented. Of the 773 patients (80.1%) with documentation of substance use history, 10 (1.0%) had an assessment, and 3 (0.3%) had a plan.


      Despite clinical recommendations and standards of care, clinicians are under documenting assessments and plans prior to CRT. Future research should develop an algorithm to prompt clinicians to document this content. Such quality improvement efforts may ensure adherence to standards of care and clinical guidelines.

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