JMIR Publications

ABSTRACT

Background:

Major Depressive Disorder (MDD) and treatment-resistant depression (TRD) are heterogeneous conditions in which key clinical details are split across structured fields and free-text notes in electronic health records (EHRs), constraining population-level insight and timely audit of care quality.

Objective:

To present a clinician-oriented, artificial intelligence-supported real-world evidence (RWE) methodology integrating structured and unstructured EHR data to profile MDD and TRD, and report comorbidity patterns from a two-site pilot.

Methods:

We conducted a retrospective study in two Belgian hospitals (Sept 2021–Jun 2023). Adults (≥18 years) with MDD were identified via DSM-IV/ICD-10 codes or natural language processing (NLP)-detected note mentions; bipolar depression was excluded. TRD was defined as initiation of a third distinct antidepressant, supplemented by explicit mentions of TRD in notes. Structured data (demographics, diagnoses, medications, hospitalizations) were harmonized in an Observational Medical Outcomes Partnership (OMOP) warehouse. Free-text notes were processed with an NLP pipeline to capture symptoms, psychiatric comorbidities, and contextual events.

Results:

We identified 1147 adults with MDD, of which 46% met TRD criteria. Females comprised 62.9% and mean (SD) age was 57.8 (18.4) years. Mortality was 13.3% overall (10.9% TRD vs 15.2% non-TRD). Common medical comorbidities were central nervous system diseases (41.6%) and heart diseases (30.4%). Dementia was more frequent in TRD (8.0% vs 5.1%), whereas obesity was higher in non-TRD (11.2% vs 8.8%). Anxiety disorder occurred in 35.4% overall and was more prevalent in TRD (43.7% vs 28.4%); personality and panic disorders also trended higher. Severity was sparsely documented (severe MDD 14.8%) and standardized scales were rarely recorded.

Conclusions:

We present a step-by-step methodology tailored for clinicians, discussing challenges in integrating RWE into psychiatry, and identifying opportunities to enhance data collection with minimal workflow changes, which emphasizes the transformative potential of RWE systems in mental health research.