Incorporating patient-reported outcome measures into the electronic health record for research: application using the...

Electronic health records (EHRs) present an opportunity to access large stores of data for research, but mapping raw EHR data to clinical phenotypes is complex. We propose adding patient-reported data to the EHR to improve phenotyping performance and describe a retrospective cohort study demonstrating a test case in depressive disorder.

We compared four EHR-phenotyping methods based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, medication records, and the Patient Health Questionnaire 9 (PHQ-9) regarding the ability to identify cases with depression and characteristics of patients identified with depression. Our sample included 168,884 patients seen (2007-2013) at our neurological institute. We assessed the diagnostic performance in a subset of 225 patients who had a reference standard measurement available.

ICD-9-CM codes identified the fewest number of patients as depressed (4,658), followed by PHQ-9 (46,565), and medication data (50,505). The presence of at least one of these criteria identified the largest number (78,322). The PHQ-9 identified a higher proportion of elderly, disabled, Medicaid, and rural patients, as compared to ICD-9-CM codes. ICD-9-CM codes were least sensitive (6.7% sensitivity), whereas the method using at least one of the criteria identified the highest number of truly depressed patients (93.3% sensitivity); however, specificity dropped from 97.7 to 58.1%.

The choice of phenotyping method may disproportionately exclude patient groups from research. Patient-reported data hold potential to improve sensitivity while maintaining an acceptable loss of specificity, depending on the context. Researchers should consider including patient-reported data in EHR-driven phenotyping methods.