Computable phenotypes for multimorbidity measurement: development and implementation of a new electronic health...

To develop an electronic health record (EHR)-based multimorbidity-weighted index (eMWI) using computable phenotypes mapped to the Observational Medical Outcomes Partnership (OMOP) common data model and previously validated physical functioning-based condition weights.

We constructed eMWI from the previously validated MWI, which includes 76 chronic conditions weighted by their impact on physical functioning. For each condition, we identified validated EHR case algorithms through targeted literature search. For conditions without published algorithms, we performed chart review validation of ICD-based definitions in UCLA EHR. We translated all condition algorithms into OMOP computable phenotypes to enable use across harmonized EHR datasets. We applied eMWI to adults ≥18 years with ≥2 outpatient encounters between 2012 and 2024 in the University of California Health Data Warehouse (UCHDW), a harmonized dataset spanning 6 health systems.

Validated algorithms were available for 53/76 conditions. Chart review validation supported ≥1 International Classification of Diseases, Ninth and Tenth Revisions (ICD-9 and ICD-10) code definitions for 22/23 remaining conditions. We translated 76 condition definitions into OMOP phenotypes and applied eMWI in UCHDW. Final sample included 4 672 662 adults with mean ± SD age 46.3 ± 16.8 years, eMWI 5.66 ± 7.85 (range 0-91), 55.7% female, and 45.5% non-Hispanic White. eMWI increased with age and social vulnerability and captured wide distributions across population subgroups.

eMWI enables scalable implementation of multimorbidity measurement in large harmonized EHR datasets. Its weighting to physical functioning preserves a person-centered approach while adapting disease ascertainment to rich, clinical information in EHRs. eMWI establishes a foundation for rigorous comorbidity adjustment, risk stratification, and population health research across health systems and harmonized EHR datasets.