# New EHR-based multimorbidity index for diverse populations across the lifespan: development, validation, and application

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2024 · $611,706

## Abstract

Project Summary / Abstract
Virtually all U.S. adults will develop multimorbidity (coexistence of multiple chronic conditions) by late
adulthood. The sequelae are substantial: vulnerability to acute illness, disease exacerbation, hospitalization,
disability, poor health-related quality of life, and mortality. Despite this, there are no viable, patient-centered
measures for multimorbidity in the electronic health record (EHR) that include a comprehensive inventory of
conditions based on their impacts on physical functioning in community-dwelling adults and are thus broadly
applicable for the general population. The absence of such tools impedes systematic efforts to develop
effective interventions for patients with multimorbidity. To bridge these gaps, this proposal aims to develop and
validate a robust, clinically relevant, readily-available EHR-based multimorbidity-weighted index (eMWI) that
accurately ascertains disease presence using EHR data and is applicable for diverse populations across the
lifespan. The central hypothesis is that a comprehensive multimorbidity index that weights conditions based on
their impacts on physical functioning can more precisely quantify multimorbidity and provide a better model fit
to predict key health outcomes than prior measures. This hypothesis is strongly supported by our preliminary
results using large national surveys and survey-linked claims data, in which we rigorously developed and
validated a comprehensive set of 91 chronic conditions weighted by their average impacts on physical
functioning over the disease life course, thus incorporating illness burden and physical functioning into a
clinically meaningful measure applicable for the general population. As a transformative step for multimorbidity
measurement in patient care, population health, and research using EHR data, the team aims to 1) improve
multimorbidity measurement by more accurately ascertaining disease cases, and merging these with validated
physical functioning disease weights to create a new patient-centered eMWI applicable to diverse populations;
2) assess the validity of eMWI via its association with key clinical outcomes: multimorbidity progression,
hospitalization, and mortality; and 3) test the applicability of eMWI to national population health and policy by
applying it to evaluate the risk of severe and fatal COVID-19 among vaccinated vs. unvaccinated adults based
on their multimorbidity. This study uses large, diverse EHR data from 6 California health systems (>6 million
adults) with unique data linkages to census data, and the largest, most nationally-representative National
COVID Cohort Collaborative (N3C) dataset (>5 million COVID cases). The results will yield a new, validated,
patient-centered multimorbidity index for EHR data – the eMWI – to help guide clinical decisions, population-
health management, policy, and research for diverse populations. The team anticipates that eMWI can directly
impact future practice and outcom...

## Key facts

- **NIH application ID:** 10933500
- **Project number:** 5R01AG083370-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Melissa Wei
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $611,706
- **Award type:** 5
- **Project period:** 2023-09-30 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10933500

## Citation

> US National Institutes of Health, RePORTER application 10933500, New EHR-based multimorbidity index for diverse populations across the lifespan: development, validation, and application (5R01AG083370-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10933500. Licensed CC0.

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