Background The aim of this study was to characterize patterns of multimorbidity across patients and identify opportunities to strengthen the informatics capacity of learning health systems that are used to characterize multimorbidity across patients. Methods Electronic health record (EHR) data on 225,710 multimorbidity patients were extracted from the Arkansas Clinical Data Repository as a use case. Hierarchical cluster analysis identified the most frequently occurring combinations of chronic conditions within the learning health system’s captured data. Results Results revealed multimorbidity was highest among patients ages 60 to 74, Caucasians, females, and Medicare payors. The largest numbers of chronic conditions occurred in the smallest numbers of patients (i.e., 70,262 (31%) patients with two conditions, two (<1%) patients with 22 chronic conditions). The results revealed urgent needs to improve EHR systems and processes that collect and manage multimorbidity data (e.g., creating new, multimorbidity-centric data elements in EHR systems, detailed longitudinal tracking of compounding disease diagnoses). Conclusions Without additional capacity to collect and aggregate large-scale data, multimorbidity patients cannot benefit from the recent advancements in informatics (i.e., clinical data registries, emerging data standards) that are abundantly working to improve the outcomes of patients with single chronic conditions. Additionally, robust socio-technical system studies of clinical workflows are needed to assess the feasibility of integrating the collection of risk factor data elements (i.e., psycho-social, cultural, ethnic, and socioeconomic attributes of populations) into primary care encounters. These approaches to advancing learning health systems for multimorbidity could substantially reduce the constraints of current technologies, data, and data-capturing processes.

OBJECTIVES/GOALS: 1) Characterize racial differences in congestive heart failure care delivery. 2) Examine the extent to which specific clinical roles were associated with improved care outcomes (i.e., hospitalizations, readmissions, days between readmissions, and charges) of African Americans (AA) with CHF. METHODS/STUDY POPULATION: EMR data was extracted from the Arkansas Clinical Data Repository (AR-CDR) on patients (ages 18-105) who received care between January 1, 2014 and December 31, 2021. Variables included age, sex, race, ethnicity, rurality, clinical diagnosis, morbidities, medical history, medications, heart failure phenotypes, and care delivery team composition. Binomial logistic regression ascertained the effects of these variables on patient’s care outcomes. A Mann Whitney-U test identified racial differences in outcomes. Psychometrically, classical test theory and item response theory assessed items for the risk surveillance tool. RESULTS/ANTICIPATED RESULTS: The study identified 5,962 CHF patients who generated 80,921 care encounters. The results revealed the disproportionate impact of CHF prevalence, hospitalizations, and readmissions on AAs. AAs had a significantly higher number of hospitalizations (i.e., 50% more) than Caucasians. Specific clinical roles (i.e., MDs, RNs, Care Managers) were consistently associated with 30% or greater decrease in odds of hospitalization and readmission, even when stratified by heart failure phenotype. Classical test theory results (e.g., Cronbach’s alpha; 0.88) indicated the set of items on the risk surveillance tool accurately reflect a patient risk for improved outcomes. DISCUSSION/SIGNIFICANCE: The findings stimulate the need for 1) EHR-based tools that manage care delivery equity and 2) investigations of specific clinical roles in risk stratifying and operationalizing the care plans of AAs, advancing formal access-to-care frameworks by ensuring access to clinical roles that are associated with improved outcomes.