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Defining and Assessing Geriatric Risk Factors and Associated Health Care Utilization Among Older Adults Using Claims
Hong J Kan1, Hadi Kharrazi1, Bruce Leff2
1Department of Health Policy and Management, Center for Population Health IT, Johns Hopkins Bloomberg School of Public Health.
Integrating electronic health records (EHRs) with claims data enhances the identification of geriatric risk factors in older adults, improving population health management and predicting healthcare utilization.
Area of Science:
- Geriatric Medicine
- Health Informatics
- Population Health Management
Background:
- Identifying geriatric risk factors is crucial for managing older adults' health.
- Traditional methods using claims data may not capture the full spectrum of risk.
- Electronic Health Records (EHRs) offer a more comprehensive data source.
Purpose of the Study:
- To define and compare geriatric risk factors derived from claims, structured EHRs, and unstructured EHRs.
- To estimate the association between identified geriatric risk factors and healthcare utilization.
Main Methods:
- Retrospective cohort study of Medicare Advantage enrollees (2011-2013).
- Defined 10 geriatric risk factors and a summary risk index using claims and EHR data (structured and unstructured).
- Logistic regression used to analyze the association between geriatric risk index and healthcare utilization.
Main Results:
- Incorporating unstructured EHR data significantly increased the prevalence of identified geriatric risk factors compared to claims or structured EHRs alone.
- A higher geriatric risk index was statistically associated with increased hospitalizations, emergency department visits, and nursing home stays.
- Odds ratios for hospitalization increased with higher geriatric risk scores, demonstrating predictive value.
Conclusions:
- EHRs and claims data can be effectively utilized to gather novel geriatric risk information.
- This approach can augment existing disease-specific data for proactive management of high-risk older patients.
- Feasible to identify and manage at-risk older patients earlier through comprehensive data integration.
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