Using electronic health records (EHRs), in addition to claims, to systematically identify patients with factors associated with adverse outcomes (geriatric risk) among older adults can prove beneficial for population health management and clinical service delivery.
To define and compare geriatric risk factors derivable from claims, structured EHRs, and unstructured EHRs, and estimate the relationship between geriatric risk factors and health care utilization.
We performed a retrospective cohort study of patients enrolled in a Medicare Advantage plan from 2011 to 2013 using both administrative claims and EHRs. We defined 10 individual geriatric risk factors and a summary geriatric risk index based on diagnosed conditions and pattern matching techniques applied to EHR free text. The prevalence of geriatric risk factors was estimated using claims, structured EHRs, and structured and unstructured EHRs combined. The association of geriatric risk index with any occurrence of hospitalizations, emergency department visits, and nursing home visits were estimated using logistic regression adjusted for demographic and comorbidity covariates.
The prevalence of geriatric risk factors increased after adding unstructured EHR data to structured EHRs, compared with those derived from structured EHRs alone and claims alone. On the basis of claims, structured EHRs, and structured and unstructured EHRs combined, 12.9%, 15.0%, and 24.6% of the patients had 1 geriatric risk factor, respectively; 3.9%, 4.2%, and 15.8% had ≥2 geriatric risk factors, respectively. Statistically significant association between geriatric risk index and health care utilization was found independent of demographic and comorbidity covariates. For example, based on claims, estimated odds ratios for having 1 and ≥2 geriatric risk factors in year 1 were 1.49 (P<0.001) and 2.62 (P<0.001) in predicting any occurrence of hospitalizations in year 1, and 1.32 (P<0.001) and 1.34 (P=0.003) in predicting any occurrence of hospitalizations in year 2.
The results demonstrate the feasibility and potential of using EHRs and claims for collecting new types of geriatric risk information that could augment the more commonly collected disease information to identify and move upstream the management of high-risk cases among older patients.
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*Department of Health Policy and Management, Center for Population Health IT, Johns Hopkins Bloomberg School of Public Health
∥Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health
†Division of Geriatric Medicine, Center for Transformative Geriatric Research, Johns Hopkins University School of Medicine, Baltimore, MD
‡Atrius Health, Newton
§Department of Population Medicine, Harvard Medical School, Boston, MA
This work was performed with support by faculty and staff at The Johns Hopkins University, where the ACG method was developed and is maintained. The Johns Hopkins University holds the copyright to the ACG software. To help support research and development, The Johns Hopkins University receives royalties from health plans and other organizations that use the ACG software. This study was funded by Atrius Health and Center for Population Health IT, Johns Hopkins University. C.B. wrote a chapter on multimorbidity for Uptodate, for which she shares a royalty. The remaining authors declare no conflict of interest.
Reprints: Hong J. Kan, PhD, MPP, MA, Department of Health Policy and Management, Center for Population Health IT, Johns Hopkins Bloomberg School of Public Health, Hampton House HH502, 624 N. Broadway, Baltimore, MD 21205. E-mail: firstname.lastname@example.org.