Defining and Assessing Geriatric Risk Factors and Associated Health Care Utilization Among Older Adults Using Claims and Electronic Health Records

Datos Bibliográficos

ID9101062
AutoresHong J Kan (Department of Health Policy and Management, Center for Population Health IT, Johns Hopkins Bloomberg School of Public Health), Hong Kan (0000-0002-1685-791X, Johns Hopkins University, autor de correspondencia), Hadi Kharrazi (0000-0003-1481-4323, Department of Health Policy and Management, Center for Population Health IT, Johns Hopkins Bloomberg School of Public Health, autor de correspondencia), Bruce Leff (0000-0003-1714-7458, Division of Geriatric Medicine, Center for Transformative Geriatric Research, Johns Hopkins University School of Medicine, Baltimore, MD), Cynthia Boyd (Division of Geriatric Medicine, Center for Transformative Geriatric Research, Johns Hopkins University School of Medicine, Baltimore, MD), Cynthia M Boyd (0000-0001-5642-9015, Johns Hopkins University), Ashwini Davison (Division of Geriatric Medicine, Center for Transformative Geriatric Research, Johns Hopkins University School of Medicine, Baltimore, MD), Hsien-Yen Chang (Department of Health Policy and Management, Center for Population Health IT, Johns Hopkins Bloomberg School of Public Health), Hsien‐Yen Chang (0000-0002-7997-4822, Johns Hopkins University, autor de correspondencia), Joe Kimura (0000-0001-7713-9826, Atrius Health, Newton), Shannon Wu (0000-0002-0959-0743, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health), Laura Anzaldi (Department of Health Policy and Management, Center for Population Health IT, Johns Hopkins Bloomberg School of Public Health, autor de correspondencia), Tom Richards (0000-0002-4480-9624, Department of Health Policy and Management, Center for Population Health IT, Johns Hopkins Bloomberg School of Public Health, autor de correspondencia), Elyse C Lasser (0000-0002-1758-9822, Department of Health Policy and Management, Center for Population Health IT, Johns Hopkins Bloomberg School of Public Health, autor de correspondencia), Jonathan P Weiner (0000-0002-8299-3995, Department of Health Policy and Management, Center for Population Health IT, Johns Hopkins Bloomberg School of Public Health, autor de correspondencia)
Año2018
Volumen56
Número3
Páginas233-239
Fecha de publicación2018-03-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaMedical Care (JOURNAL)
Identificadores de la revistaISSN: 0025-7079 • E-ISSN: 1537-1948
EditorialOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000000865
PMID29438193
OpenAlexW2792056444
IdiomaEN
Citas recibidas3
Referencias citadas36

BACKGROUND: 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. OBJECTIVE: 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. RESEARCH DESIGN: 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. RESULTS: 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. CONCLUSIONS: 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

Comorbidity · Emergency department · Family medicine · Geriatrics · Health care · Logistic regression · Medicare Advantage · Odds ratio · Psychiatry · Chronic Disease Management Strategies · Frailty in Older Adults · Heart Failure Treatment and Management · Internal Medicine · Medicine · Gerontology