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

Hongjun Kan, Hadi H K Kharrazi, Bruce A Leff, Cynthia Boyd, Ashwini Davison, Hsien-Yen Chang, Joe Kimura, Shannon Wu, Laura Anzaldi, Tom Richards, Elyse Lasser, Jonathan Weiner

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)233-239
Number of pages7
JournalMedical Care
Volume56
Issue number3
DOIs
StatePublished - Jan 1 2018

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Patient Acceptance of Health Care
Electronic Health Records
Geriatrics
Hospitalization
Comorbidity
Medicare Part C
Emergency Nursing
Demography
House Calls
Risk Management
Nursing Homes

Keywords

  • clinical information systems
  • functional status
  • geriatric assessment
  • health care delivery
  • health status
  • logistic regression
  • population health
  • utilization

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

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title = "Defining and Assessing Geriatric Risk Factors and Associated Health Care Utilization among Older Adults Using Claims and Electronic Health Records",
abstract = "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.",
keywords = "clinical information systems, functional status, geriatric assessment, health care delivery, health status, logistic regression, population health, utilization",
author = "Hongjun Kan and Kharrazi, {Hadi H K} and Leff, {Bruce A} and Cynthia Boyd and Ashwini Davison and Hsien-Yen Chang and Joe Kimura and Shannon Wu and Laura Anzaldi and Tom Richards and Elyse Lasser and Jonathan Weiner",
year = "2018",
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TY - JOUR

T1 - Defining and Assessing Geriatric Risk Factors and Associated Health Care Utilization among Older Adults Using Claims and Electronic Health Records

AU - Kan, Hongjun

AU - Kharrazi, Hadi H K

AU - Leff, Bruce A

AU - Boyd, Cynthia

AU - Davison, Ashwini

AU - Chang, Hsien-Yen

AU - Kimura, Joe

AU - Wu, Shannon

AU - Anzaldi, Laura

AU - Richards, Tom

AU - Lasser, Elyse

AU - Weiner, Jonathan

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

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KW - functional status

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KW - health status

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KW - population health

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