Comparing Population-based Risk-stratification Model Performance Using Demographic, Diagnosis and Medication Data Extracted from Outpatient Electronic Health Records Versus Administrative Claims

Hadi Kharrazi, Winnie Chi, Hsien Yen Chang, Thomas M. Richards, Jason M. Gallagher, Susan M. Knudson, Jonathan P. Weiner

Research output: Contribution to journalArticle

Abstract

Background: There is an increasing demand for electronic health record (EHR)-based risk stratification and predictive modeling tools at the population level. This trend is partly due to increased value-based payment policies and the increasing availability of EHRs at the provider level. Risk stratification models, however, have been traditionally derived from claims or encounter systems. This study evaluates the challenges and opportunities of using EHR data instead of or in addition to administrative claims for risk stratification. Methods: This study used the structured EHR records and administrative claims of 85,581 patients receiving outpatient care at a large integrated provider system. Common data elements for risk stratification (ie, age, sex, diagnosis, and medication) were extracted from outpatient EHR records and administrative claims. The performance of a validated risk-stratification model was assessed using data extracted from claims alone, EHR alone, and claims and EHR combined. Results: EHR-derived metrics overlapped considerably with administrative claims (eg, number of chronic conditions). The accuracy of the model, when using EHR data alone, was acceptable with an area under the curve of ∼0.81 for hospitalization and ∼0.85 for identifying top 1% utilizers using the concurrent model. However, when using EHR data alone, the predictive model explained a lower amount of variation in utilization-based outcomes compared with administrative claims. Discussion: The results show a promising performance of models predicting cost and hospitalization using outpatient EHR's diagnosis and medication data. More research is needed to evaluate the benefits of other EHR data types (eg, lab values and vital signs) for risk stratification.

LanguageEnglish (US)
Pages789-796
Number of pages8
JournalMedical Care
Volume55
Issue number8
DOIs
StatePublished - 2017

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Electronic Health Records
Outpatients
Demography
Population
Hospitalization
Vital Signs
Ambulatory Care
Area Under Curve
Costs and Cost Analysis

Keywords

  • administrative claims
  • electronic health records
  • predictive modeling
  • risk stratification
  • The Johns Hopkins ACG System

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

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title = "Comparing Population-based Risk-stratification Model Performance Using Demographic, Diagnosis and Medication Data Extracted from Outpatient Electronic Health Records Versus Administrative Claims",
abstract = "Background: There is an increasing demand for electronic health record (EHR)-based risk stratification and predictive modeling tools at the population level. This trend is partly due to increased value-based payment policies and the increasing availability of EHRs at the provider level. Risk stratification models, however, have been traditionally derived from claims or encounter systems. This study evaluates the challenges and opportunities of using EHR data instead of or in addition to administrative claims for risk stratification. Methods: This study used the structured EHR records and administrative claims of 85,581 patients receiving outpatient care at a large integrated provider system. Common data elements for risk stratification (ie, age, sex, diagnosis, and medication) were extracted from outpatient EHR records and administrative claims. The performance of a validated risk-stratification model was assessed using data extracted from claims alone, EHR alone, and claims and EHR combined. Results: EHR-derived metrics overlapped considerably with administrative claims (eg, number of chronic conditions). The accuracy of the model, when using EHR data alone, was acceptable with an area under the curve of ∼0.81 for hospitalization and ∼0.85 for identifying top 1\{%} utilizers using the concurrent model. However, when using EHR data alone, the predictive model explained a lower amount of variation in utilization-based outcomes compared with administrative claims. Discussion: The results show a promising performance of models predicting cost and hospitalization using outpatient EHR's diagnosis and medication data. More research is needed to evaluate the benefits of other EHR data types (eg, lab values and vital signs) for risk stratification.",
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AU - Kharrazi,Hadi

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AU - Chang,Hsien Yen

AU - Richards,Thomas M.

AU - Gallagher,Jason M.

AU - Knudson,Susan M.

AU - Weiner,Jonathan P.

PY - 2017

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N2 - Background: There is an increasing demand for electronic health record (EHR)-based risk stratification and predictive modeling tools at the population level. This trend is partly due to increased value-based payment policies and the increasing availability of EHRs at the provider level. Risk stratification models, however, have been traditionally derived from claims or encounter systems. This study evaluates the challenges and opportunities of using EHR data instead of or in addition to administrative claims for risk stratification. Methods: This study used the structured EHR records and administrative claims of 85,581 patients receiving outpatient care at a large integrated provider system. Common data elements for risk stratification (ie, age, sex, diagnosis, and medication) were extracted from outpatient EHR records and administrative claims. The performance of a validated risk-stratification model was assessed using data extracted from claims alone, EHR alone, and claims and EHR combined. Results: EHR-derived metrics overlapped considerably with administrative claims (eg, number of chronic conditions). The accuracy of the model, when using EHR data alone, was acceptable with an area under the curve of ∼0.81 for hospitalization and ∼0.85 for identifying top 1% utilizers using the concurrent model. However, when using EHR data alone, the predictive model explained a lower amount of variation in utilization-based outcomes compared with administrative claims. Discussion: The results show a promising performance of models predicting cost and hospitalization using outpatient EHR's diagnosis and medication data. More research is needed to evaluate the benefits of other EHR data types (eg, lab values and vital signs) for risk stratification.

AB - Background: There is an increasing demand for electronic health record (EHR)-based risk stratification and predictive modeling tools at the population level. This trend is partly due to increased value-based payment policies and the increasing availability of EHRs at the provider level. Risk stratification models, however, have been traditionally derived from claims or encounter systems. This study evaluates the challenges and opportunities of using EHR data instead of or in addition to administrative claims for risk stratification. Methods: This study used the structured EHR records and administrative claims of 85,581 patients receiving outpatient care at a large integrated provider system. Common data elements for risk stratification (ie, age, sex, diagnosis, and medication) were extracted from outpatient EHR records and administrative claims. The performance of a validated risk-stratification model was assessed using data extracted from claims alone, EHR alone, and claims and EHR combined. Results: EHR-derived metrics overlapped considerably with administrative claims (eg, number of chronic conditions). The accuracy of the model, when using EHR data alone, was acceptable with an area under the curve of ∼0.81 for hospitalization and ∼0.85 for identifying top 1% utilizers using the concurrent model. However, when using EHR data alone, the predictive model explained a lower amount of variation in utilization-based outcomes compared with administrative claims. Discussion: The results show a promising performance of models predicting cost and hospitalization using outpatient EHR's diagnosis and medication data. More research is needed to evaluate the benefits of other EHR data types (eg, lab values and vital signs) for risk stratification.

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