Dataset Information

The importance of health insurance claims data in creating learning health systems: evaluating care for high-need high-cost patients using the National Patient-Centered Clinical Research Network (PCORNet).

ABSTRACT:

Objective

Case management programs for high-need high-cost patients are spreading rapidly among health systems. PCORNet has substantial potential to support learning health systems in rapidly evaluating these programs, but access to complete patient data on health care utilization is limited as PCORNet is based on electronic health records not health insurance claims data. Because matching cases to comparison patients on baseline utilization is often a critical component of high-quality observational comparative effectiveness research for high-need high-cost patients, limited access to claims may negatively affect the quality of the matching process. We sought to determine whether the evaluation of programs for high-need high-cost patients required claims data to match cases to comparison patients.

Materials and methods

A retrospective cohort study design with multiple measures of before-and-after health care utilization for 1935 case management patients and 3833 matched comparison patients aged 18 years and older from 2011 to 2015. EHR and claims data were extracted from 3 health systems participating in PCORNet.

Results

Without matching on claims-based health care utilization, the case management programs at 2 of 3 health systems were associated with fewer hospital admissions and emergency visits over the subsequent 12 months. With matching on claims-based health care utilization, case management was no longer associated with admissions and emergency visits at those 2 programs.

Discussion

The results of a PCORNet-facilitated evaluation of 3 programs for high-need high-cost patients differed substantially depending on whether claims data were available for matching cases to comparison patients.

Conclusions

Partnering with learning health systems to rapidly evaluate programs for high-need high-cost patients will require that PCORNet facilitates comprehensive and timely access to both electronic health records and health insurance claims data.

SUBMITTER: Smith MA

PROVIDER: S-EPMC7647219 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The importance of health insurance claims data in creating learning health systems: evaluating care for high-need high-cost patients using the National Patient-Centered Clinical Research Network (PCORNet).

Smith Maureen A MA Vaughan-Sarrazin Mary S MS Yu Menggang M Wang Xinyi X Nordby Peter A PA Vogeli Christine C Jaffery Jonathan J Metlay Joshua P JP

Journal of the American Medical Informatics Association : JAMIA 20191101 11

<h4>Objective</h4>Case management programs for high-need high-cost patients are spreading rapidly among health systems. PCORNet has substantial potential to support learning health systems in rapidly evaluating these programs, but access to complete patient data on health care utilization is limited as PCORNet is based on electronic health records not health insurance claims data. Because matching cases to comparison patients on baseline utilization is often a critical component of high-quality ...[more]

PMID: 31233126

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Project description:BackgroundPredictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models.MethodsA random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented.ResultsBoth diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status.ConclusionsPredicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling.

Dataset Information

The importance of health insurance claims data in creating learning health systems: evaluating care for high-need high-cost patients using the National Patient-Centered Clinical Research Network (PCORNet).

Objective

Materials and methods

Results

Discussion

Conclusions

Publications

The importance of health insurance claims data in creating learning health systems: evaluating care for high-need high-cost patients using the National Patient-Centered Clinical Research Network (PCORNet).

Similar Datasets

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