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Identifying Future High-Healthcare Users

Exploring the Value of Diagnostic and Prior Utilization Information

  • Original Research Article
  • Published:
Disease Management & Health Outcomes

Abstract

Objective

Diagnosis-based risk-adjustment measures are increasingly being promoted as disease management tools. We compared the ability of several types of predictive models to identify future high-risk older people likely to benefit from disease management.

Study design

Veterans Health Administration (VHA) data were used to identify veterans ≥65 years of age who used healthcare services during fiscal years (FY) 1997 and 1998 and who remained alive through FY 1997. This yielded a development sample of 412 679 individuals and a validation sample of 207 294.

Methods

Prospective risk-adjustment models were fitted and tested using Adjusted Clinical Groups (ACGs), Diagnostic Cost Groups (DCGs), a prior-utilization model (prior), and combined models (prior + ACGs and prior + DCGs). Prespecified high use in FY 1998 was defined as ≥92 days of care (top 2.2%) for an individual (i.e. the number of days during the year in which an individual received inpatient or outpatient healthcare services). We developed a second outcome, defined as ≥164 days of care (top 1.0%), to explore whether changing the criterion for high risk would affect the number of misclassifications.

Results

The diagnosis-based models performed better than the prior model in identifying a subgroup of future high-cost individuals with high disease burden and chronic diseases appropriate for disease management. The combined models performed best at correctly classifying those without high use in the prospective year. The utility for efficiently identifying high-risk cases appeared limited because of the high number of individuals misclassified as future high-risk cases by all the models. Changing the criterion for high risk generally decreased the number of patients misclassified. There was little agreement between the models regarding who was identified as high risk.

Conclusion

Health plans should be aware that different risk-adjustment measures may select dissimilar groups of individuals for disease management. Although diagnosis-based measures show potential as predictive modeling tools, combining a diagnosis-based measure with prior-utilization model may yield the best results.

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Acknowledgments

Supported by grant number MPC 97-009, the Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development (HSR&D) Service.

We gratefully acknowledge the assistance of Priti Shokeen in manuscript preparation. Versions of this paper have been presented at the meetings of the International Conference on Health Policy Research (ICHPR), American Statistical Association (December 2001, Boston, MA, USA and October 2003, Chicago, IL, USA), and the Academy for Health Services Research and Health Policy Annual Meeting (June 2002, Washington, DC, USA). Dr Rosen is a Senior Research Scientist at the Center for Health Quality, Outcomes, and Economic Research in Bedford, MA, USA, and an Associate Professor of Health Services, Boston University School of Public Health, Boston, MA, USA. The views expressed are solely those of the authors.

The authors have no conflicts of interest that are directly relevant to the content of this study.

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Correspondence to Amy K. Rosen.

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Rosen, A.K., Wang, F., Montez, M.E. et al. Identifying Future High-Healthcare Users. Dis-Manage-Health-Outcomes 13, 117–127 (2005). https://doi.org/10.2165/00115677-200513020-00005

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  • DOI: https://doi.org/10.2165/00115677-200513020-00005

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