Literature DB >> 28850789

Predicting high-cost privately insured patients based on self-reported health and utilization data.

Peter J Cunningham1.   

Abstract

OBJECTIVES: To examine how well self-reported data on health, health behaviors, and healthcare utilization by a sample of privately insured patients predict whether they will incur high healthcare costs the following year. STUDY
DESIGN: A 2012 mail survey of autoworkers from Chrysler, Ford, and General Motors, with 3983 survey respondents linked to their health insurance claims data for 2012 and 2013.
METHODS: High healthcare costs are defined as being in the 75th percentile or higher of healthcare expenditures. Models that include combinations of claims-based measures of expenditures and morbidity and self-reported measures of health, health behaviors, and healthcare utilization are compared.
RESULTS: Claims-based measures of healthcare costs and comorbidity for 2012 were strong predictors of whether a patient would incur high healthcare costs in 2013 (C statistic = 0.78). Self-reported measures of chronic conditions, health status, health behaviors, and hospital use are also good predictors of high healthcare costs. However, even the most comprehensive model that included self-reported measures was not as accurate in predicting high healthcare costs (C statistic = 0.73).
CONCLUSIONS: Efficient targeting of high-cost patients is crucial to the success of innovative care delivery models that attempt to lower costs and improve quality of care through more intensive care management of patients. The results of this study show that in the absence of claims data on prior use and expenditures, patient-reported measures of health status and prior healthcare use are reasonable predictors of future healthcare costs for a privately insured population.

Entities:  

Mesh:

Year:  2017        PMID: 28850789

Source DB:  PubMed          Journal:  Am J Manag Care        ISSN: 1088-0224            Impact factor:   2.229


  6 in total

1.  Cost-Related Access Barriers, Medical Debt, and Dissatisfaction with Care Among Privately Insured Americans.

Authors:  Charlie M Wray; Lenny Lopez; Meena Khare; Salomeh Keyhani
Journal:  J Gen Intern Med       Date:  2022-09-27       Impact factor: 6.473

2.  Machine-learning-based prediction models for high-need high-cost patients using nationwide clinical and claims data.

Authors:  Itsuki Osawa; Tadahiro Goto; Yuji Yamamoto; Yusuke Tsugawa
Journal:  NPJ Digit Med       Date:  2020-11-11

3.  The cumulative impact of health insurance on health status.

Authors:  Abigail R Barker; Linda Li
Journal:  Health Serv Res       Date:  2020-07-22       Impact factor: 3.402

4.  Predicting Future Utilization Using Self-Reported Health and Health Conditions in a Longitudinal Cohort Study: Implications for Health Insurance Decision Support.

Authors:  Abigail R Barker; Karen E Joynt Maddox; Ellen Peters; Kristine Huang; Mary C Politi
Journal:  Inquiry       Date:  2021 Jan-Dec       Impact factor: 2.099

5.  Validation of the What Matters Index: A brief, patient-reported index that guides care for chronic conditions and can substitute for computer-generated risk models.

Authors:  John H Wasson; Lynn Ho; Laura Soloway; L Gordon Moore
Journal:  PLoS One       Date:  2018-02-22       Impact factor: 3.240

6.  How effective are population health surveys for estimating prevalence of chronic conditions compared to anonymised clinical data?

Authors:  T Whiffen; A Akbari; T Paget; S Lowe; R Lyons
Journal:  Int J Popul Data Sci       Date:  2020-06-12
  6 in total

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