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Case-Mix for Performance Management: A Risk Algorithm Based on ICD-10-CM.

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Developing a new case-mix algorithm using expanded diagnostic categories significantly improves healthcare cost prediction accuracy. This enhanced risk adjustment method offers better provider performance assessment for hospitals and payers.

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Area of Science:

  • Health Services Research
  • Medical Informatics
  • Health Economics

Background:

  • Accurate risk adjustment is crucial for comparing healthcare provider and hospital performance.
  • Current algorithms, based on age, sex, and diagnoses, explain only up to 50% of cost variations.
  • There is a need for more precise risk adjustment methods for evaluating and improving provider performance.

Purpose of the Study:

  • To develop an advanced case-mix algorithm for healthcare providers and payers.
  • The algorithm aims to enhance the measurement and comparison of provider cost and quality performance.

Main Methods:

  • Utilized data from 6,048,895 patients in the US Veterans health care system (FY2016).
  • Developed a case-mix algorithm using age, sex, and expanded comorbidities (762 groups from CCS based on ICD-10-CM).
  • Employed split-sample validation and compared predictive metrics (R, Mape, RMSE, predictive ratios, c-statistics).

Main Results:

  • Expanding Clinical Classifications Software categories enhanced predictive power.
  • Achieved an R-squared of 0.72 for transformed cost and 0.52 for raw scale cost.
  • The developed algorithm demonstrated superior predictive capabilities compared to existing models.

Conclusions:

  • The novel case-mix algorithm, incorporating expanded diagnoses, surpasses current models in accuracy.
  • This methodology enables health systems to create customized risk adjustment models.
  • The improved risk adjustment facilitates more reliable assessments of provider cost and quality performance.