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Misdiagnosis‐related harm quantification through mixture models and harm measures

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  • Yuxin Zhu
  • Zheyu Wang
  • David Newman‐Toker

Abstract

Investigating and monitoring misdiagnosis‐related harm is crucial for improving health care. However, this effort has traditionally focused on the chart review process, which is labor intensive, potentially unstable, and does not scale well. To monitor medical institutes' diagnostic performance and identify areas for improvement in a timely fashion, researchers proposed to leverage the relationship between symptoms and diseases based on electronic health records or claim data. Specifically, the elevated disease risk following a false‐negative diagnosis can be used to signal potential harm. However, off‐the‐shelf statistical methods do not fully accommodate the data structure of a well‐hypothesized risk pattern and thus fail to address the unique challenges adequately. To fill these gaps, we proposed a mixture regression model and its associated goodness‐of‐fit testing. We further proposed harm measures and profiling analysis procedures to quantify, evaluate, and compare misdiagnosis‐related harm across institutes with potentially different patient population compositions. We studied the performance of the proposed methods through simulation studies. We then illustrated the methods through data analyses on stroke occurrence data from the Taiwan Longitudinal Health Insurance Database. From the analyses, we quantitatively evaluated risk factors for being harmed due to misdiagnosis, which unveiled some insights for health care quality research. We also compared general and special care hospitals in Taiwan and observed better diagnostic performance in special care hospitals using various new evaluation measures.

Suggested Citation

  • Yuxin Zhu & Zheyu Wang & David Newman‐Toker, 2023. "Misdiagnosis‐related harm quantification through mixture models and harm measures," Biometrics, The International Biometric Society, vol. 79(3), pages 2633-2648, September.
  • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:2633-2648
    DOI: 10.1111/biom.13759
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    References listed on IDEAS

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    2. Ching-Chih Lee & Hsu-Chueh Ho & Yu-Chieh Su & Brian C-H Chiu & Yung-Cheng Su & Yi-Da Lee & Pesus Chou & Sou-Hsin Chien & Yung-Sung Huang, 2012. "Increased Risk of Vascular Events in Emergency Room Patients Discharged Home with Diagnosis of Dizziness or Vertigo: A 3-Year Follow-Up Study," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-9, April.
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