A Dirichlet-Multinomial Mixture Model for Evaluating Hospital Diagnostic Performance Accounting for Cross-Over Events

Music City Center 

Improving healthcare quality relies heavily on accurately evaluating and reducing misdiagnosis-related risk. Traditionally, these efforts have centered on the chart review process, which is often hindered by incomplete documentation, low inter-rater reliability, and hindsight bias. To better assess diagnostic performance and highlight areas for improvement, researchers have suggested leveraging electronic health records (EHRs) within the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) framework. However, relying solely on internal EHRs introduces bias, as it overlooks cross-over events when patients seek follow-up care outside the hospital of the initial visit. Additionally, the low incidence of many diseases, such as stroke, increases uncertainty in assessing misdiagnosis risk. To address these issues, we propose a Dirichlet-Multinomial mixture model, through regression, to estimate the distribution of misdiagnosis-related harm across hospitals and to predict misdiagnosis probabilities. Our model further enables the examination of covariates that may influence misdiagnosis-related risk, providing hospitals with actionable insights to reduce this risk. We evaluate our approach using simulation studies and apply it to dizziness-stroke occurrence data from the Healthcare Cost and Utilization Project (HCUP). Through these analyses, we assess misdiagnosis risk across 216 hospitals in the dataset and identify relationships between risk of harm and hospital characteristics, such as neurological examination coverage and symptom-related patient volume.

Misdiagnosis-related harm

Mixture model

Electronic health records

Health care improvement