Declining Accuracy in Disease Classification on Health Insurance Claims: Should We Reconsider Classification by Principal Diagnosis?

BACKGROUND: An ideal classification should have maximum intercategory variance and minimal intracategory variance. Health insurance claims typically include multiple diagnoses and are classified into different disease categories by choosing principal diagnoses. The accuracy of classification based on principal diagnoses was evaluated by comparing intercategory and intracategory variance of per-claim costs and the trend in accuracy was reviewed. METHODS: Means and standard deviations of log-transformed per-claim costs were estimated from outpatient claims data from the National Health Insurance Medical Benefit Surveys of 1995 to 2007, a period during which only the ICD10 classification was applied. Intercategory and intracategory variances were calculated for each of 38 mutually exclusive disease categories and the percentage of intercategory variance to overall variance was calculated to assess the trend in accuracy of classification. RESULTS: A declining trend in the percentage of intercategory variance was observed: from 19.5% in 1995 to 10% in 2007. This suggests that there was a decline in the accuracy of disease classification in discriminating per-claim costs for different disease categories. The declining trend temporarily reversed in 2002, when hospitals and clinics were directed to assign the principal diagnosis. However, this reversal was only temporary and the declining trend appears to be consistent. CONCLUSIONS: Classification of health insurance claims based on principal diagnoses is becoming progressively less accurate in discriminating per-claim costs. Researchers who estimate disease-specific health care costs using health insurance claims must therefore proceed with caution.