Patient safety analysis linking claims and administrative data

The purpose of this paper is to provide international data on the occurrence (and rates) of clinical errors, identified by type and consequence in the Lombardy region, and to assess empirically the association between hospital accreditation-type measures and clinical error rates by merging hospital discharge records and medical malpractice claim data in the Lombardy region (Italy).

Data were drawn from the regional database collecting claims and demands for reimbursement declared by patients hospitalized in regional healthcare structures and regional archives collecting hospital discharge records. To model the variability of clinical errors rates, binomial negative regression models were applied. For improved interpretation of the results, a regression tree methodology was used.

The results demonstrated that the rate of readmission for the same major diagnostic category and the rate of discharges against medical advice significantly affect the incidence of errors causing patient death, whereas the rate of unscheduled surgical readmission in the operating room significantly affects the rate of surgical error.

The findings confirm that claims data is problematic in nature because of the limited number of claims generally emerging from administrative sources. The article proposes using proper regression models for count data, taking into account over-dispersion and excess zeroes and classification tree methods for a better interpretation of empirical evidence.

Health structures where quality outcomes have a significant impact on clinical error rates should be monitored in depth, investigating the medical charts of involved patients to identify quality problems and problematic areas.

As a risk management strategy, the combined use of claims data and clinical administrative data is proposed to shed light on the more problematic, error-prone areas, allowing regional stakeholders to receive relevant, highly cost-effective and timely information and an in-depth understanding of the problematic areas in the assessment of risk.