Benchmarking clinical practice in surgery: looking beyond traditional mortality rates

Ricardo A. S. Castro, Pedro N. Oliveira, Conceição Silva Portela, Ana S. Camanho, João Queiroz e Melo

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This paper proposes two new measures to assess performance of surgical practice based on observed mortality: reliability, measured as the area under the ROC curve and a living score, the sum of individual risk among surviving patients, divided by the total number of patients. A Monte Carlo simulation of surgeons' practice was used for conceptual validation and an analysis of a real-world hospital department was used for managerial validation. We modelled surgical practice as a bivariate distribution function of risk and final state. We sampled 250 distributions, varying the maximum risk each surgeon faced, the distribution of risk among dead patients, the mortality rate and the number of surgeries performed yearly. We applied the measures developed to a Portuguese cardiothoracic department. We found that the joint use of the reliability and living score measures overcomes the limitations of risk adjustedmortality rates, as it enables a different valuation of deaths, according to their risk levels. Reliability favours surgeons with casualties, predominantly, in high values of risk and penalizes surgeons with deaths in relatively low levels of risk. The living score is positively influenced by the maximum risk for which a surgeon yields surviving patients. These measures enable a deeper understanding of surgical practice and, as risk adjusted mortality rates, they rely only on mortality and risk scores data. The case study revealed that the performance of the department analysed could be improved with enhanced policies of risk management, involving the assignment of surgeries based on surgeon's reliability and living score.
Original languageEnglish
Pages (from-to)431-443
Number of pages13
JournalHealth Care Management Science
Volume18
Issue number4
DOIs
Publication statusPublished - 1 Dec 2015

Keywords

  • Quality measurement
  • Patient outcomes
  • Simulation and modelling
  • ROC curves

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