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首页> 外文期刊>The quarterly review of economics and finance >New information technology systems and a Bayesian hierarchical bivariate probit model for profiling surgeon quality at a large hospital
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New information technology systems and a Bayesian hierarchical bivariate probit model for profiling surgeon quality at a large hospital

机译：新的信息技术系统和贝叶斯等级二焦化概率模型，用于在大型医院剖析外科医生品质

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New information technology systems at hospitals and medical centers provide administrators and policymakers with greater information than ever before on both the characteristics of patients and on the outcomes associated with individual surgeons. This paper develops a Bayesian hierarchical bivariate probit model describing surgeon performance in terms of the 30-day mortality and 30-day morbidity of their patients. We apply the model to a sample of 2,578 patients who received care from one of 36 surgeons at a large hospital. The model is estimated using Markov Chain Monte Carlo (MCMC) simulation methods. After accounting for observed differences in the health status of patients prior to surgery and the complexity of the procedure performed, we construct quality indices measuring surgeon performance in terms of morbidity and mortality. These indices are used to evaluate surgeon performance against absolute standards for morbidity and mortality rates, and then to conduct "head to head" comparisons of individual surgeons within subspecialty surgery departments at the hospital. Our approach highlights the potential benefits of new information technologies for monitoring surgeon quality.
机译：医院和医疗中心的新信息技术系统为管理员和政策制定者提供比以往任何时候都更加多的信息，而不是患者的特征以及与个别外科医生相关的结果。本文开发了一个贝叶斯等级二焦化概率模型，描述了在30天死亡率和患者的30天发病率方面的外科医生表现。我们将模型应用于2,578名患者的样本，该患者收到一家大型医院的36名外科医生之一。使用马尔可夫链蒙特卡罗（MCMC）仿真方法估计该模型。在手术前观察到患者健康状况的差异以及所进行的程序的复杂性进行审计后，我们在发病率和死亡率方面构建了监测外科医生性能的质量指标。这些指数用于评估外科医生性能，以防止发病率和死亡率的绝对标准，然后在医院亚特写外科部门内的个别外科医生进行“前往头部”比较。我们的方法突出了新信息技术监测外科医生质量的潜在好处。

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