Reformulating Provider Profiling to Improve Patient Outcomes: Grouping Providers Treating Similar Populations of Patients Prior to Evaluating Performance

项目概要/摘要： 当前的提供程序分析方法有很大的局限性，值得立即关注 由于档案报告对卫生保健系统的广泛影响。长期目标是 通过解决一些最紧迫的问题， 局限性。由于性能估计仅在患者中存在足够重叠时才有效， 特征，当前方法的一个重大局限性是未能评估 正在分析的提供者之间的患者协变量重叠。此应用程序的总体目标是 实施一种方法，该方法根据接纳情况识别类似的提供者组， 他们治疗的患者的特点。推测是，提供者将需要被分配到 多个组以实现患者特征的充分重叠，并且在对提供者进行分组时， 关于供应商业绩的结论将不同于根据现行办法得出的结论。 解决这一重大限制提供了一个新的框架，其他研究人员可以建立 继续完善现行办法。总体目标将通过以下三个方面来实现： 具体目标：1）开发一种基于患者入院特征对提供者进行分组的方法; 2） 进行平衡检查，告知组内患者协变量所需的组数 平衡;以及3）将方法应用于养老院数据，以比较每个养老院内的家庭绩效 组对于第一个目标，将使用贝叶斯分层混合模型来估计每个供应商的 根据患者入院情况属于不同组的后验概率 特色这些后验概率将用于将每个提供者分配到一个组。为 第二个目标是，评估患者入院协变量组内平衡的平衡检查将 旨在确保每组内患者协变量的分布类似于 预期在随机化环境中。这反映了在比较之前的因果推理中所做的事情 多次治疗。第三个目标适用于发达国家的方法，通过分组养老院跨 美国使用患者特征，如年龄和性别。在使用余额检查通知 家庭应分配到的组数，家庭再入院率将在每个组内进行比较。 组在申请人看来，拟议的论文研究是创新的，因为它开发了一个 因果推理和供应商分析之间有希望的联系，同时应用新的贝叶斯统计 对供应商进行分析的方法。拟议的项目意义重大，因为它重新制定了 目前的统计方法，因此有可能提高分析报告的准确性。 这一点至关重要，因为准确的性能评估有助于患者做出明智的决定， 激励供应商提高质量，并指导政策制定者制定政策。 .

Project Summary/Abstract: The current approach for provider profiling has significant limitations that deserve immediate attention due to the extensive repercussions profile reports have on the health care system. The long-term goal is to improve the current methodology for profiling providers by addressing some of its most pressing limitations. Since performance estimates are only valid when there exists sufficient overlap in patient characteristics, a substantial limitation with the current approach is its failure to assess the extent of patient covariate overlap among providers being profiled. The overall objective for this application is to implement a methodology that identifies similar groups of providers based on the admission characteristics of the patients they treat. The conjecture is that providers will need to be assigned to multiple groups to achieve sufficient overlap in patient characteristics and that, upon grouping providers, conclusions regarding provider performance will differ from conclusions under the current approach. Addressing this significant limitation provides a novel framework from which other researchers can build from to continue refining the current approach. The overall objective will be attained by pursuing three specific aims: 1) develop a method for grouping providers based on patient admission characteristics; 2) perform balance checks informing the number of groups needed for within-group patient covariate balance; and 3) apply methods to nursing home data to compare performance of homes within each group. For the first aim, a Bayesian hierarchical mixture model will be used to estimate each provider’s posterior probability of belonging to the different groups based on their patient’s admission characteristics. These posterior probabilities will be used to assign each provider to a group. For the second aim, balance checks assessing within-group balance in patient admission covariates will be designed to ensure that the distribution of patient covariates within each group resembles what would be expected in a randomized setting. This mirrors what is done in causal inference prior to comparing multiple treatments. The third aim applies the developed methodology by grouping nursing homes across the US using patient characteristics like age and gender. Upon using balance checks to inform the number of groups homes should be assigned to, home readmission rates will be compared within each group. The proposed dissertation research is innovative, in the applicant’s opinion, because it develops a promising link between causal inference and provider profiling while applying novel Bayesian statistical methodology to the profiling of providers. The proposed project is significant because it reformulates the current statistical methodology and thus has the potential to improve the accuracy of profiling reports. This is of utmost importance, as accurate performance estimates help patients make informed decisions, motivate providers to improve quality, and guide policymakers as they develop policy. .