Lunn Yazmine, Patel Rudra, Sokphat Timothy S, Bourn Laura, Fields Khalil, Fitzgerald Anna, Sundaresan Vandana, Thomas Greeshma, Korvink Michael, Gunn Laura H
School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Healthcare (Basel). 2022 Jan 28;10(2):248. doi: 10.3390/healthcare10020248.
Resource utilization measures are typically modeled by relying on clinical characteristics. However, in some settings, those clinical markers are not available, and hospitals are unable to explore potential inefficiencies or resource misutilization. We propose a novel approach to exploring misutilization that solely relies on administrative data in the form of patient characteristics and competing resource utilization, with the latter being a novel addition. We demonstrate this approach in a 2019 patient cohort diagnosed with prostate cancer ( = 51,111) across 1056 U.S. healthcare facilities using Premier, Inc.'s (Charlotte, NC, USA) all payor databases. A multivariate logistic regression model was fitted using administrative information and competing resources utilization. A decision curve analysis informed by industry average standards of utilization allows for a definition of misutilization with regards to these industry standards. Odds ratios were extracted at the patient level to demonstrate differences in misutilization by patient characteristics, such as race; Black individuals experienced higher under-utilization compared to White individuals ( < 0.0001). Volume-adjusted Poisson rate regression models allow for the identification and ranking of facilities with large departures in utilization. The proposed approach is scalable and easily generalizable to other diseases and resources and can be complemented with clinical information from electronic health record information, when available.
资源利用措施通常依靠临床特征进行建模。然而，在某些情况下，这些临床指标不可用，医院无法探究潜在的效率低下或资源利用不当问题。我们提出了一种探索利用不当的新方法，该方法仅依赖患者特征形式的管理数据以及竞争性资源利用情况，后者是一个新增加的内容。我们在美国1056家医疗机构中，使用Premier公司（美国北卡罗来纳州夏洛特市）的所有付款人数据库，对2019年被诊断患有前列腺癌的患者队列（n = 51,111）进行了这种方法的演示。使用管理信息和竞争性资源利用情况拟合了多变量逻辑回归模型。根据行业平均利用标准进行的决策曲线分析允许根据这些行业标准定义利用不当情况。在患者层面提取优势比，以证明不同患者特征（如种族）在利用不当方面的差异；与白人个体相比，黑人个体的利用不足情况更严重（p < 0.0001）。体积调整后的泊松率回归模型允许识别和排名在利用方面有较大偏差的医疗机构。所提出的方法具有可扩展性，并且可以很容易地推广到其他疾病和资源，并且在可用时可以与电子健康记录信息中的临床信息相结合。
