ITS Data Science, Premier Inc, 13034 Ballantyne Corporate Pl, Charlotte, NC 28277. Email:
Am J Manag Care. 2022 Jul 1;28(7):e263-e270. doi: 10.37765/ajmc.2022.89185.
More robust attribution methods are necessary to understand physician-level variation in quality of care across risk-adjusted inpatient measures. We address a gap in the literature involving attribution of physicians to inpatient stays using administrative claims data, in which rule-based methods often inadequately attribute physicians.
Methodology comparison study using a cross-section of inpatient stays.
A novel approach is proposed in which physicians' relative degrees of responsibility for inpatient stays are expressed through physician-specific attribution ratios informed by existing patient characteristics and comorbidities. Attribution results are compared with the rule-based benchmark method for 7 CMS-defined clinical cohorts, including a COVID-19 cohort.
Using 6,835,460 unique patient encounters during 2020 (n = 136,339 in out-of-sample cohort), the proposed approach favored specialists generally considered responsible for primary clinical conditions when compared with the benchmark. The most salient shift within the acute myocardial infarction (+17.0%), heart failure (+20.2%), and coronary artery bypass graft (+4.0%) cohorts was toward the cardiovascular diseases specialty, and the chronic obstructive pulmonary disease (+24.0%) and pneumonia (+16.2%) cohorts resulted in a shift toward the pulmonary diseases specialty. The COVID-19 cohort resulted in considerable shifts toward infectious diseases and pulmonary diseases specialties (+17.4% and +14.1%, respectively). The stroke cohort experienced a considerable shift toward the neurology specialty (+42.2%).
We provide a robust method to attribute physicians to patients, which is a necessary tool to understand physician-level variation in quality of care within the inpatient acute care setting. The proposed method provides consistency across facilities and eliminates unattributed patients resulting from unsatisfied business rules.
需要更强大的归因方法来理解经过风险调整的住院患者衡量标准下,医生在医疗质量方面的差异。我们解决了文献中的一个空白,涉及使用行政索赔数据将医生归因于住院患者,在这种情况下,基于规则的方法通常无法充分归因于医生。
使用住院患者的横截面进行方法比较研究。
提出了一种新方法,通过利用现有患者特征和合并症的医生特定归因比来表达医生对住院患者的相对责任程度。将归因结果与基于规则的基准方法进行比较,用于 7 个 CMS 定义的临床队列,包括 COVID-19 队列。
在 2020 年期间,使用 6835460 个独特的患者就诊记录(136339 个在样本外队列中),与基准相比,所提出的方法普遍有利于被认为对主要临床状况负责的专科医生。急性心肌梗死(+17.0%)、心力衰竭(+20.2%)和冠状动脉旁路移植术(+4.0%)队列中最显著的变化是向心血管疾病专业转移,而慢性阻塞性肺疾病(+24.0%)和肺炎(+16.2%)队列导致向肺部疾病专业转移。COVID-19 队列导致向传染病和肺部疾病专业的大量转移(分别为+17.4%和+14.1%)。中风队列向神经病学专业转移(+42.2%)。
我们提供了一种将医生归因于患者的强大方法,这是理解住院急性护理环境中医生医疗质量差异的必要工具。该方法在医疗机构之间提供了一致性,并消除了因不满意的业务规则而导致的未归因患者。