Health at Scale Corporation, San Jose, CA, United States.
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States.
J Med Internet Res. 2020 Dec 1;22(12):e22765. doi: 10.2196/22765.
Patients' choices of providers when undergoing elective surgeries significantly impact both perioperative outcomes and costs. There exist a variety of approaches that are available to patients for evaluating between different hospital choices.
This paper aims to compare differences in outcomes and costs between hospitals ranked using popular internet-based consumer ratings, quality stars, reputation rankings, average volumes, average outcomes, and precision machine learning-based rankings for hospital settings performing hip replacements in a large metropolitan area.
Retrospective data from 4192 hip replacement surgeries among Medicare beneficiaries in 2018 in a the Chicago metropolitan area were analyzed for variations in outcomes (90-day postprocedure hospitalizations and emergency department visits) and costs (90-day total cost of care) between hospitals ranked through multiple approaches: internet-based consumer ratings, quality stars, reputation rankings, average yearly surgical volume, average outcome rates, and machine learning-based rankings. The average rates of outcomes and costs were compared between the patients who underwent surgery at a hospital using each ranking approach in unadjusted and propensity-based adjusted comparisons.
Only a minority of patients (1159/4192, 27.6% to 2078/4192, 49.6%) were found to be matched to higher-ranked hospitals for each of the different approaches. Of the approaches considered, hip replacements at hospitals that were more highly ranked by consumer ratings, quality stars, and machine learning were all consistently associated with improvements in outcomes and costs in both adjusted and unadjusted analyses. The improvement was greatest across all metrics and analyses for machine learning-based rankings.
There may be a substantive opportunity to increase the number of patients matched to appropriate hospitals across a broad variety of ranking approaches. Elective hip replacement surgeries performed at hospitals where patients were matched based on patient-specific machine learning were associated with better outcomes and lower total costs of care.
患者在接受择期手术时选择医生的行为会对围手术期结果和成本产生重大影响。患者有多种方法可以评估不同医院之间的选择。
本研究旨在比较在一个大城市地区进行髋关节置换术的医院中，使用流行的基于互联网的消费者评分、质量星级、声誉排名、平均手术量、平均结果和基于精确机器学习的排名对医院进行排名，比较结果和成本的差异。
对 2018 年在芝加哥大都市区接受医疗保险福利的 4192 例髋关节置换术患者的回顾性数据进行分析，比较通过多种方法（基于互联网的消费者评分、质量星级、声誉排名、平均年手术量、平均结果率和基于机器学习的排名）对医院进行排名时，在结果（90 天术后住院和急诊就诊）和成本（90 天总护理成本）方面的差异。在未调整和基于倾向评分的调整比较中，比较了使用每种排名方法进行手术的患者之间的结果和成本的平均比率。
只有少数患者（1159/4192，27.6%至 2078/4192，49.6%）与每个不同方法的排名较高的医院相匹配。在所考虑的方法中，消费者评分、质量星级和机器学习排名较高的医院进行髋关节置换术，在调整和未调整分析中，结果和成本均得到改善。基于机器学习的排名在所有指标和分析中都有最大的改善。
通过多种排名方法，可能有机会增加与广泛的各种排名方法相匹配的合适医院的患者数量。根据患者特定的机器学习对患者进行匹配的医院进行的择期髋关节置换术与更好的结果和更低的总护理成本相关。
