Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, United States of America.
Center for Information and Systems Engineering, Boston University, Boston, MA, United States of America.
PLoS One. 2020 Sep 9;15(9):e0238118. doi: 10.1371/journal.pone.0238118. eCollection 2020.
New financial incentives, such as reduced Medicare reimbursements, have led hospitals to closely monitor their readmission rates and initiate efforts aimed at reducing them. In this context, many surgical departments participate in the American College of Surgeons National Surgical Quality Improvement Program (NSQIP), which collects detailed demographic, laboratory, clinical, procedure and perioperative occurrence data. The availability of such data enables the development of data science methods which predict readmissions and, as done in this paper, offer specific recommendations aimed at preventing readmissions.
This study leverages NSQIP data for 722,101 surgeries to develop predictive and prescriptive models, predicting readmissions and offering real-time, personalized treatment recommendations for surgical patients during their hospital stay, aimed at reducing the risk of a 30-day readmission. We applied a variety of classification methods to predict 30-day readmissions and developed two prescriptive methods to recommend pre-operative blood transfusions to increase the patient's hematocrit with the objective of preventing readmissions. The effect of these interventions was evaluated using several predictive models.
Predictions of 30-day readmissions based on the entire collection of NSQIP variables achieve an out-of-sample accuracy of 87% (Area Under the Curve-AUC). Predictions based only on pre-operative variables have an accuracy of 74% AUC, out-of-sample. Personalized interventions, in the form of pre-operative blood transfusions identified by the prescriptive methods, reduce readmissions by 12%, on average, for patients considered as candidates for pre-operative transfusion (pre-operative hematoctic <30). The prediction accuracy of the proposed models exceeds results in the literature.
This study is among the first to develop a methodology for making specific, data-driven, personalized treatment recommendations to reduce the 30-day readmission rate. The reported predicted reduction in readmissions can lead to more than $20 million in savings in the U.S. annually.
新的财务激励措施，如减少医疗保险报销，导致医院密切监测其再入院率，并启动旨在降低再入院率的措施。在这种情况下，许多外科部门参与美国外科医师学会国家外科质量改进计划（NSQIP），该计划收集详细的人口统计学、实验室、临床、手术和围手术期发生数据。这些数据的可用性使开发数据科学方法成为可能，这些方法可以预测再入院率，并像本文中所做的那样，提供旨在预防再入院的具体建议。
本研究利用 722,101 例手术的 NSQIP 数据开发预测和规定性模型，预测 30 天内再入院率，并为住院期间的外科患者提供实时个性化治疗建议，以降低 30 天内再入院的风险。我们应用了多种分类方法来预测 30 天内再入院率，并开发了两种规定性方法来建议术前输血，以增加患者的血细胞比容，从而预防再入院。使用几种预测模型评估了这些干预措施的效果。
基于 NSQIP 变量的整个集合进行的 30 天内再入院预测的样本外准确率为 87%（曲线下面积-AUC）。仅基于术前变量的预测的样本外准确率为 74%AUC。规定性方法确定的个性化干预措施，以术前输血的形式，使被认为适合术前输血的患者（术前血细胞比容<30）的再入院率平均降低 12%。所提出模型的预测准确性超过了文献中的结果。
本研究是首批开发特定数据驱动个性化治疗建议以降低 30 天再入院率的方法之一。报告的再入院率预测降低可导致美国每年节省超过 2000 万美元。
