Assessment of utilization efficiency using machine learning techniques: A study of heterogeneity in preoperative healthcare utilization among super-utilizers

被引：13
作者：
Hyer, J. Madison [1 ,2 ]
Paredes, Anghela Z. [1 ,2 ]
White, Susan [2 ,3 ]
Ejaz, Aslam [1 ,2 ]
Pawlik, Timothy M. [1 ,2 ]
机构：
[1] Ohio State Univ, Dept Surg, Wexner Med Ctr, Div Surg Oncol, Columbus, OH 43210 USA
[2] James Canc Hosp & Solove Res Inst, Columbus, OH USA
[3] Ohio State Univ, Dept Financial Serv, Wexner Med Ctr, Columbus, OH 43210 USA
来源：
AMERICAN JOURNAL OF SURGERY | 2020年 / 220卷 / 03期
关键词：
Morbidity; Complexity; Risk factors; Machine learning; RISK ADJUSTMENT; CLUSTER; SURGERY;
D O I：
10.1016/j.amjsurg.2020.01.043
中图分类号：
R61 [外科手术学];
学科分类号：
摘要：
Introduction: In the United States, 5% of patients represent up to 55% of all health care costs. This study sought to define healthcare utilization patterns among super-utilizers, as well as assess possible variation in patient outcomes. Methods: Medicare super-utilizers undergoing either a total hip or knee arthroplasty were identified and entered into a cluster analysis using annual preoperative charges to identify distinct patterns of utilization. Results: Among 19,522 super-utilizers who underwent THA or TKA, there was a marked heterogeneity in overall utilization with 5 distinct clusters of utilization patterns. Of note, comorbidity burden was similar among the 5 clusters. Patient outcomes also varied by Cluster type, ranging from 6.9% to 16.5% experi-encing complications and 1.0%-3.2% experiencing 90-day mortality. Conclusion: While previous studies have suggested that super-utilizers are a homogenous group of pa-tients, the current study demonstrated a large degree of heterogeneity within super-utilizers. Variations in utilization patterns were associated with postoperative outcomes and subsequent health care costs. (c) 2020 Elsevier Inc. All rights reserved.
引用
收藏
页码：714 / 720
页数：7
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