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Original Investigation
August 14, 2019

Novel Machine Learning Approach to Identify Preoperative Risk Factors Associated With Super-Utilization of Medicare Expenditure Following Surgery

Author Affiliations
  • 1Division of Surgical Oncology, Department of Surgery, Solove Research Institute, The Ohio State University, Wexner Medical Center, James Cancer Hospital, Columbus
  • 2Deputy Editor, JAMA Surgery
JAMA Surg. Published online August 14, 2019. doi:10.1001/jamasurg.2019.2979
Key Points

Question  What preoperative risk factors are associated with super-utilization of health care resources after surgery?

Findings  In this cohort study of 1 049 160 patients, super-utilizers of health care comprised 4.8% of the overall cohort yet incurred 31.7% of the expenditures. A machine learning approach identified history of hemiplegia, paraplegia, weight loss, and congestive heart failure with chronic kidney disease stages I to IV as the most significant risk factors associated with super-utilization following surgery.

Meaning  By proactively identifying patients who may be at risk for super-utilization of health care following surgery, targeted efforts may decrease the cost burden on the health care system while improving quality of care and outcomes for those patients.


Importance  Typically defined as the top 5% of health care users, super-utilizers are responsible for an estimated 40% to 55% of all health care costs. Little is known about which factors may be associated with increased risk of long-term postoperative super-utilization.

Objective  To identify clusters of patients with distinct constellations of clinical and comorbid patterns who may be associated with an elevated risk of super-utilization in the year following elective surgery.

Design, Setting, and Participants  A retrospective longitudinal cohort study of 1 049 160 patients who underwent abdominal aortic aneurysm repair, coronary artery bypass graft, colectomy, total hip arthroplasty, total knee arthroplasty, or lung resection were identified from the 100% Medicare inpatient and outpatient Standard Analytic Files at all inpatient facilities performing 1 or more of the evaluated surgical procedures from 2013 to 2015. Data from 2012 to 2016 were used to evaluate expenditures in the year preceding and following surgery. Using a machine learning approach known as Logic Forest, comorbidities and interactions of comorbidities that put patients at an increased chance of becoming a super-utilizer were identified. All comorbidities, as defined by the Charlson (range, 0-24) and Elixhauser (range, 0-29) comorbidity indices, were used in the analysis. Higher scores indicated higher comorbidity burden. Data analysis was completed on November 16, 2018.

Main Outcome and Measures  Super-utilization of health care in the year following surgery.

Results  In total, 1 049 160 patients met inclusion criteria and were included in the analytic cohort. Their median (interquartile range) age was 73 (69-78) years, and approximately 40% were male. Super-utilizers comprised 4.8% of the overall cohort (n = 79 746) yet incurred 31.7% of the expenditures. Although the difference in overall expenditures per person between super-utilizers ($4049) and low users ($2148) was relatively modest prior to surgery, the difference in expenditures between super-utilizers ($79 698) vs low users ($2977) was marked in the year following surgery. Risk factors associated with super-utilization of health care included hemiplegia/paraplegia (odds ratio, 5.2; 95% CI, 4.4-6.2), weight loss (odds ratio, 3.5; 95% CI, 2.9-4.2), and congestive heart failure with chronic kidney disease stages I to IV (odds ratio, 3.4; 95% CI, 3.0-3.9).

Conclusions and Relevance  Super-utilizers comprised only a small fraction of the surgical population yet were responsible for a disproportionate amount of Medicare expenditure. Certain subpopulations were associated with super-utilization of health care following surgical intervention despite having lower overall use in the preoperative period.

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