CoChat
Sign Up
Cited by 50Cardiac, Anesthesia and Surgical OutcomesHealthcare Policy and ManagementChronic Disease Management Strategies

Novel machine learning approach to identify preoperative risk factors associated with super-utilization of medicare expenditure following surgery

Open access
Hyer, J. Madison, Ejaz, Aslam, Tsilimigras, Diamantis I., Paredes, Anghela Z., Mehta, Rittal, Pawlik, Timothy M.·2019·JAMA Surgery
DOI 10.1001/jamasurg.2019.2979PMC PMC6694398
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1001/jamasurg.2019.2979

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Other ML models have been developed and validated to predict the risk of super-utilization and plan accordingly, starting in the preoperative setting.

    “Other ML models have also been developed and validated to predict the risk of super-utilization and plan accordingly, starting in the preoperative setting in an increased effort to enhance value-based care (17).”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [17]

  • A growing nationwide effort is seeking to enhance preoperative and perioperative support for high-risk patients and high-cost populations.

    “A growing nationwide effort is seeking to enhance preoperative and perioperative support for high-risk patients and high-cost populations (16, 17).”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [17]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

3 of the 51 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Related

Authors
Hyer, J. M., Ejaz, A., Tsilimigras, D. I., Paredes, A. Z., Mehta, R., & Pawlik, T. M.
Affiliations
The Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and Richard J. Solove Research Institute (US)
Corresponding author
Pawlik, Timothy M.
ORCID
Venue
JAMA Surgery
Publisher
American Medical Association (AMA)
Published
2019-11-01
Volume
154
Issue
11
Pages
1014
ISSN
2168-6254 (ISSN-L)
DOI
10.1001/jamasurg.2019.2979
PMC
PMC6694398
OpenAlex
W2967100253
Cited by
49
References
51
Citations by year
2026: 5 · 2025: 5 · 2024: 6 · 2023: 5 · 2022: 10 · 2021: 7 · 2020: 8 · 2019: 3
FWCI
5.06
Type
journal article
Language
en
Open access
Locations
  • JAMA Surgery (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
  • Health Sciences › Medicine › Epidemiology › Chronic Disease Management Strategies
Keywords
Medicare spending, postoperative care, comorbidities, comorbidity burden, patient clustering, elective surgery, abdominal aortic aneurysm repair, coronary artery bypass grafting, colectomy, total hip arthroplasty, total knee arthroplasty, lung resection, health care costs, risk factors, Charlson Comorbidity Index
MeSH terms
Machine Learning, Aged, Health Expenditures, Female, Medicare, Humans, Male, Patient Acceptance of Health Care, Postoperative Care, Preoperative Care, Retrospective Studies, Risk Factors, United States, Elective Surgical Procedures, Risk Assessment

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review