Prediction of total healthcare cost following total shoulder arthroplasty utilizing machine learning

Gowd, Anirudh K.; Agarwalla, Avinesh; Beck, Edward C.; Rosas, Samuel; Waterman, Brian R.; Romeo, Anthony A.; Liu, Joseph N.

doi:10.1016/j.jse.2022.07.013

Cited by 45Shoulder Injury and TreatmentHealthcare Policy and ManagementTotal Knee Arthroplasty Outcomes

Prediction of total healthcare cost following total shoulder arthroplasty utilizing machine learning

Gowd, Anirudh K., Agarwalla, Avinesh, Beck, Edward C., Rosas, Samuel, Waterman, Brian R., Romeo, Anthony A., Liu, Joseph N.·2022·Journal of Shoulder and Elbow Surgery

DOI 10.1016/j.jse.2022.07.013

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Abstract

Background Given the increase in demand in treatment of glenohumeral arthritis with anatomic total (aTSA) and reverse shoulder arthroplasty (RTSA), it is imperative to improve quality of patient care while controlling costs as private and federal insurers continue its gradual transition toward bundled payment models. Big data analytics with machine learning shows promise in predicting health care costs. This is significant as cost prediction may help control cost by enabling health care systems to appropriately allocate resources that help mitigate the cause of increased cost.Methods The Nationwide Readmissions Database (NRD) was accessed in 2018. The database was queried for all primary aTSA and RTSA by International Classification of Diseases, Tenth Revision (ICD-10) procedure codes: 0RRJ0JZ and 0RRK0JZ for aTSA and 0RRK00Z and 0RRJ00Z for RTSA. Procedures were categorized by diagnoses: osteoarthritis (OA), rheumatoid arthritis (RA), avascular necrosis (AVN), fracture, and rotator cuff arthropathy (RCA). Costs were calculated by utilizing the total hospital charge and each hospital's cost-to-charge ratio. Hospital characteristics were included, such as volume of procedures performed by the respective hospital for the calendar year and wage index, which represents the relative average hospital wage for the respective geographic area. Unplanned readmissions within 90 days were calculated using unique patient identifiers, and cost of readmissions was added to the total admission cost to represent the short-term perioperative health care cost. Machine learning algorithms were used to predict patients with immediate postoperative admission costs greater than 1 standard deviation from the mean, and readmissions.Results A total of 49,354 patients were isolated for analysis, with an average patient age of 69.9 ± 9.6 years. The average perioperative cost of care was $18,843 ± $10,165. In total, there were 4279 all-cause readmissions, resulting in an average cost of $13,871.00 ± $14,301.06 per readmission. Wage index, hospital volume, patient age, readmissions, and diagnosis-related group severity were the factors most correlated with the total cost of care. The logistic regression and random forest algorithms were equivalent in predicting the total cost of care (area under the receiver operating characteristic curve = 0.83).Conclusion After shoulder arthroplasty, there is significant variability in cumulative hospital costs, and this is largely affected by readmissions. Hospital characteristics, such as geographic area and volume, are key determinants of overall health care cost. When accounting for this, machine learning algorithms may predict cases with high likelihood of increased resource utilization and/or readmission.

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Deep learning programs that use data from medical records can identify cost of care variations.
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Authors: Gowd, A. K., Agarwalla, A., Beck, E. C., Rosas, S., Waterman, B. R., Romeo, A. A., & Liu, J. N.
Affiliations: Atrium Health Wake Forest Baptist (US)
Westchester Medical Center (US)
University of Southern California (US)
Corresponding author: Liu, Joseph N.
ORCID: Gowd, Anirudh K.
Agarwalla, Avinesh
Beck, Edward C.
Rosas, Samuel
Waterman, Brian R.
Romeo, Anthony A.
Liu, Joseph N.
Venue: Journal of Shoulder and Elbow Surgery
Publisher: Elsevier BV
Published: 2022-12
Volume: 31
Issue: 12
Pages: 2449-2456
ISSN: 1058-2746 (ISSN-L)
DOI: 10.1016/j.jse.2022.07.013
OpenAlex: W4292622580
Cited by: 45
References: 37
Citations by year: 2026: 10 · 2025: 11 · 2024: 17 · 2023: 6
FWCI: 8.12
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Journal of Shoulder and Elbow Surgery (publishedVersion)
PubMed (publishedVersion)
Funders: American Shoulder and Elbow Surgeons
Topics: Health Sciences › Medicine › Surgery › Shoulder Injury and Treatment
Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
Health Sciences › Medicine › Surgery › Total Knee Arthroplasty Outcomes
Keywords: total shoulder arthroplasty, reverse shoulder arthroplasty, healthcare cost prediction, hospital readmission, glenohumeral arthritis, bundled payment, hospital volume, wage index, random forest, logistic regression, osteoarthritis, rheumatoid arthritis, avascular necrosis, rotator cuff arthropathy
MeSH terms: Machine Learning, Arthroplasty, Replacement, Shoulder, Aged, Humans, Middle Aged, Osteoarthritis, Retrospective Studies, Shoulder Joint, Treatment Outcome, Rotator Cuff, Health Care Costs

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Prediction of total healthcare cost following total shoulder arthroplasty utilizing machine learning

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