TY - JOUR
T1 - Duration of Care and Operative Time Are the Primary Drivers of Total Charges After Ambulatory Hip Arthroscopy
T2 - A Machine Learning Analysis
AU - Lu, Yining
AU - Lavoie-Gagne, Ophelie
AU - Forlenza, Enrico M.
AU - Pareek, Ayoosh
AU - Kunze, Kyle N.
AU - Forsythe, Brian
AU - Levy, Bruce A.
AU - Krych, Aaron J.
N1 - Funding Information:
The authors report the following potential conflicts of interest or sources of funding: Support was provided by the Foderaro-Quattrone Musculoskeletal-Orthopaedic Surgery Research Innovation Fund. A.P. receives hospitality payments from Stryker, outside the submitted work. B.F. receives research support from Arthrex, Smith & Nephew, and Stryker; receives publishing royalties and financial or material support from Elsevier; owns stock or stock options in Jace Medical; is a paid consultant for Stryker; receives education support from Medwest Associates; and receives food and beverage support from DePuy Synthes Sales, outside the submitted work. B.A.L. receives intellectual property royalties from Arthrex; is a paid consultant for Arthrex and Smith & Nephew; owns stock or stock options in COVR Medical; and is on the editorial or governing board of Journal of Knee Surgery, Knee Surgery, Sports Traumatology, Arthroscopy, and Orthopedics Today, outside the submitted work A.J.K. receives research support from Aesculap/B. Braun, Arthritis Foundation, Ceterix, Histogenics, Exactech, and Gemini Medical; is on the editorial or governing board of American Journal of Sports Medicine; is a paid consultant for Arthrex, Musculoskeletal Transplant Foundation, Vericel, DePuy, JRF, and Responsive Arthroscopy; receives travel/lodging support from Arthrex and Musculoskeletal Transplant Foundation; is a paid speaker for Arthrex and Musculoskeletal Transplant Foundation; and is a board or committee member of International Cartilage Repair Society, International Society of Arthroscopy, Knee Surgery and Orthopaedic Sports Medicine, Minnesota Orthopaedic Society, and Musculoskeletal Transplant Foundation, outside the submitted work. Full ICMJE author disclosure forms are available for this article online, as supplementary material.
Publisher Copyright:
© 2022
PY - 2022/7
Y1 - 2022/7
N2 - Purpose: To develop a machine learning algorithm to predict total charges after ambulatory hip arthroscopy and create a risk-adjusted payment model based on patient comorbidities. Methods: A retrospective review of the New York State Ambulatory Surgery and Services database was performed to identify patients who underwent elective hip arthroscopy between 2015 and 2016. Features included in initial models consisted of patient characteristics, medical comorbidities, and procedure-specific variables. Models were generated to predict total charges using 5 algorithms. Model performance was assessed by the root-mean-square error, root-mean-square logarithmic error, and coefficient of determination. Global variable importance and partial dependence curves were constructed to show the impact of each input feature on total charges. For performance benchmarking, the best candidate model was compared with a multivariate linear regression using the same input features. Results: A total of 5,121 patients were included. The median cost after hip arthroscopy was $19,720 (interquartile range, $12,399-$26,439). The gradient-boosted ensemble model showed the best performance (root-mean-square error, $3,800 [95% confidence interval, $3,700-$3,900]; logarithmic root-mean-square error, 0.249 [95% confidence interval, 0.24-0.26]; R2 = 0.73). Major cost drivers included total hours in facility less than 12 or more than 15, longer procedure time, performance of a labral repair, age younger than 30 years, Elixhauser Comorbidity Index (ECI) of 1 or greater, African American race, residence in extreme urban and rural areas, and higher household and neighborhood income. Conclusions: The gradient-boosted ensemble model effectively predicted total charges after hip arthroscopy. Few modifiable variables were identified other than anesthesia type; nonmodifiable drivers of total charges included duration of care less than 12 hours or more than 15 hours, operating room time more than 100 minutes, age younger than 30 years, performance of a labral repair, and ECI greater than 0. Stratification of patients based on the ECI highlighted the increased financial risk borne by physicians via flat reimbursement schedules given variable degrees of comorbidities. Level of Evidence: Level III, retrospective cohort study.
AB - Purpose: To develop a machine learning algorithm to predict total charges after ambulatory hip arthroscopy and create a risk-adjusted payment model based on patient comorbidities. Methods: A retrospective review of the New York State Ambulatory Surgery and Services database was performed to identify patients who underwent elective hip arthroscopy between 2015 and 2016. Features included in initial models consisted of patient characteristics, medical comorbidities, and procedure-specific variables. Models were generated to predict total charges using 5 algorithms. Model performance was assessed by the root-mean-square error, root-mean-square logarithmic error, and coefficient of determination. Global variable importance and partial dependence curves were constructed to show the impact of each input feature on total charges. For performance benchmarking, the best candidate model was compared with a multivariate linear regression using the same input features. Results: A total of 5,121 patients were included. The median cost after hip arthroscopy was $19,720 (interquartile range, $12,399-$26,439). The gradient-boosted ensemble model showed the best performance (root-mean-square error, $3,800 [95% confidence interval, $3,700-$3,900]; logarithmic root-mean-square error, 0.249 [95% confidence interval, 0.24-0.26]; R2 = 0.73). Major cost drivers included total hours in facility less than 12 or more than 15, longer procedure time, performance of a labral repair, age younger than 30 years, Elixhauser Comorbidity Index (ECI) of 1 or greater, African American race, residence in extreme urban and rural areas, and higher household and neighborhood income. Conclusions: The gradient-boosted ensemble model effectively predicted total charges after hip arthroscopy. Few modifiable variables were identified other than anesthesia type; nonmodifiable drivers of total charges included duration of care less than 12 hours or more than 15 hours, operating room time more than 100 minutes, age younger than 30 years, performance of a labral repair, and ECI greater than 0. Stratification of patients based on the ECI highlighted the increased financial risk borne by physicians via flat reimbursement schedules given variable degrees of comorbidities. Level of Evidence: Level III, retrospective cohort study.
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U2 - 10.1016/j.arthro.2021.12.012
DO - 10.1016/j.arthro.2021.12.012
M3 - Article
C2 - 34921955
AN - SCOPUS:85131247985
SN - 0749-8063
VL - 38
SP - 2204-2216.e3
JO - Arthroscopy - Journal of Arthroscopic and Related Surgery
JF - Arthroscopy - Journal of Arthroscopic and Related Surgery
IS - 7
ER -