Recent Medicare Billing Trends of Hospitals and Surgeons for Revision Hip and Knee Total Joint Arthroplasty: Is the Value of a Surgeon's Work Decreasing?

BACKGROUND: Revision hip and knee total joint arthroplasty (TJA) is associated with higher health care costs and work burden than primary TJAs. However, previous studies demonstrated a decrease in the value of reimbursements for revision TJA, causing concerns for hospitals and surgeons regarding the financial sustainability of these resource-expensive procedures. This study aimed to investigate the Medicare billing trends of hospitals and surgeons for revision TJA between 2017 and 2022. METHODS: Medicare claims and payments for revision TJA were identified from the Centers for Medicare and Medicaid Services Part A and B databases. Hospital claims for revision TJA were identified through Diagnostic-Related Groups (467, 468). Surgeon claims were identified using Current Procedural Terminology codes for revision hip (27134, 27137, 27138) and knee (27486, 27487) TJA. Yearly charges, reimbursements, and markup ratios (MR = charge/reimbursement) were analyzed. All monetary values were adjusted to the 2022 U.S. dollars. RESULTS: A total of 43,125 surgeons and 152,974 hospital claims were included in this study. From 2017 to 2022, the total volume of revision TJA decreased by 19.4%. Hospital reimbursements remained relatively unchanged, with a decrease of 1.4%, while hospital charges increased by 11.8%, resulting in a 13.3% increase in the MR. For surgeons, reimbursements decreased by 13.8%, and charges decreased by 11.0%, leading to a 3.3% increase in the MR. The proportion of surgeon reimbursement to hospital reimbursement decreased from 8.5 to 7.5%. CONCLUSIONS: The comparison of the billing trends of hospitals and surgeons showed the relatively stable value of hospital reimbursement while the value of surgeon reimbursement continued to decline, implying the decreasing fiscal value of physicians' work. The study suggests the need for sustainable financial incentives for surgeons performing revision TJA and strategies to control hospital charges to alleviate financial burdens and improve patient access to revision TJA.

Machine learning model outperforms the ACS Risk Calculator in predicting non-home discharge following primary total knee arthroplasty.

PURPOSE: Despite the increase in outpatient total knee arthroplasty (TKA) procedures, many patients are still discharged to non-home locations following index surgery. The ability to accurately predict non-home discharge (NHD) following TKAs has the potential to promote a reduction in associated adverse events and excess healthcare costs. This study aimed to evaluate whether a machine learning (ML) model could outperform the American College of Surgeons (ACS) Risk Calculator in predicting NHD following TKA, using the same set of clinical variables. We hypothesised that the ML model would outperform the ACS Risk Calculator. METHODS: Data from 365,240 patients who underwent a primary TKA between 2013 and 2020 were extracted from the ACS-National Surgical Quality Improvement Program database and used to develop an artificial neural network (ANN) to predict discharge disposition following primary TKA. The ANN and ACS calculator were assessed and compared using discrimination, calibration and decision curve analysis. RESULTS: Age (>68 years), BMI (>35.5 kg/m2) and ASA Class (≥2) were found to be the most important variables in predicting NHD following TKA. When compared to the ACS calculator, the ANN model demonstrated a significantly superior ability to distinguish the area under the receiver operating characteristic curve (AUC) among NHD patients and provided probability predictions well aligned with the true outcomes (AUCANN = 0.69, AUCACS = 0.50, p = 0.002, slopeANN = 0.85, slopeACS = 4.46, interceptANN = 0.04, and interceptACS = 0.06). CONCLUSION: Our findings support the hypothesis that machine learning models outperform the ACS Risk Calculator in predicting non-home discharge after TKA, even when constrained to the same clinical variables. Our findings underscore the potential benefits of integrating machine learning models into clinical practice for improving preoperative patient risk identification, optimisation, counselling and clinical decision-making. LEVEL OF EVIDENCE: III.

Medical Malpractice Litigation Trends Following Primary Total Hip and Knee Joint Arthroplasty: An Updated Nationwide Analysis.

BACKGROUND: Total joint arthroplasty (TJA) is the most common procedure associated with malpractice claims within orthopaedic surgery. Although prior research has assessed prevalent causes and outcomes of TJA-related lawsuits before 2018, the dynamic healthcare environment demands regular re-evaluations. This study aimed to provide an updated analysis of the predominant causes and outcomes of TJA-related malpractice lawsuits and analyze the outcomes of subsequent appeals following initial jury verdicts. METHODS: A legal database was queried for cases between 2018 and 2022 involving primary hip and knee TJA in the United States. Cases were listed as original rulings or appeals and reviewed for the alleged negligence, damages incurred, demographics, and verdicts. Appeals were further assessed for appellant details, preliminary judgment, and outcomes. The findings were compared to previous litigation data using descriptive statistics. RESULTS: The final cohort comprised 59 cases: 33 (56%) total knee arthroplasty (TKA) and 26 (44%) total hip arthroplasty (THA). The TKA cases primarily cited pain (24%), while the THA cases cited nerve injuries (31%). Negligence largely stemmed from procedural error (47%), postsurgical error (27%), and failure to inform (14%). Case outcomes were in favor of the defense in 66% of cases. Overall, 90% of primary verdicts led to appeals, with 71% by the plaintiff. Initial rulings were upheld in 87% of plaintiff appeals, whereas 53% of defendant appeals retained the initial judgment. CONCLUSIONS: The primary cause of litigation shifted from infection to ongoing/worsening pain in TKA cases over time. While nerve injury TKA cases have decreased, it remains the most cited damage after THA. Defense verdicts are common, but there is an increasing number of verdicts against defendants. Plaintiffs are more likely to appeal, but are less successful in appellate courts. These findings allow surgeons and policymakers to address emerging litigation trends in TJA to mitigate risks and improve the overall quality of TJA.

Validation and Generalizability of Machine Learning Models for the Prediction of Discharge Disposition Following Revision Total Knee Arthroplasty.

BACKGROUND: Postoperative discharge to facilities account for over 33% of the $ 2.7 billion revision total knee arthroplasty (TKA)-associated annual expenditures and are associated with increased complications when compared to home discharges. Prior studies predicting discharge disposition using advanced machine learning (ML) have been limited due to a lack of generalizability and validation. This study aimed to establish ML model generalizability by externally validating its prediction for nonhome discharge following revision TKA using national and institutional databases. METHODS: The national and institutional cohorts comprised 52,533 and 1,628 patients, respectively, with 20.6 and 19.4% nonhome discharge rates. Five ML models were trained and internally validated (five-fold cross-validation) on a large national dataset. Subsequently, external validation was performed on our institutional dataset. Model performance was assessed using discrimination, calibration, and clinical utility. Global predictor importance plots and local surrogate models were used for interpretation. RESULTS: The strongest predictors of nonhome discharge were patient age, body mass index, and surgical indication. The area under the receiver operating characteristic curve increased from internal to external validation and ranged between 0.77 and 0.79. Artificial neural network was the best predictive model for identifying patients at risk for nonhome discharge (area under the receiver operating characteristic curve = 0.78), and also the most accurate (calibration slope = 0.93, intercept = 0.02, and Brier score = 0.12). CONCLUSION: All five ML models demonstrated good-to-excellent discrimination, calibration, and clinical utility on external validation, with artificial neural network being the best model for predicting discharge disposition following revision TKA. Our findings establish the generalizability of ML models developed using data from a national database. The integration of these predictive models into clinical workflow may assist in optimizing discharge planning, bed management, and cost containment associated with revision TKA.