Trends and outcomes of outpatient total shoulder arthroplasty after its removal from CMS's inpatient-only list.

BACKGROUND: In January 2021, the US Medicare program approved reimbursement of outpatient total shoulder arthroplasties (TSA), including anatomic and reverse TSAs. It remains unclear whether shifting TSAs from the inpatient to outpatient setting has affected clinical outcomes. Herein, we describe the rate of outpatient TSA growth and compare inpatient and outpatient TSA complications, readmissions, and mortality. METHODS: Medicare fee-for-service claims for 2019-2022Q1 were analyzed to identify the trends in outpatient TSAs and to compare 90-day postoperative complications, all-cause hospital readmissions, and mortality between outpatients and inpatients. Outpatient cases were defined as those discharged on the same day of the surgery. To reduce the COVID-19 pandemic's impact and selection bias, we excluded 2020Q2-Q4 data and used propensity scores to match 2021-2022Q1 outpatients with inpatients from the same period (the primary analysis) and from 2019-2020Q1 (the secondary analysis), respectively. We performed both propensity score-matched and -weighted multivariate analyses to compare outcomes between the two groups. Covariates included sociodemographics, preoperative diagnosis, comorbid conditions, the Hierarchical Condition Category risk score, prior year hospital/skilled nursing home admissions, annual surgeon volume, and hospital characteristics. RESULTS: Nationally, the proportion of outpatient TSAs increased from 3% (619) in 2019Q1 to 22% (3456) in 2021Q1 and 38% (6778) in 2022Q1. A total of 55,166 cases were identified for the primary analysis (14,540 outpatients and 40,576 inpatients). Overall, glenohumeral osteoarthritis was the most common indication for surgery (70.8%), followed by rotator cuff pathology (14.6%). The unadjusted rates of complications (1.3 vs 2.4%, P < .001), readmissions (3.7 vs 6.1%, P < .001), and mortality (0.2 vs 0.4%, P = .024) were significantly lower among outpatient TSAs than inpatient TSAs. Using 1:1 nearest matching, 12,703 patient pairs were identified. Propensity score-matched multivariate analyses showed similar rates of postoperative complications, hospital readmissions, and mortality between outpatients and inpatients. Propensity score-weighted multivariate analyses resulted in similar conclusions. The secondary analysis showed a lower hospital readmission rate in outpatients (odds ratio: 0.8, P < .001). CONCLUSIONS: There has been accelerated growth in outpatient TSAs since 2019. Outpatient and inpatient TSAs have similar rates of postoperative complication, hospital readmission, and mortality.

Variation in the Cost of Hip Arthroscopy for Labral Pathological Conditions: A Time-Driven Activity-Based Costing Analysis.

BACKGROUND: Despite growing interest in delivering high-value orthopaedic care, the costs associated with hip arthroscopy remain poorly understood. By employing time-driven activity-based costing (TDABC), we aimed to characterize the cost composition of hip arthroscopy for labral pathological conditions and to identify factors that drive variation in cost. METHODS: Using TDABC, we measured the costs of 890 outpatient hip arthroscopy procedures for labral pathological conditions across 5 surgeons at 4 surgery centers from 2015 to 2022. All patients were ≥18 years old and were treated by surgeons who each performed ≥20 surgeries during the study period. Costs were normalized to protect the confidentiality of internal hospital cost data. Descriptive analyses and multivariable linear regression were performed to identify factors underlying cost variation. RESULTS: The study sample consisted of 515 women (57.9%) and 375 men (42.1%), with a mean age (and standard deviation) of 37.1 ± 12.7 years. Most of the procedures were performed in patients who were White (90.6%) or not Hispanic (93.4%). The normalized total cost of hip arthroscopy per procedure ranged from 43.4 to 203.7 (mean, 100 ± 24.2). Of the 3 phases of the care cycle, the intraoperative phase was identified as the largest generator of cost (>90%). On average, supply costs accounted for 48.8% of total costs, whereas labor costs accounted for 51.2%. A 2.5-fold variation between the 10th and 90th percentiles for total cost was attributed to supplies, which was greater than the 1.8-fold variation attributed to labor. Variation in total costs was most effectively explained by the labral management method (partial R2 = 0.332), operating surgeon (partial R2 = 0.326), osteoplasty type (partial R2 = 0.087), and surgery center (partial R2 = 0.086). Male gender (p < 0.001) and younger age (p = 0.032) were also associated with significantly increased costs. Finally, data trends revealed a shift toward labral preservation techniques over debridement during the study period (with the rate of such techniques increasing from 77.8% to 93.2%; Ptrend = 0.0039) and a strong correlation between later operative year and increased supply costs, labor costs, and operative time (p < 0.001 for each). CONCLUSIONS: By applying TDABC to outpatient hip arthroscopy, we identified wide patient-to-patient cost variation that was most effectively explained by the method of labral management, the operating surgeon, the osteoplasty type, and the surgery center. Given current procedural coding trends, declining reimbursements, and rising health-care costs, these insights may enable stakeholders to design bundled payment structures that better align reimbursements with costs. LEVEL OF EVIDENCE: Economic and Decision Analysis Level IV. See Instructions for Authors for a complete description of levels of evidence.

Trends in the Adoption of Outpatient Joint Arthroplasties and Patient Risk: A Retrospective Analysis of 2019 to 2021 Medicare Claims Data.

INTRODUCTION: Total joint arthroplasties (TJAs) have recently been shifting toward outpatient arthroplasty. This study aims to explore recent trends in outpatient total joint arthroplasty (TJA) procedures and examine whether patients with a higher comorbidity burden are undergoing outpatient arthroplasty. METHODS: Medicare fee-for-service claims were screened for patients who underwent total hip, knee, or shoulder arthroplasty procedures between January 2019 and December 2022. The procedure was considered to be outpatient if the patient was discharged on the same date of the procedure. The Hierarchical Condition Category Score (HCC) and the Charlson Comorbidity Index (CCI) scores were used to assess patient comorbidity burden. Patient adverse outcomes included all-cause hospital readmission, mortality, and postoperative complications. Logistic regression analyses were used to evaluate if higher HCC/CCI scores were associated with adverse patient outcomes. RESULTS: A total of 69,520, 116,411, and 41,922 respective total knee, hip, and shoulder arthroplasties were identified, respectively. Despite earlier removal from the inpatient-only list, outpatient knee and hip surgical volume did not markedly increase until the pandemic started. By 2022Q4, 16%, 23%, and 36% of hip, knee, and shoulder arthroplasties were discharged on the same day of surgery, respectively. Both HCC and CCI risk scores in outpatients increased over time (P < 0.001). DISCUSSION: TJA procedures are shifting toward outpatient surgery over time, largely driven by the COVID-19 pandemic. TJA outpatients' HCC and CCI risk scores increased over this same period, and additional research to determine the effects of this should be pursued. LEVEL OF EVIDENCE: Level III, therapeutic retrospective cohort study.

Redesign of radiotherapy for prostate cancer: a proposal for universal healthcare systems.

Aim: This study was designed to recommend strategies to improve prostate patients' access to radiotherapy treatment in the Brazilian Unified Health System, along with a cost-tool to support radiotherapy care pathways' lead times and costs. Methods: Data was collected prospectively from patients with prostate cancer receiving radiotherapy in two Brazilian centers to provide data to apply design thinking and process reengineering techniques. The current status of the radiotherapy pathway was determined and the length of time taken for in-hospital activities was measured using data exported from ARIA®. Interviews with patients were used to estimate their waiting periods. This provided the data used to provide recommended strategies and the cost tool based on time-driven activity-based costing. The strategies were classified according to priority. Results: Data from 47 patients were analyzed. The mean interval from diagnosis to start of radiotherapy was 349 days (SD581), and the mean interval from seeking medical attention to starting treatment was 635 days (SD629). Twelve strategies affecting in-hospital processes and 11 impacting patients' care pathways and experiences are recommended, mostly focused on system improvement opportunities. A time-driven activity-based costing monitoring using data extracted from ARIA was coded and can be used by centers as a cost assessment guide. Conclusion: This study uses reengineering and design techniques to introduce priority strategies to allow more efficient and patient-centered radiotherapy.

Cost standard set program: moving forward to standardization of cost assessment based on clinical condition.

This communication piece is reporting the launching of the International Cost Standard set program, aiming to introduce standardized frameworks to measure costs for specific clinical conditions worldwide. A scientific committee including 16 international healthcare cost assessment experts from several countries, and International Consortium for Health Outcomes Measurement was formed to introduce the program. The committee got together in Lisbon for a first scientific meeting, followed by an international conference where time-driven activity-based costing applied studies were shared with the community. The cost standard set program start to offer instruments for people to measure with real-world data, the financial impact of having access to health technologies, improving the ability to evaluate inequity. Those advances might represent a paradigm shift in our ability to generate cost information on an individual level.

Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers.

BACKGROUND: With the rapid adoption of electronic medical records (EMRs), there is an ever-increasing opportunity to collect data and extract knowledge from EMRs to support patient-centered stroke management. OBJECTIVE: This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of clinical patient outcomes and the extraction of patient characteristics from EMRs. METHODS: Our study addressed the computational problems of information extraction and automatic text classification. We identified essential tasks to be considered in an ischemic stroke value-based program. The 30 selected tasks were classified (manually labeled by specialists) according to the following value agenda: tier 1 (achieved health care status), tier 2 (recovery process), care related (clinical management and risk scores), and baseline characteristics. The analyzed data set was retrospectively extracted from the EMRs of patients with stroke from a private Brazilian hospital between 2018 and 2019. A total of 44,206 sentences from free-text medical records in Portuguese were used to train and develop 10 supervised computational machine learning methods, including state-of-the-art neural and nonneural methods, along with ontological rules. As an experimental protocol, we used a 5-fold cross-validation procedure repeated 6 times, along with subject-wise sampling. A heatmap was used to display comparative result analyses according to the best algorithmic effectiveness (F1 score), supported by statistical significance tests. A feature importance analysis was conducted to provide insights into the results. RESULTS: The top-performing models were support vector machines trained with lexical and semantic textual features, showing the importance of dealing with noise in EMR textual representations. The support vector machine models produced statistically superior results in 71% (17/24) of tasks, with an F1 score >80% regarding care-related tasks (patient treatment location, fall risk, thrombolytic therapy, and pressure ulcer risk), the process of recovery (ability to feed orally or ambulate and communicate), health care status achieved (mortality), and baseline characteristics (diabetes, obesity, dyslipidemia, and smoking status). Neural methods were largely outperformed by more traditional nonneural methods, given the characteristics of the data set. Ontological rules were also effective in tasks such as baseline characteristics (alcoholism, atrial fibrillation, and coronary artery disease) and the Rankin scale. The complementarity in effectiveness among models suggests that a combination of models could enhance the results and cover more tasks in the future. CONCLUSIONS: Advances in information technology capacity are essential for scalability and agility in measuring health status outcomes. This study allowed us to measure effectiveness and identify opportunities for automating the classification of outcomes of specific tasks related to clinical conditions of stroke victims, and thus ultimately assess the possibility of proactively using these machine learning techniques in real-world situations.

The Economic Impact of COVID-19 Treatment at a Hospital-level: Investment and Financial Registers of Brazilian Hospitals.

Background: The economic impact associated with the treatment strategies of coronavirus disease-2019 (COVID-19) patients by hospitals and health-care systems in Brazil is unknown and difficult to estimate. This research describes the investments made to absorb the demand for treatment and the changes in occupation rates and billing in Brazilian hospitals. Methods: This research covers the initial findings of "COVID-19 hospital costs and the proposition of a bundled reimbursement strategy for the health-care system," which includes 10 hospitals. The chief financial officer, the chief medical officer, and hospital executives of each participating hospital provided information regarding investments attributed to COVID-19 patient treatment. The analysis included variations in occupation rates and billing from 2019 to 2020 observed in each institution, and the investments for medical equipment, individual protection materials and building construction per patient treated. Results: The majority of hospitals registered a decrease in hospitalization rates and revenue from 2019 to 2020. For intensive care units (ICUs), the mean occupancy rate ranged from 88% to 83%, and for wards, it ranged from 85% to 73%. Monthly average revenue decreased by 10%. The mean hospital investment per COVID-19 inpatient was I$6800 (standard deviation 7664), with the purchase of ventilators as the most common investment. For this item, the mean, highest and lowest acquisition cost per ventilator were, respectively, I$31 468, I$48 881 and I$17 777. Conclusion: There was significant variability in acquisition costs and investments by institution for responding to the COVID-19 pandemic. These findings highlight the importance of continuing microeconomic studies for a comprehensive assessment of hospital costs. Only with more detailed analyses, will it be possible to define and drive sustainable strategies to manage and reimburse COVID-19 treatment in health-care systems.