Identifying modifiable and nonmodifiable cost drivers of ambulatory rotator cuff repair: a machine learning analysis

The Effect of Social Drivers of Health on 90-Day Readmission Rates and Costs After Primary Total Hip and Total Knee Arthroplasty

INTRODUCTION

The effect of social drivers of health (SDOH) on readmissions and costs after total hip arthroplasty (THA) and total knee arthroplasty (TKA) is poorly understood. Policies such as the Hospital Readmissions Reduction Program have targeted overall readmission reduction, using value-based strategies to improve healthcare quality. However, the implications of SDOH on these outcomes are not yet understood. We hypothesized that the area deprivation index (ADI) as a surrogate for SDOH would markedly influence readmission rates and healthcare costs in the 90-day postprocedural period for THA and TKA.

METHODS

We used the 100% US fee-for-service Medicare claims data from 2019 to 2021. Patients were identified using diagnosis-related groups. Our primary outcomes included 90-day unplanned readmission after hospital discharge and cost of care, treated as "high cost" if > 1 standard deviation above the mean. The relationships between ADI and primary outcomes were estimated with logistic regression models.

RESULTS

A total of 628,399 patients were included in this study. The mean age of patients was 75.6, 64% were female, and 7.8% were dually eligible for Medicaid. After full covariate adjustment, readmission was higher for patients in more deprived areas (high Area Deprivation Index (ADI)) (low socioeconomic status (SES) group OR: 1.30 [95% confidence intervals 1.23, 1.38]). ADI was associated with high cost before adjustment (low SES group odds ratio 1.08 [95% confidence intervals 1.04, 1.11], P < 0.001), although, after adjustment, this association was lost.

DISCUSSION

This analysis highlights the effect of SDOH on readmission rates after THA and TKA. A nuanced understanding of neighborhood-level disparities may facilitate targeted strategies to reduce avoidable readmissions in orthopaedic surgery. Regarding cost, although there is some association between ADI and cost, this study may illustrate that ADI for THA and TKA is not sufficiently granular to identify the contribution of social drivers to elevated costs.

Machine learning: implications and applications for ambulatory anesthesia

PURPOSE OF REVIEW

This review explores the timely and relevant applications of machine learning in ambulatory anesthesia, focusing on its potential to optimize operational efficiency, personalize risk assessment, and enhance patient care.

RECENT FINDINGS

Machine learning models have demonstrated the ability to accurately forecast case durations, Post-Anesthesia Care Unit (PACU) lengths of stay, and risk of hospital transfers based on preoperative patient and procedural factors. These models can inform case scheduling, resource allocation, and preoperative evaluation. Additionally, machine learning can standardize assessments, predict outcomes, improve handoff communication, and enrich patient education.

SUMMARY

Machine learning has the potential to revolutionize ambulatory anesthesia practice by optimizing efficiency, personalizing care, and improving quality and safety. However, limitations such as algorithmic opacity, data biases, reproducibility issues, and adoption barriers must be addressed through transparent, participatory design principles and ongoing validation to ensure responsible innovation and incremental adoption.

Artificial intelligence in shoulder and elbow surgery: overview of current and future applications

Artificial intelligence (AI) is amongst the most rapidly growing technologies in orthopedic surgery. With the exponential growth in healthcare data, computing power, and complex predictive algorithms, this technology is poised to aid providers in data processing and clinical decision support throughout the continuum of orthopedic care. Understanding the utility and limitations of this technology is vital to practicing orthopedic surgeons, as these applications will become more common place in everyday practice. AI has already demonstrated its utility in shoulder and elbow surgery for imaging-based diagnosis, predictive modeling of clinical outcomes, implant identification, and automated image segmentation. The future integration of AI and robotic surgery represents the largest potential application of AI in shoulder and elbow surgery with the potential for significant clinical and financial impact. This editorial's purpose is to summarize common AI terms, provide a framework to understand and interpret AI model results, and discuss current applications and future directions within shoulder and elbow surgery.

Ambulatory Surgery Centers Reduce Patient Out-of-Pocket Expenditures for Isolated Arthroscopic Rotator Cuff Repair, but Patient Out-of-Pocket Expenditures Are Increasing at a Faster Rate Than Total Healthcare Utilization Reimbursement From Payers

PURPOSE

To categorize and trend annual out-of-pocket expenditures for arthroscopic rotator cuff repair (RCR) patients relative to total healthcare utilization (THU) reimbursement and compare drivers of patient out-of-pocket expenditures (POPE) in a granular fashion via analyses by insurance type and surgical setting.

METHODS

Patients who underwent outpatient arthroscopic RCR in the United States from 2013 to 2018 were identified from the IBM MarketScan Database. Primary outcome variables were total POPE and THU reimbursement, which were calculated for all claims in the 9-month perioperative period. Trends in outcome variables over time and differences across insurance types were analyzed. Multivariable analysis was performed to investigate drivers of POPE.

RESULTS

A total of 52,330 arthroscopic RCR patients were identified. Between 2013 and 2018, median POPE increased by 47.5% ($917 to $1,353), and median THU increased by 9.3% ($11,964 to $13,076). Patients with high deductible insurance plans paid $1,910 toward their THU, 52.5% more than patients with preferred provider plans ($1,253, P = .001) and 280.5% more than patients with managed care plans ($502, P = .001). All components of POPE increased over the study period, with the largest observed increase being POPE for the immediate procedure (P = .001). On multivariable analysis, out-of-network facility, out-of-network surgeon, and high-deductible insurance most significantly increased POPE.

CONCLUSIONS

POPE for arthroscopic RCR increased at a higher rate than THU over the study period, demonstrating that patients are paying an increasing proportion of RCR costs. A large percentage of this increase comes from increasing POPE for the immediate procedure. Out-of-network facility status increased POPE 3 times more than out-of-network surgeon status, and future cost-optimization strategies should focus on facility-specific reimbursements in particular. Last, ambulatory surgery centers (ASCs) significantly reduced POPE, so performing arthroscopic RCRs at ASCs is beneficial to cost-minimization efforts.

CLINICAL RELEVANCE

This study highlights that although payers have increased reimbursement for RCR, patient out-of-pocket expenditures have increased at a much higher rate. Furthermore, this study elucidates trends in and drivers of patient out-of-pocket payments for RCR, providing evidence for development of cost-optimization strategies and counseling of patients undergoing RCR.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review

» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

Current clinical applications of artificial intelligence in shoulder surgery: what the busy shoulder surgeon needs to know and what's coming next

Background

Artificial intelligence (AI) is a continuously expanding field with the potential to transform a variety of industries-including health care-by providing automation, efficiency, precision, accuracy, and decision-making support for simple and complex tasks. Basic knowledge of the key features as well as limitations of AI is paramount to understand current developments in this field and to successfully apply them to shoulder surgery. The purpose of the present review is to provide an overview of AI within orthopedics and shoulder surgery exploring current and forthcoming AI applications.

Methods

PubMed and Scopus databases were searched to provide a narrative review of the most relevant literature on AI applications in shoulder surgery.

Results

Despite the enormous clinical and research potential of AI, orthopedic surgery has been a relatively late adopter of AI technologies. Image evaluation, surgical planning, aiding decision-making, and facilitating patient evaluations over time are some of the current areas of development with enormous opportunities to improve surgical practice, research, and education. Furthermore, the advancement of AI-driven strategies has the potential to create a more efficient medical system that may reduce the overall cost of delivering and implementing quality health care for patients with shoulder pathology.

Conclusion

AI is an expanding field with the potential for broad clinical and research applications in orthopedic surgery. Many challenges still need to be addressed to fully leverage the potential of AI to clinical practice and research such as privacy issues, data ownership, and external validation of the proposed models.

Artificial Intelligence and Machine Learning in Rotator Cuff Tears

Rotator cuff tears (RCTs) negatively impacts patient well-being. Artificial intelligence (AI) is emerging as a promising tool in medical decision-making. Within AI, deep learning allows to autonomously solve complex tasks. This review assesses the current and potential applications of AI in the management of RCT, focusing on diagnostic utility, challenges, and future perspectives. AI demonstrates promise in RCT diagnosis, aiding clinicians in interpreting complex imaging data. Deep learning frameworks, particularly convoluted neural networks architectures, exhibit remarkable diagnostic accuracy in detecting RCTs on magnetic resonance imaging. Advanced segmentation algorithms improve anatomic visualization and surgical planning. AI-assisted radiograph interpretation proves effective in ruling out full-thickness tears. Machine learning models predict RCT diagnosis and postoperative outcomes, enhancing personalized patient care. Challenges include small data sets and classification complexities, especially for partial thickness tears. Current applications of AI in RCT management are promising yet experimental. The potential of AI to revolutionize personalized, efficient, and accurate care for RCT patients is evident. The integration of AI with clinical expertise holds potential to redefine treatment strategies and optimize patient outcomes. Further research, larger data sets, and collaborative efforts are essential to unlock the transformative impact of AI in orthopedic surgery and RCT management.

Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review

Background

Artificial intelligence (AI) aims to simulate human intelligence using automated computer algorithms. There has been a rapid increase in research applying AI to various subspecialties of orthopedic surgery, including shoulder surgery. The purpose of this review is to assess the scope and validity of current clinical AI applications in shoulder surgery literature.

Methods

A systematic literature review was conducted using PubMed for all articles published between January 1, 2010 and June 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (shoulder OR shoulder surgery OR rotator cuff). All studies that examined AI application models in shoulder surgery were included and evaluated for model performance and validation (internal, external, or both).

Results

A total of 45 studies were included in the final analysis. Eighteen studies involved shoulder arthroplasty, 13 rotator cuff, and 14 other areas. Studies applying AI to shoulder surgery primarily involved (1) automated imaging analysis including identifying rotator cuff tears and shoulder implants (2) risk prediction analyses including perioperative complications, functional outcomes, and patient satisfaction. Highest model performance area under the curve ranged from 0.681 (poor) to 1.00 (perfect). Only 2 studies reported external validation.

Conclusion

Applications of AI in the field of shoulder surgery are expanding rapidly and offer patient-specific risk stratification for shared decision-making and process automation for resource preservation. However, model performance is modest and external validation remains to be demonstrated, suggesting increased scientific rigor is warranted prior to deploying AI-based clinical applications.