Duration of Care and Operative Time Are the Primary Drivers of Total Charges After Ambulatory Hip Arthroscopy: A Machine Learning Analysis

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.

Diagnostic performance of deep learning for leg length measurements on radiographs in leg length discrepancy: A systematic review

Purpose

To systematically review the literature regarding machine learning in leg length discrepancy (LLD) and to provide insight into the most relevant manuscripts on this topic in order to highlight the importance and future clinical implications of machine learning in the diagnosis and treatment of LLD.

Methods

A systematic electronic search was conducted using PubMed, OVID/Medline and Cochrane libraries in accordance with Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Two observers independently screened the abstracts and titles of potential articles.

Results

A total of six studies were identified in the search. All measurements were calculated using standardized anterior-posterior long-leg radiographs. Five (83.3%) of the studies used measurements of the femoral length, tibial length and leg length to assess LLD, whereas one (16.6%) study used the iliac crest height difference to quantify LLD. The deep learning models showed excellent reliability in predicting all length measurements with intraclass correlation coefficients ranging from 0.98 to 1.0 and mean absolute error (MAE) values ranging from 0.11 to 0.45 cm. Three studies reported measurements of LLD, and the convolutional neural network model showed the lowest MAE of 0.13 cm in predicting LLD.

Conclusions

Machine learning models are effective and efficient in determining LLD. Implementation of these models may reduce cost, improve efficiency and lead to better overall patient outcomes.

Clinical Relevance

This review highlights the potential of deep learning (DL) algorithms for accurate and reliable measurement of lower limb length and leg length discrepancy (LLD) on long-leg radiographs. The reported mean absolute error and intraclass correlation coefficient values indicate that the performance of the DL models was comparable to that of radiologists, suggesting that DL-based assessments could potentially be used to automate the measurement of lower limb length and LLD in clinical practice.

Level of Evidence

Level IV.

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 R 2 = 0.332), operating surgeon (partial R 2 = 0.326), osteoplasty type (partial R 2 = 0.087), and surgery center (partial R 2 = 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%; P trend = 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.

Machine learning prediction of hospitalization costs for coronary artery bypass grafting operations

BACKGROUND

With the steady rise in health care expenditures, the examination of factors that may influence the costs of care has garnered much attention. Although machine learning models have previously been applied in health economics, their application within cardiac surgery remains limited. We evaluated several machine learning algorithms to model hospitalization costs for coronary artery bypass grafting.

METHODS

All adult hospitalizations for isolated coronary artery bypass grafting were identified in the 2016 to 2020 Nationwide Readmissions Database. Machine learning models were trained to predict expenditures and compared with traditional linear regression. Given the significance of postoperative length of stay, we additionally developed models excluding postoperative length of stay to uncover other drivers of costs. To facilitate comparison, machine learning classification models were also trained to predict patients in the highest decile of costs. Significant factors associated with high cost were identified using SHapley Additive exPlanations beeswarm plots.

RESULTS

Among 444,740 hospitalizations included for analysis, the median cost of hospitalization in coronary artery bypass grafting patients was $43,103. eXtreme Gradient Boosting most accurately predicted hospitalization costs, with R2 = 0.519 over the validation set. The top predictive features in the eXtreme Gradient Boosting model included elective procedure status, prolonged mechanical ventilation, new-onset respiratory failure or myocardial infarction, and postoperative length of stay. After removing postoperative length of stay, eXtreme Gradient Boosting remained the most accurate model (R2 = 0.38). Prolonged ventilation, respiratory failure, and elective status remained important predictive parameters.

CONCLUSION

Machine learning models appear to accurately model total hospitalization costs for coronary artery bypass grafting. Future work is warranted to uncover other drivers of costs and improve the value of care in cardiac surgery.

Time-Driven Activity-Based Costing Analysis Identifies Use of Consumables and Operating Room Time as Factors Associated With Increased Cost of Outpatient Primary Hip Arthroscopic Labral Repair

PURPOSE

To use time-driven, activity-based costing (TDABC) methodology to investigate drivers of cost variation and to elucidate preoperative and intraoperative factors associated with increased cost of outpatient arthroscopic hip labral repair.

METHODS

A retrospective analysis of data from January 2020 to October 2021 was performed. Patients undergoing primary hip arthroscopy for labral repair in the outpatient setting were included. Indexed TDABC data from Avant-garde Health's analytics platform were used to represent cost-of-care breakdowns. Patients in the top decile of cost were defined as high cost, and cost category variance was determined as a percent increase between high and low cost. Analyses tested for associations between preoperative and perioperative factors with total cost. Surgical procedures performed concomitantly to labral repair were included in subanalyses.

RESULTS

Data from 151 patients were analyzed. Consumables made up 61% of total outpatient cost with surgical personnel costs (30%) being the second largest category. The average total cost was 19% higher for patients in the top decile of cost compared to the remainder of the cohort. Factors contributing to this difference were implants (36% higher), surgical personnel (20% higher), and operating room (OR) consumables (15% higher). Multivariate linear regression modeling indicated that OR time (Standardized β = 0.504; P < .001) and anchor quantity (standardized β = 0.443; P < .001) were significant predictors of increased cost. Femoroplasty (Unstandardized β = 15.274; P = .010), chondroplasty (Unstandardized β = 8.860; P = .009), excision of os acetabuli (unstandardized β = 13.619; P = .041), and trochanteric bursectomy (Unstandardized β = 21.176; P = .009) were also all independently associated with increasing operating time.

CONCLUSIONS

TDABC analysis showed that OR consumables and implants were the largest drivers of cost for the procedure. OR time was also shown to be a significant predictor of increased costs.

LEVEL OF EVIDENCE

Level IV, economic analysis.

Demographic and Socioeconomic Patient Data Are Rarely Included in Randomized Controlled Trials for Femoral Acetabular Impingement and Hip Arthroscopy: A Systematic Review

Purpose

To determine the rate of reporting for sociodemographic variables in randomized controlled trials (RCTs) investigating femoral acetabular impingement (FAI) and hip arthroscopy.

Methods

PubMed, Scopus, and Web of Science were queried for articles relating to FAI and hip arthroscopy. Articles included in final analysis were RCTs investigating operative management of FAI. Included RCTs were analyzed for reporting of age and sex or gender as well as the following sociodemographic variables: race, ethnicity, insurance status, income, housing status, work status, and education level in the results section or any section of the paper. Data was analyzed using χ2 and Fisher exact tests with significance defined as P < .05.

Results

Forty-eight RCTs were identified from 2011 to 2023. Age was reported in 48 of 48 (100%) of included papers; sex or gender was reported in 47 of 48 (97.9%). Reporting of sociodemographic variables in any section respectively was: race (7/48, 14.6%), ethnicity (4/48, 8.33%), insurance status (0/48, 0%), income (1/48, 2.08%), housing status (0/48, 0%), work status (3/48, 6.25%), and education (2/48, 4.17%). There was no significant difference for reporting demographic variables with respect to journal or year of publication (P = .666 and P = .761, respectively). Sociodemographic variables (9/48) were reported significantly less frequently than age and sex or gender (48/48) (P < .001).

Conclusions

This study found that sociodemographic variables in FAI and hip arthroscopy RCTs are reported with much lower frequency than age and sex or gender. These findings may demonstrate the need to include patient sociodemographic data in RCTs so that their results can be better generalized and applied to the appropriate patient population.

Level of Evidence

Level II, systematic review of level I and II evidence.

Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications

Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.

Increased rate of out-of-network surgeon selection for hip arthroscopy compared to more common orthopedic sports procedures

Background

Demand for hip arthroscopy (HA) has increased, but shortfalls in HA training may create disparities in care access. This analysis aimed to (1) compare out-of-network (OON) surgeon utilization for HA with that of more common orthopedics sports procedures, including rotator cuff repair (RCR), partial meniscectomy (PM), and anterior cruciate ligament reconstruction (ACLR), (2) compare the HA OON surgeon rate with another less commonly performed procedure, meniscus allograft transplant (MAT), and (3) analyze trends and predictors of OON surgeon utilization.

Methods

The 2013-2017 IBM MarketScan database identified patients under 65 who underwent HA, RCR, PM, ACLR, or MAT. Demographic differences were determined using standardized differences. Cochran-Armitage tests analyzed trends in OON surgeon utilization. Multivariable logistic regression identified predictors of OON surgeon utilization. Statistical significance was set to p < 0.05 and significant standardized differences were >0.1.

Results

410,487 patients were identified, of which 12,636 patients underwent HA, 87,607 RCR, 233,241 PM, 76,700 ACLR, and 303 MAT. OON surgeon utilization increased for HA, rising from 7.98 % in 2013 to 9.37 % in 2017 (p = 0.026). Compared to RCR, PM, and ACLR, HA was associated with higher likelihood of OON surgeon utilization. Usage of ambulatory surgery centers (ASCs) was predictive of higher OON surgeon rates along with procedure year, insurance plan type, and geographic region. HA performed in an ASC was 13 % less likely to have an OON surgeon (p = 0.047).

Conclusion

OON surgeon utilization generally declined but increased for HA. HA was a predictor of OON surgeon status, possibly because HA is a technically complicated procedure with fewer trained in-network providers. Other predictors of OON surgeon status included ASC usage, PPO/EPO plan type, and Northeast geographic region. There is a need to improve access to experienced HA providers-perhaps with prioritization of HA training in residency and fellowship programs-in order to address rising OON surgeon utilization.

Assessment of Risk Factors and Rate of Conversion to Total Hip Arthroplasty Within 2 Years After Hip Arthroscopy Utilizing a Large Database of Commercially Insured Patients in the United States

Background

The conversion rate of hip arthroscopy (HA) to total hip arthroplasty (THA) has been reported to be as high as 10%. Despite identifying factors that increase the risk of conversion, current studies do not stratify patients by type of arthroscopic procedure.

Purpose/Hypothesis

To analyze the rate and predictors of conversion to THA within 2 years after HA. It was hypothesized that osteoarthritis (OA) and increased patient age would negatively affect the survivorship of HA.

Study Design

Cohort study; Evidence level, 3.

Methods

The IBM MarketScan database was utilized to identify patients who underwent HA and converted to THA within 2 years at inpatient and outpatient facilities between 2013 and 2017. Patients were split into 3 procedure cohorts as follows: (1) femoroacetabular osteoplasty (FAO), which included treatment for femoroacetabular impingement; (2) isolated debridement; and (3) isolated labral repair. Cohort characteristics were compared using standardized differences. Conversion rates between the 3 cohorts were compared using chi-square tests. The relationship between age and conversion was assessed using linear regression. Predictors of conversion were analyzed using multivariable logistic regression. The median time to conversion was estimated using Kaplan-Meier tests.

Results

A total of 5048 patients were identified, and the rates of conversion to THA were 12.86% for isolated debridement, 8.67% for isolated labral repair, and 6.76% for FAO (standardized difference, 0.138). The isolated labral repair cohort had the shortest median time to conversion (isolated labral repair, 10.88 months; isolated debridement, 10.98 months; and FAO, 11.9 months [P = .034). For patients >50 years, isolated debridement had the highest rate of conversion at 18.8%. The conversion rate increased linearly with age. Factors that increased the odds of conversion to THA were OA, having an isolated debridement procedure, and older patient age (P < .05).

Conclusion

Older patients and those with preexisting OA of the hip were at a significantly increased risk of failing HA and requiring a total hip replacement within 2 years of the index procedure. Younger patients were at low risk of requiring a conversion procedure no matter which arthroscopic procedure was performed.

Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence

The proliferation of generative artificial intelligence (AI) and its extensive potential for integration into many aspects of health care signal a transformational shift within the health care environment. In this context, medical education must evolve to ensure that medical trainees are adequately prepared to navigate the rapidly changing health care landscape. Medical education has moved toward a competency-based education paradigm, leading the Association of American Medical Colleges (AAMC) to define a set of Entrustable Professional Activities (EPAs) as its practical operational framework in undergraduate medical education. The AAMC's 13 core EPAs for entering residencies have been implemented with varying levels of success across medical schools. In this paper, we critically assess the existing core EPAs in the context of rapid AI integration in medicine. We identify EPAs that require refinement, redefinition, or comprehensive change to align with the emerging trends in health care. Moreover, this perspective proposes a set of "emerging" EPAs, informed by the changing landscape and capabilities presented by generative AI technologies. We provide a practical evaluation of the EPAs, alongside actionable recommendations on how medical education, viewed through the lens of the AAMC EPAs, can adapt and remain relevant amid rapid technological advancements. By leveraging the transformative potential of AI, we can reshape medical education to align with an AI-integrated future of medicine. This approach will help equip future health care professionals with technological competence and adaptive skills to meet the dynamic and evolving demands in health care.

Hip Arthroscopy Trends: Increasing Patient Out-of-Pocket Costs, Lower Surgeon Reimbursement, and Cost Reduction With Utilization of Ambulatory Surgery Centers

PURPOSE

To (1) report on trends in immediate procedure reimbursement, patient out-of-pocket expenditures, and surgeon reimbursement in hip arthroscopy (2) compare trends in ambulatory surgery centers (ASC) versus outpatient hospitals (OH) utilization; (3) quantify the cost differences (if any) associated with ASC versus OH settings; and 4) determine the factors that predict ASC utilization for hip arthroscopy.

METHODS

The cohort for this descriptive epidemiology study was any patient over 18 years identified in the IBM MarketScan Commercial Claims Encounter database who underwent an outpatient hip arthroscopy, identified by Current Procedural Terminology codes, in the United States from 2013 to 2017. Immediate procedure reimbursement, patient out-of-pocket expenditure, and surgeon reimbursement were calculated, and a multivariable model was used to determine the influence of specific factors on these outcome variables. Statistically significant P values were less than .05, and significant standardized differences were more than 0.1.

RESULTS

The cohort included 20,335 patients. An increasing trend in ASC utilization was observed (P = .001), and ASC utilization for hip arthroscopy was 32.4% in 2017. Patient out-of-pocket expenditures for femoroacetabular impingement surgery increased 24.3% over the study period (P = .003), which was higher than the rate for immediate procedure reimbursement (4.2%; P = .007). ASCs were associated with $3,310 (28.8%; P = .001) reduction in immediate procedure reimbursement and $47 (6.2%; P = .001) reduction in patient out-of-pocket expenditure per hip arthroscopy.

CONCLUSIONS

ASCs provide a significant cost difference for hip arthroscopy. Although there is an increasing trend toward ASC utilization, it remains relatively low at 32.4% in 2017. Thus, there are opportunities for expanded ASC utilization, which is associated with significant immediate procedure reimbursement difference of $3,310 and patient out-of-pocket expenditure difference of $47 per hip arthroscopy case, ultimately benefiting healthcare systems, surgeons, and patients alike.

LEVEL OF EVIDENCE

Level III, retrospective comparative trial.

Surgical Setting in Achilles Tendon Repair: How Does It Relate to Costs and Complications?

Background

Primary Achilles tendon repair (ATR) can be performed in ambulatory surgery centers (ASCs) or hospitals. We compared costs and complication rates of ATR performed in these settings.

Methods

We retrospectively queried the electronic medical record of our academic health system and identified 97 adults who underwent primary ATR from 2015 to 2021. Variables were compared between patients treated at ASCs vs those treated in hospitals. We compared continuous variables with Wilcoxon rank-sum tests and categorical variables with χ2 tests. We used an α of 0.05. Multivariable logistic regression was performed to determine associations between surgical setting and costs. Linear regression was performed between each charge subtype and total cost to identify which charge subtypes were most associated with total cost.

Results

Patients who underwent ATR in hospitals had a higher rate of unanticipated postoperative hospital admission (13%) than those treated in ASCs (0%) (P = .01). We found no differences with regard to postoperative complications, emergency department visits, readmission, rerupture, reoperation/revision, or death. Patients treated in hospitals had a higher mean (±SD) implant cost ($664 ± $810) than those treated in ASCs ($175 ± $585) (P < .01). We found no differences between settings with regard to total cost, supply costs, operating room charges, or anesthesia charges. Higher implant cost was associated with hospital setting (odds ratio = 16 [95% CI: 1.7-157]) and body mass index > 25 (odds ratio = 1.2 [95% CI: 1.0-1.5]). Operating room costs were strongly correlated with total costs (R2 = .94).

Conclusion

The overall cost and complication rate of ATRs were not significantly different between ASCs and hospitals. ATRs performed in hospitals had higher implant costs and higher rates of postoperative admission than those performed in ASCs.

Level of Evidence

Level III, retrospective comparative study.

Risk factors for secondary meniscus tears can be accurately predicted through machine learning, creating a resource for patient education and intervention

PURPOSE

This study sought to develop and internally validate a machine learning model to identify risk factors and quantify overall risk of secondary meniscus injury in a longitudinal cohort after primary ACL reconstruction (ACLR).

METHODS

Patients with new ACL injury between 1990 and 2016 with minimum 2-year follow-up were identified. Records were extensively reviewed to extract demographic, treatment, and diagnosis of new meniscus injury following ACLR. Four candidate machine learning algorithms were evaluated to predict secondary meniscus tears. Performance was assessed through discrimination using area under the receiver operating characteristics curve (AUROC), calibration, and decision curve analysis; interpretability was enhanced utilizing global variable importance plots and partial dependence curves.

RESULTS

A total of 1187 patients underwent ACLR; 139 (11.7%) experienced a secondary meniscus tear at a mean time of 65 months post-op. The best performing model for predicting secondary meniscus tear was the random forest (AUROC = 0.790, 95% CI: 0.785-0.795; calibration intercept = 0.006, 95% CI: 0.005-0.007, calibration slope = 0.961 95% CI: 0.956-0.965, Brier's score = 0.10 95% CI: 0.09-0.12), and all four machine learning algorithms outperformed traditional logistic regression. The following risk factors were identified: shorter time to return to sport (RTS), lower VAS at injury, increased time from injury to surgery, older age at injury, and proximal ACL tear.

CONCLUSION

Machine learning models outperformed traditional prediction models and identified multiple risk factors for secondary meniscus tears after ACLR. Following careful external validation, these models can be deployed to provide real-time quantifiable risk for counseling and timely intervention to help guide patient expectations and possibly improve clinical outcomes.

LEVEL OF EVIDENCE

III.

Editorial Commentary: Knowledge is Power: A Primer for Machine Learning

Machine learning (ML) has become an increasingly common statistical methodology in medical research. In recent years, ML techniques have been used with greater frequency to evaluate orthopaedic data. ML allows for the creation of adaptive predictive models that can be applied to clinical patient outcomes. However, ML models for predicting clinical or safety outcomes may be made available online so that physicians may apply these models to their patients to make predictions. If the algorithms have not been externally validated, then the models are not likely to generalize, and their predictions will suffer from inaccuracy. This is especially important to bear in mind because the recent increase in ML papers in the medical literature includes publications with fundamental flaws.

Implementation of Machine Learning to Predict Cost of Care Associated with Ambulatory Single-Level Lumbar Decompression

BACKGROUND

With the emergence of the concept of value-based care, efficient resource allocation has become an increasingly prominent factor in surgical decision-making. Validated machine learning (ML) models for cost prediction in outpatient spine surgery are limited. As such, we developed and internally validated a supervised ML algorithm to reliably identify cost drivers associated with ambulatory single-level lumbar decompression surgery.

METHODS

A retrospective review of the New York State Ambulatory Surgical Database was performed to identify patients who underwent single-level lumbar decompression from 2014 to 2015. Patients with a length of stay of >0 were excluded. Using pre- and intraoperative parameters (features) derived from the New York State Ambulatory Surgical Database, an optimal supervised ML model was ultimately developed and internally validated after 5 candidate models were rigorously tested, trained, and compared for predictive performance related to total charges. The best performing model was then evaluated by testing its performance on identifying relationships between features of interest and cost prediction. Finally, the best performing algorithm was entered into an open-access web application.

RESULTS

A total of 8402 patients were included. The gradient-boosted ensemble model demonstrated the best performance assessed via internal validation. Major cost drivers included anesthesia type, operating room time, race, patient income and insurance status, community type, worker's compensation status, and comorbidity index.

CONCLUSIONS

The gradient-boosted ensemble model predicted total charges and associated cost drivers associated with ambulatory single-level lumbar decompression using a large, statewide database with excellent performance. External validation of this algorithm in future studies may guide practical application of this clinical tool.

Editorial Commentary: Machine Learning in Orthopaedics: Venturing Into the Valley of Despair

Machine learning, a subset of artificial intelligence, has become increasingly common in the analysis of orthopaedic data. The resources needed to utilize machine-learning approaches for data analysis have become increasingly accessible to researchers, contributing to a recent influx of research using these techniques. As machine learning becomes increasingly available, misapplication owing to a lack of competence becomes more common. Sensationalized titles, misused vernacular, and a failure to fully vet machine learning-derived algorithms are just a few issues that warrant attention. As the orthopaedic community's knowledge on this topic grows, the flaws in our understanding of this field will likely become apparent, allowing for rectification and ultimately improvement of how machine learning is utilized in research.

Editorial Commentary: Machine Learning Can Indicate Hip Arthroscopy Procedures, Predict Postoperative Improvement, and Estimate Costs

Complex statistical approaches are increasingly being used in the orthopaedic literature, and this is especially true in the field of sports medicine. Tools such as machine learning provide the opportunity to analyze certain research areas that would often require the complex assessment of large amounts of data. Generally, decision making is multifactorial and based upon experience, personal capabilities, available utilities, and literature. Given the difficulty associated with determining the optimal patient treatment, many studies have moved toward more complex statistical approaches to create algorithms that take large amounts of data and distill it into a formula that may guide surgeons to better patient outcomes while estimating and even optimizing costs. In the future, this clinical and economic information will play an important role in patient management.