Medical Students' Perceptions of Clinical and Research Training: An International Needs Assessment of 26 Countries.

Objective: Despite calls to incorporate research training into medical school curriculum, minimal research has been conducted to elucidate trends in research knowledge, opportunities, and involvement globally. This study aims to: (1) assess medical students' perceptions of the level of training they received on research based on their medical school training, and (2) evaluate the obstacles related to conducting research as part of medical students' training. Methods: A 94-question, bilingual survey designed by a small focus group of individuals from medical schools across the globe and administered to medical students from different parts of the world, distributed via social media networks (Twitter, Now X, Facebook) and email distributions via international partnerships from November 1 to December 31, 2020. The survey collected demographic information including age, gender, medical institution and country, degree, year in training, clinical rotations completed, plans for specialization, and additional graduate degrees completed. Statistical analysis included a summary of survey participant characteristics, and a comparison between regions, with a variety of comparison and logistic regression models used. Results: A total of 318 medical students from 26 countries successfully completed the survey. Respondents were majority female (60.1%), from Latin America (LA) (53.1%), North America (NA) (28.6%), and Other world regions (Other) (18.2%). Students felt research was an important component of medical training (87.7%), although many reported lacking research support from their institution (47.5%). There were several reported barriers to research, including lack of research opportunities (69.4%), lack of mentors (56.6%), lack of formal training (54.6%), and barriers due to the coronavirus disease 2019 (COVID-19) pandemic (49.3%). Less frequent were barriers related to financial resources (41.6%), physical resources (computer or internet access) (18%), and English language ability (6.9%). Students from Latin America and Other were more likely to report a desire to pursue research later in their medical careers compared with students from North America. Conclusions and Implications for Translation: Despite significant interest in research, medical students globally report a lack of formal research training, opportunities, and several barriers to conducting research, including the COVID-19 pandemic. The study highlights the need for student research training internationally and the role of further regional-specific and institutional-specific evaluation of research training needs.

Development of a Machine Learning Model to Predict Outcomes and Cost After Cardiac Surgery.

BACKGROUND: Machine learning (ML) algorithms may enhance outcomes prediction and help guide clinical decision making. This study aimed to develop and validate a ML model that predicts postoperative outcomes and costs after cardiac surgery. METHODS: The Society of Thoracic Surgeons registry data from 4874 patients who underwent cardiac surgery (56% coronary artery bypass grafting, 42% valve surgery, 19% aortic surgery) at our institution were divided into training (80%) and testing (20%) datasets. The Extreme Gradient Boosting decision-tree ML algorithms were trained to predict three outcomes: operative mortality, major morbidity or mortality, and Medicare outlier high hospitalization cost. Algorithm performance was determined using accuracy, F1 score, and area under the precision-recall curve (AUC-PR). The ML algorithms were validated in index surgery cases with The Society of Thoracic Surgeons risk scores for mortality and major morbidities and with logistic regression and were then applied to nonindex cases. RESULTS: The ML algorithms with 25 input parameters predicted operative mortality (accuracy 95%; F1 0.31; AUC-PR 0.21), major morbidity or mortality (accuracy 71%, F1 0.47; AUC-PR 0.47), and high cost (accuracy 84%; F1 0.62; AUC-PR 0.65). Preoperative creatinine, complete blood count, patient height and weight, ventricular function, and liver dysfunction were important predictors for all outcomes. For patients undergoing nonindex cardiac operations, the ML model achieved an AUC-PR of 0.15 (95% CI, 0.05-0.32) for mortality and 0.59 (95% CI, 0.51-0.68) for major morbidity or mortality. CONCLUSIONS: The extreme gradient boosting ML algorithms can predict mortality, major morbidity, and high cost after cardiac surgery, including operations without established risk models. These ML algorithms may refine risk prediction after cardiac surgery for a wide range of procedures.

Addressing Supply Chain Management Issues in Cost-effective Maternal and Pediatric Global Surgery: A Call to Action.

Persistent global disparities in maternal and neonatal outcomes exist, in part, due to a lack of access to safe surgery. This commentary examines the relative need for increased focus on access to safe maternal and pediatric surgery globally, starting with a focus on cost-effective surgeries. There is a need to understand context-specific surgeries for regions, including understanding regional versus tertiary development. Most important is a need to understand the crucial role of supply chain management (SCM) in developing better access to maternal and pediatric surgery in limited resource settings. We evaluate the role of SCM in global surgery and global health, and the current landscape of inefficiency. We outline specific findings and takeaways from recent solutions developed in pediatric and maternal surgery to address SCM inefficiencies. We then examine the applicability to other settings and look at the future. Our goal is to summarize the challenges that exist today in a global setting to provide better access to maternal and pediatric surgery and outline solutions relying on structural, SCM-related framework.

Can a machine learning model accurately predict patient resource utilization following lumbar spinal fusion?

BACKGROUND CONTEXT: With the increasing emphasis on value-based healthcare in Centers for Medicare and Medicaid Services reimbursement structures, bundled payment models have been adopted for many orthopedic procedures. Immense variability of patients across hospitals and providers makes these models potentially less viable in spine surgery. Machine-learning models have been shown reliable at predicting patient-specific outcomes following lumbar spine surgery and could, therefore, be applied to developing stratified bundled payment schemes. PURPOSE: (1) Can a Naïve Bayes machine-learning model accurately predict inpatient payments, length of stay (LOS), and discharge disposition, following dorsal and lumbar fusion? (2) Can such a model then be used to develop a risk-stratified payment scheme? STUDY DESIGN: A Naïve Bayes machine-learning model was constructed using an administrative database. PATIENT SAMPLE: Patients undergoing dorsal and lumbar fusion for nondeformity indications from 2009 through 2016 were included. Preoperative inputs included age group, gender, ethnicity, race, type of admission, All Patients Refined (APR) risk of mortality, APR severity of illness, and Clinical Classifications Software diagnosis code. OUTCOME MEASURES: Predicted resource utilization outcomes included LOS, discharge disposition, and total inpatient payments. Model validation was addressed via reliability, model output quality, and decision speed, based on application of training and validation sets. Risk-stratified payment models were developed according to APR risk of mortality and severity of illness. RESULTS: A Naïve Bayes machine-learning algorithm with adaptive boosting demonstrated high reliability and area under the receiver-operating characteristics curve of 0.880, 0.941, and 0.906 for cost, LOS, and discharge disposition, respectively. Patients with increased risk of mortality or severity of illness incurred costs resulting in greater inpatient payments in a patient-specific tiered bundled payment, reflecting increased risk on institutions caring for these patients. We found that a large range in expected payments due to individuals' preoperative comorbidities indicating an individualized risk-based model is warranted. CONCLUSIONS: A Naïve Bayes machine-learning model was shown to have good-to-excellent reliability and responsiveness for cost, LOS, and discharge disposition. Based on APR risk of mortality and APR severity of illness, there was a significant difference in episode costs from lowest to highest risk strata. After using normalized model error to develop a risk-adjusted proposed payment plan, it was found that institutions incur significantly more financial risk in flat bundled payment models for patients with higher rates of comorbidities.

Exploration of Gender-Specific Authorship Disparities in the Pain Medicine Literature.

BACKGROUND: Given the readily increasing membership of the pain physician community, efforts toward correcting notable gender disparities are instrumental. The under-representation of women is particularly prevalent within leadership roles in academic medicine, thought to be driven largely by diminished research efforts. Consequently, we aimed to characterize gender differences among the highest impact pain literature. METHODS: The 20 highest cited articles per year from 2014 to 2018 were extracted from each of seven impactful journals affiliated to the largest pain medicine societies. Collected data from each article included genders of the first and last authors, the number of citations accumulated and the journal impact factor at the time of publication. RESULTS: Across all considered literature, female authors were surprisingly not under-represented when considering the national prevalence of female pain physicians. However, more in-depth analysis found trends toward significance to suggest that female authorship was relatively diminished within more impactful and higher cited literature. When exploring gender-gender collaboration patterns, we found that male authors were favored over female counterparts with statistical significance; it must be noted that this likelihood analysis and preference toward male authors may be statistically obfuscated by the high prevalence of male authors. Nonetheless, these findings help to quantify overt, demonstrated disparity patterns. Of note, this inequity may also be fully secondary to the lower number of female pain physicians and/or those involved in research endeavors and decreased number of submissions from female physicians. Establishing gender discrimination patterns as causal factors in such disparities can be extremely challenging to determine. CONCLUSION: In our analysis of authorship between genders within the context of pain medicine literature, we found trends, although non-significant, toward women being lesser represented in the more impactful literature. We suggest that these inequities are possibly resultant of a markedly small and outnumbered female pain physician membership that has yet to achieve a critical mass and possible implicit gender biases that may restrict female authorship. However, further exploration and analysis of this issue are necessary to more clearly illuminate which systemic deficits exist and how they may, in turn, be corrected with cultural and macroscopic organizational-driven change.

Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review.

BACKGROUND: Driven by the rapid development of big data and processing power, artificial intelligence and machine learning (ML) applications are poised to expand orthopedic surgery frontiers. Lower extremity arthroplasty is uniquely positioned to most dramatically benefit from ML applications given its central role in alternative payment models and the value equation. METHODS: In this report, we discuss the origins and model specifics behind machine learning, consider its progression into healthcare, and present some of its most recent advances and applications in arthroplasty. RESULTS: A narrative review of artificial intelligence and ML developments is summarized with specific applications to lower extremity arthroplasty, with specific lessons learned from osteoarthritis gait models, joint-specific imaging analysis, and value-based payment models. CONCLUSION: The advancement and employment of ML provides an opportunity to provide data-driven, high performance medicine that can rapidly improve the science, economics, and delivery of lower extremity arthroplasty.

Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model.

BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create a patient-specific payment model (PSPM) accounting for patient complexity. METHODS: Using 15 preoperative variables from 78,335 primary total hip arthroplasty cases for osteoarthritis from the National Inpatient Sample and our institutional database, an ANN was developed to predict LOS, charges, and disposition. Validity metrics included accuracy and area under the curve of the receiver operating characteristic curve. Predictive uncertainty was stratified by All Patient Refined comorbidity cohort to establish the PSPM. RESULTS: The dynamic model demonstrated "learning" in the first 30 training rounds with areas under the curve of 82.0%, 83.4%, and 79.4% for LOS, charges, and disposition, respectively. The proposed PSPM established a risk increase of 2.5%, 8.9%, and 17.3% for moderate, major, and severe comorbidities, respectively. CONCLUSION: The deep learning ANN demonstrated "learning" with good reliability, responsiveness, and validity in its prediction of value-centered outcomes. This model can be applied to implement a PSPM for tiered payments based on the complexity of the case.

Remote Patient Monitoring Using Mobile Health for Total Knee Arthroplasty: Validation of a Wearable and Machine Learning-Based Surveillance Platform.

BACKGROUND: Recent technologic advances capable of measuring outcomes after total knee arthroplasty (TKA) are critical in quantifying value-based care. Traditionally accomplished through office assessments and surveys with variable follow-up, this strategy lacks continuous and complete data. The primary objective of this study was to validate the feasibility of a remote patient monitoring (RPM) system in terms of the frequency of data interruptions and patient acceptance. Second, we report pilot data for (1) mobility; (2) knee range of motion, (3) patient-reported outcome measures (PROMs); (4) opioid use; and (5) home exercise program (HEP) compliance. METHODS: A pilot cohort of 25 patients undergoing primary TKA for osteoarthritis was enrolled. Patients downloaded the RPM mobile application preoperatively to collect baseline activity and PROMs data, and the wearable knee sleeve was paired to the smartphone during admission. The following was collected up to 3 months postoperatively: mobility (step count), range of motion, PROMs, opioid consumption, and HEP compliance. Validation was determined by acquisition of continuous data and patient tolerance at semistructured interviews 3 months after operation. RESULTS: Of the 25 enrolled patients, 100% had uninterrupted passive data collection. Of the 22 available for follow-up interviews, all found the system motivating and engaging. Mean mobility returned to baseline within 6 weeks and exceeded preoperative baseline by 30% at 3 months. Mean knee flexion achieved was 119°, which did not differ from clinic measurements (P = .31). Mean KOOS improvement was 39.3 after 3 months (range: 3-60). Opioid use typically stopped by postoperative day 5. HEP compliance was 62% (range: 0%-99%). CONCLUSIONS: In this pilot study, we established the ability to remotely acquire continuous data for patients undergoing TKA, who found the application to be engaging. RPM offers the newfound ability to more completely evaluate the patients undergoing TKA in terms of mobility and rehabilitation compliance. Study with more patients is required to establish clinical significance.

Bundled Care for Hip Fractures: A Machine-Learning Approach to an Untenable Patient-Specific Payment Model.

OBJECTIVES: With the transition to a value-based model of care delivery, bundled payment models have been implemented with demonstrated success in elective lower extremity joint arthroplasty. Yet, hip fracture outcomes are dependent on patient-level factors that may not be optimized preoperatively due to acuity of care. The objectives of this study are to (1) develop a supervised naive Bayes machine-learning algorithm using preoperative patient data to predict length of stay and cost after hip fracture and (2) propose a patient-specific payment model to project reimbursements based on patient comorbidities. METHODS: Using the New York Statewide Planning and Research Cooperative System database, we studied 98,562 Medicare patients who underwent operative management for hip fracture from 2009 to 2016. A naive Bayes machine-learning model was built using age, sex, ethnicity, race, type of admission, risk of mortality, and severity of illness as predictive inputs. RESULTS: Accuracy was demonstrated at 76.5% and 79.0% for length of stay and cost, respectively. Performance was 88% for length of stay and 89% for cost. Model error analysis showed increasing model error with increasing risk of mortality, which thus increased the risk-adjusted payment for each risk of mortality. CONCLUSIONS: Our naive Bayes machine-learning algorithm provided excellent accuracy and responsiveness in the prediction of length of stay and cost of an episode of care for hip fracture using preoperative variables. This model demonstrates that the cost of delivery of hip fracture care is dependent on largely nonmodifiable patient-specific factors, likely making bundled care an implausible payment model for hip fractures.

Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models.

BACKGROUND: Value-based payment programs in orthopedics, specifically primary total hip arthroplasty (THA), present opportunities to apply forecasting machine learning techniques to adjust payment models to a specific patient or population. The objective of this study is to (1) develop and validate a machine learning algorithm using preoperative big data to predict length of stay (LOS) and patient-specific inpatient payments after primary THA and (2) propose a risk-adjusted patient-specific payment model (PSPM) that considers patient comorbidity. METHODS: Using an administrative database, we applied 122,334 patients undergoing primary THA for osteoarthritis between 2012 and 16 to a naïve Bayesian model trained to forecast LOS and payments. Performance was determined using area under the receiver operating characteristic curve and percent accuracy. Inpatient payments were grouped as <$12,000, $12,000-$24,000, and >$24,000. LOS was grouped as 1-2, 3-5, and 6+ days. Payment model uncertainty was applied to a proposed risk-based PSPM. RESULTS: The machine learning algorithm required age, race, gender, and comorbidity scores ("risk of illness" and "risk of morbidity") to demonstrate excellent validity, reliability, and responsiveness with an area under the receiver operating characteristic curve of 0.87 and 0.71 for LOS and payment. As patient complexity increased, error for predicting payment increased in tiers of 3%, 12%, and 32% for moderate, major, and extreme comorbidities, respectively. CONCLUSION: Our preliminary machine learning algorithm demonstrated excellent construct validity, reliability, and responsiveness predicting LOS and payment prior to primary THA. This has the potential to allow for a risk-based PSPM prior to elective THA that offers tiered reimbursement commensurate with case complexity. LEVEL OF EVIDENCE: III.

Optimizing the Volume-Value Relationship in Laminectomy: An Evidence-Based Analysis of Outcomes and Economies of Scale.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: The objective of the present study was to establish evidence-based volume thresholds for surgeons and hospitals predictive of enhanced value in the setting of laminectomy. SUMMARY OF BACKGROUND DATA: Previous studies have attempted to characterize the relationship between volume and value; however, none to the authors' knowledge has employed an evidence-based approach to identify thresholds yielding enhanced value. METHODS: In total, 67,758 patients from the New York Statewide Planning and Research Cooperative System database undergoing laminectomy in the period 2009 to 2015 were included. We used stratum-specific likelihood ratio analysis of receiver operating characteristic curves to establish volume thresholds predictive of increased length of stay (LOS) and cost for surgeons and hospitals. RESULTS: Analysis of LOS by surgeon volume produced strata at: <17 (low), 17 to 40 (medium), 41 to 71 (high), and >71 (very high). Analysis of cost by surgeon volume produced strata at: <17 (low), 17 to 33 (medium), 34 to 86 (high), and >86 (very high). Analysis of LOS by hospital volume produced strata at: <43 (very low), 43 to 96 (low), 97 to 147 (medium), 148 to 172 (high), and >172 (very high). Analysis of cost by hospital volume produced strata at: <43 (very low), 43 to 82 (low), 83 to 115 (medium), 116 to 169 (high), and >169 (very high). LOS and cost decreased significantly (P < 0.05) in progressively higher volume categories for both surgeons and hospitals. For LOS, medium-volume surgeons handle the largest proportion of laminectomies (36%), whereas very high-volume hospitals handle the largest proportion (48%). CONCLUSION: This study supports a direct volume-value relationship for surgeons and hospitals in the setting of laminectomy. These findings provide target-estimated thresholds for which hospitals and surgeons may receive meaningful return on investment in our increasingly value-based system. Further value-based optimization is possible in the finding that while the highest volume hospitals handle the largest proportion of laminectomies, the highest volume surgeons do not. LEVEL OF EVIDENCE: 3.

Stratum-Specific Likelihood Ratio Analysis: An Evidence-Based and Pragmatic Approach to Meaningful Thresholds in Lower Extremity Arthroplasty.

BACKGROUND: With the transition toward a value-based care delivery model, an evidence-based approach to quantify the effect of procedural volume on outcomes and cost presents an opportunity to understand and optimize the delivery of lower extremity arthroplasty. Stratum-specific likelihood ratio (SSLR) analysis has been recently applied to define benchmarks which confer a significant advantage in value at the hospital or surgeon level. MATERIALS AND METHODS: In this report, the role, statistical technique, and future applications of SSLR analysis are described with an example outlined for total hip arthroplasty (THA). RESULTS: SSLR analysis provides multiple significant value-based thresholds, providing an advantage over previous methods used to describe the effects of surgeon and hospital volume. These benchmarks have been developed for THA, total knee arthroplasty (TKA), hip fracture, and several other orthopaedic procedures. Current SSLR analyses are limited by the databases employed, and the study of a national database may provide more generalizable benchmarks, which may be applied by hospitals and orthopaedic residencies to define minimum competency thresholds. CONCLUSION: The use of SSLR analysis provides a pragmatic, data-driven approach to understanding and communicating the volume-value relationship in orthopaedic surgery, particularly lower-extremity arthroplasty.

Short-Term Outcomes of Concussions in Major League Baseball: A Historical Cohort Study of Return to Play, Performance, Longevity, and Financial Impact.

BACKGROUND: The short-term outcomes of concussions within Major League Baseball (MLB) warrant further consideration beyond a medical standpoint given that performance, career, and financial data remain unknown. The perception of this injury directly affects decision making from the perspective of both player and franchise. PURPOSE: To evaluate the effect of concussion on MLB players by (1) establishing return-to-play (RTP) time after concussion; (2) comparing the career length and performance of players with concussion versus those who took nonmedical leave; and (3) analyzing player financial impact after concussion. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: Contracts, transactions, injury reports, and performance statistics from 2005 to 2017 were analyzed by comparing matched players who sustained a concussion versus those who took nonmedical leave. Of the 4186 eligible MLB players, 145 sustained concussions resulting in the activation of concussion protocol and 538 took nonmedical leave. RTP time was recorded. Career length was analyzed in reference to an experience-based stratification of full seasons remaining after the concussion. Changes in player performance and salary before and after concussion were compared with the same parameters for players who took nonmedical leave. RESULTS: The mean RTP time was 26 days (95% CI, 20-32 days) for athletes with concussion and 8 days (95% CI, 6-10 days) for those who took nonmedical leave. Athletes with concussion had a mean of 2.8 full seasons remaining, whereas athletes who took nonmedical leave had 3.1 seasons remaining (P = .493). The probability of playing in the MLB after concussion compared with the nonmedical leave pool was not significantly lower (P = .534, log-rank test; hazard ratio, 1.108). Postconcussion performance decreased significantly in position players, including a lower batting average and decreased on-base percentage in the players with concussion compared with those returning from nonmedical leave. Players who sustained a concussion lost a mean of US$654,990 annually compared with players who took nonmedical leave. CONCLUSION: This study of the short-term outcomes after concussion in limited-contact MLB athletes demonstrates that concussions may not decrease career spans but may result in decreased performance in addition to financial loss when compared with matched controls who took nonmedical leave. In sports such as baseball that are not subject to repetitive head trauma, career spans may not decrease after a single concussive event. However, sentinel concussions have deleterious short-term effects on performance and compensation among MLB players.

Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model.

BACKGROUND: Value-based and patient-specific care represent 2 critical areas of focus that have yet to be fully reconciled by today's bundled care model. Using a predictive naïve Bayesian model, the objectives of this study were (1) to develop a machine-learning algorithm using preoperative big data to predict length of stay (LOS) and inpatient costs after primary total knee arthroplasty (TKA) and (2) to propose a tiered patient-specific payment model that reflects patient complexity for reimbursement. METHODS: Using 141,446 patients undergoing primary TKA from an administrative database from 2009 to 2016, a Bayesian model was created and trained to forecast LOS and cost. Algorithm performance was determined using the area under the receiver operating characteristic curve and the percent accuracy. A proposed risk-based patient-specific payment model was derived based on outputs. RESULTS: The machine-learning algorithm required age, race, gender, and comorbidity scores ("risk of illness" and "risk of morbidity") to demonstrate a high degree of validity with an area under the receiver operating characteristic curve of 0.7822 and 0.7382 for LOS and cost. As patient complexity increased, cost add-ons increased in tiers of 3%, 10%, and 15% for moderate, major, and extreme mortality risks, respectively. CONCLUSION: Our machine-learning algorithm derived from an administrative database demonstrated excellent validity in predicting LOS and costs before primary TKA and has broad value-based applications, including a risk-based patient-specific payment model.

Fixed and Variable Relationship Models to Define the Volume-Value Relationship in Spinal Fusion Surgery: A Macroeconomic Analysis Using Evidence-Based Thresholds.

OBJECTIVE: Increased surgical volume has been associated with improved patient outcomes at the surgeon and hospital level. To date, clinically meaningful stratified volume benchmarks have yet to be defined for surgeons or hospitals in the context of spinal fusion surgery. The objective of this study was to establish evidence-based thresholds using outcomes and cost to stratify surgeons and hospitals performing spinal fusion surgery by volume. METHODS: Using 155,788 patients undergoing spinal fusion surgery, we created and applied 4 models using stratum-specific likelihood ratio (SSLR) analysis of a receiver operating characteristic (ROC) curve. This statistical approach was used to generate 4 sets of volume thresholds predictive of increased length of stay (LOS) and increased cost for surgeons and hospitals. RESULTS: SSLR analysis of the 2 ROC curves by annual surgical volume produced 3 or 4 distinct volume categories. Analysis of LOS by annual surgeon spinal fusion volume produced 4 strata: low, medium, high, and very high. Analysis of LOS by annual hospital spinal fusion volume produced 3 strata: low, medium, and high. No relationship between volume and cost could be clearly defined based on the generation of ROC curves for surgeons or hospitals offering spinal fusion. CONCLUSION: This study used evidence-based thresholds to identify a direct, variable relationship model between volume and outcomes of spinal fusion surgery, using LOS as a surrogate, for both surgeons and hospitals. A fixed relationship model was identified between surgeon and hospital volume and cost, as no statistically meaningful relationship could be established.

The Orthopaedic Surgery Residency Application Process: An Analysis of the Applicant Experience.

INTRODUCTION: Orthopaedic surgery residency positions are highly sought after. The purpose of this survey study was to report the following components of the applicant experience: (1) the number of programs to which applicants applied and interviewed, (2) the performance criteria associated with receiving interviews, (3) the way applicants respond to e-mail interview offers, (4) the pre- and post-interview communication between applicants and programs, (5) the importance of interview day activities and the determinants of the applicant rank order list (ROL), and (6) the financial cost of the application process. METHODS: An online survey was administered and entirely completed by a representative sample of 100 orthopaedic surgery residency applicants for the 2015 to 2016 cycle during the 3-week period between the last interview of the application season and the deadline for ROL certification. The survey included 45 questions: 7 for background, 7 for competitiveness, 15 for the interaction between applicants and programs, 15 for the importance of interview day experience and the determinants of the applicant ROL, and 1 for the cost of attending each interview. RESULTS: Students applied to 83 ± 27 programs, received 17 ± 10 interviews, and attended 12 ± 5 interviews. Interview offers correlated with, in descending order, Alpha Omega Alpha status, Step 2 Clinical Knowledge, and Step 1. The mean time to reply of interview offer was 17 minutes, yet 25% of the applicants lost at least one interview despite having at least one other person monitor the applicant's e-mail account. Applicants and programs frequently contacted each other to express interest. Although evaluating current residents was the most valuable aspect of interview day to applicants, the strongest determinants for applicants' ROLs were location and surgical experience, with research the least important factor. The cost of interview season was >$7,000 per applicant, excluding away externships. CONCLUSION: Applying to orthopaedic surgery residency is a complex, competitive, and costly experience for applicants. The application process may benefit from better expectation management of applicant candidacy and a more prohibitive communication policy between applicants and programs after the interview day.

Evidence-Based Thresholds for the Volume and Cost Relationship in Total Hip Arthroplasty: Outcomes and Economies of Scale.

BACKGROUND: High-volume surgeons and hospital systems have been shown to deliver higher-value care in several studies. However, no evidence-based volume thresholds for cost currently exist in total hip arthroplasty (THA). The objective of this study was to establish meaningful thresholds in cost for surgeons and hospitals performing THA. A secondary objective was to analyze the market share of THAs for each surgeon and hospital stratifications. METHODS: Using a database of 136,501 patients undergoing THA, we used stratum-specific likelihood ratio analysis of a receiver operating characteristic curve to generate volume thresholds based on costs for surgeons and hospitals. In addition, we examined the relative proportion of annual THA cases performed by each surgeon and hospital stratifications. RESULTS: Stratum-specific likelihood ratio analysis of cost by annual surgeon THA volume produced stratifications at: 0-73 (low), 74-123 (medium), and 124 or more (high). Analysis by annual hospital THA volume produced stratifications at: 0-121 (low), 122-309 (medium), and 310 or more (high). Hospital costs decreased significantly (P < .05) in progressively higher volume stratifications. High-volume centers perform the largest proportion of THA cases (48.6%); however, low volume surgeons perform the greatest share of these cases (44.6%). CONCLUSION: Our study establishes economies of scale in THA by demonstrating a direct relationship between volume and cost reduction. High-volume hospitals are performing the greatest proportion of THAs; however, low-volume surgeons perform the largest share of these cases, which highlights a potential area for enhanced value in the care of patients undergoing THA.

Short-Term Impact of Concussion in the NHL: An Analysis of Player Longevity, Performance, and Financial Loss.

Many studies have focused on the long-term impact of concussions in professional sports, but few have investigated short-term effects. This study examines concussion effects on individual players in the National Hockey League (NHL) by assessing career length, performance, and salary. Contracts, transactions, injury reports, and performance statistics from 2008-17 were obtained from the official NHL online publication. Players who sustained a concussion were compared with the 2008-17 non-concussed player pool. Career length was analyzed using Kaplan-Meier survival curves and stratification of player age, experience, and longevity. Player performance and salary changes were evaluated between the years before versus after concussion. Performance and salary changes were compared against non-concussed NHL athletes before/after their career midpoints. Of the 2194 eligible NHL players in the 9-year period, 309 sustained 399 concussions resulting in injury protocol. The probability of playing a full NHL season post-concussion was significantly decreased compared with the non-concussed pool (p < 0.05), specifically 65.0% versus 81.2% at 1 year into a player's career, 49.8% versus 67.4% at 2 years, and 14.6% versus 43.7% at 5 years. Performance was reduced at all non-goalie positions post-concussion (p < 0.05). Players scored 2.5 points/year less following a concussion. The total annualized financial impact from salary reductions after 1 concussion was $57.0 million, with a decrease of $292,000 per year in contract value per athlete. This retrospective study demonstrates that NHL concussions resulting in injury protocol activation lead to shorter career lengths, earnings reductions, and decreased performance when compared with non-concussed controls.

Evidence-Based Thresholds for the Volume and Length of Stay Relationship in Total Hip Arthroplasty: Outcomes and Economies of Scale.

BACKGROUND: Several studies have indicated that high-volume surgeons and hospitals deliver higher value care. However, no evidence-based volume thresholds currently exist in total hip arthroplasty (THA). The primary objective of this study was to establish meaningful thresholds taking patient outcomes into consideration for surgeons and hospitals performing THA. A secondary objective was to examine the market share of THAs for each surgeon and hospital strata. METHODS: Using 136,501 patients undergoing hip arthroplasty, we used stratum-specific likelihood ratio (SSLR) analysis of a receiver-operating characteristic curve to generate volume thresholds predictive of increased length of stay (LOS) for surgeons and hospitals. Additionally, we examined the relative proportion of annual THA cases performed by each surgeon and hospital strata established. RESULTS: SSLR analysis of LOS by annual surgeon THA volume produced 3 strata: 0-69 (low), 70-121 (medium), and 121 or more (high). Analysis by annual hospital THA volume produced strata at: 0-120 (low), 121-357 (medium), and 358 or more (high). LOS decreased significantly (P < .05) in progressively higher volume categories. High-volume hospitals performed the majority of cases, whereas low-volume surgeons performed the majority of THAs. CONCLUSION: Our study validates economies of scale in THA by demonstrating a direct relationship between volume and value for THA through risk-based volume stratification of surgeons and hospitals using SSLR analysis of receiver-operating characteristic curves to identify low-, medium-, and high-volume surgeons and hospitals. While the majority of primary THAs are performed at high-volume centers, low-volume surgeons are performing the majority of these cases, which may offer room for improvement in delivering value-based care.