Preoperative Magnetic Resonance Imaging Offers Questionable Clinical Utility, Delays Time to Hip Arthroscopy, and Lacks Cost-Effectiveness in Patients Aged ≤40 Years With Femoroacetabular Impingement Syndrome: A Retrospective 5-Year Analysis.

PURPOSE: To assess the clinical utility of preoperative magnetic resonance imaging (MRI) and quantify the delay in surgical care for patients aged ≤40 years undergoing primary hip arthroscopy with history, physical examination, and radiographs concordant with femoroacetabular impingement syndrome (FAIS). METHODS: From August 2015 to December 2020, 1,786 consecutive patients were reviewed from the practice of 1 fellowship-trained hip arthroscopist. Inclusion criteria were FAIS, primary surgery, and age ≤40 years. Exclusion criteria were MRI contraindication, reattempt of conservative management, or concomitant periacetabular osteotomy. After nonoperative treatment options were exhausted and a surgical plan was established, patients were stratified by those who presented with versus without MRI. Those without existing MRI received one, and any deviations from the surgical plan were noted. All preoperative MRIs were compared with office evaluation and intraoperative findings to assess agreement. Demographic data, Hip Disability and Osteoarthritis Outcome Score (HOOS)-Pain, and time from office to MRI or arthroscopy were recorded. RESULTS: Of the patients indicated by history, physical examination, and radiographs alone (70% female, body mass index 24.8 kg/m2, age 25.9 years), 198 patients presented without MRI and 934 with MRI. None of the 198 had surgical plans altered after MRI. Patients in both groups had MRI findings demonstrating anterosuperior labral tears that were visualized and repaired intraoperatively. Mean time from office to arthroscopy for patients without MRI versus those with was 107.0 ± 67 and 85.0 ± 53 days, respectively (P < .001). Time to MRI was 22.8 days. No difference between groups was observed among the 85% of patients who surpassed the HOOS-Pain minimal clinically important difference (MCID). CONCLUSION: Once indicated for surgery based on history, physical examination, and radiographs, preoperative MRI did not alter the surgical plan for patients aged ≤40 years with FAIS undergoing primary hip arthroscopy. Moreover, preoperative MRI delayed time to arthroscopy. The necessity of routine preoperative MRI in the young primary FAIS population should be challenged.

Time-Driven Activity-Based Costing Accurately Determines Bundle Cost for Rotator Cuff Repair.

PURPOSE: The purpose of this study was to determine the cost of the episode of care for primary rotator cuff repair (RCR) from day of surgery to 90 days postoperatively using the time-driven activity-based costing (TDABC) method. The secondary purpose of this study was to identify the main drivers of cost for both phases of care. METHODS: This retrospective case series study used the TDABC method to determine the bundled cost of care for an RCR. First, a process map of the RCR episode of care was constructed in order to determine drivers of fixed (i.e., rent, power), direct variable (i.e., healthcare personnel), and indirect costs (i.e., marketing, building maintenance). The study was performed at a Midwestern tertiary care medical system, and patients were included in the study if they underwent an RCR from January 2018 to January 2019 with at least 90 days of postoperative follow-up. In this article, all costs were included, but we did not account for fees to provider and professional groups. RESULTS: The TDABC method calculated a cost of $10,569 for a bundled RCR, with 76% arising from the operative phase and 24% from the postoperative phase. The main driver of cost within the operative phase was the direct fixed costs, which accounted for 35% of the cost in this phase, and the largest contributor to cost within this category was the cost of implants, which accounted for 55%. In the postoperative phase of care, physical therapy visits were the greatest contributor to cost at 59%. CONCLUSION: In a bundled cost of care for RCR, the largest cost driver occurs on the day of surgery for direct fixed costs, in particular, the implant. Physical therapy represents over half of the costs of the episode of care. Better understanding the specific cost of care for RCR will facilitate optimization with appropriately designed payment models and policies that safeguard the interests of the patient, physician, and payer. LEVEL OF EVIDENCE: IV, therapeutic case series.

Time-Driven Activity-based Costing for Anterior Cruciate Ligament Reconstruction: A Comparison to Traditional Accounting Methods.

PURPOSE: The primary purpose of this study was to compare the cost of care of one of the most common sports medicine surgical procedures, anterior cruciate ligament reconstruction (ACLR), using the time-driven activity-based costing (TDABC) method to traditional accounting methods such as activity-based costing (ABC). Our secondary purpose was to identify the main drivers of the cost of ACLR using both of these techniques. METHODS: A process map of ACLR was constructed through direct observation in the clinical setting according to established techniques to identify drivers of fixed, direct variable, and indirect costs. An episode of care consisted of each step in the surgical process from admission to discharge. Personnel costs were combined with the process map to determine the cost drivers and overall cost of the procedure. The cost generated from the TDABC method was compared with the cost from our institution's internal accounting system, which used an ABC method. RESULTS: The total cost of ACLR was $5,242.25 when using TDABC versus $10,318 when using the traditional ABC method. The largest difference between the 2 methods was within the domain of direct variable costs. CONCLUSIONS: When compared with TDABC, the hospital's traditional cost-accounting estimate for ACLR is nearly twice as costly. These findings highlight the variability of cost calculation for the same clinical episode between the 2 accounting methods. For the traditional accounting method, the direct variable cost was the main cost driver, whereas for the TDABC method, the direct fixed cost was the main cost driver. CLINICAL RELEVANCE: This study is important because it elucidates important cost drivers for one of the most common sports medicine orthopaedic surgical procedures and attempts to identify the true overall cost of the procedure.

The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty.

HYPOTHESIS/PURPOSE: The objective is to develop and validate an artificial intelligence model, specifically an artificial neural network (ANN), to predict length of stay (LOS), discharge disposition, and inpatient charges for primary anatomic total (aTSA), reverse total (rTSA), and hemi- (HSA) shoulder arthroplasty to establish internal validity in predicting patient-specific value metrics. METHODS: Using data from the National Inpatient Sample between 2003 and 2014, 4 different ANN models to predict LOS, discharge disposition, and inpatient costs using 39 preoperative variables were developed based on diagnosis and arthroplasty type: primary chronic/degenerative aTSA, primary chronic/degenerative rTSA, primary traumatic/acute rTSA, and primary acute/traumatic HSA. Models were also combined into diagnosis type only. Outcome metrics included accuracy and area under the curve (AUC) for a receiver operating characteristic curve. RESULTS: A total of 111,147 patients undergoing primary shoulder replacement were included. The machine learning algorithm predicting the overall chronic/degenerative conditions model (aTSA, rTSA) achieved accuracies of 76.5%, 91.8%, and 73.1% for total cost, LOS, and disposition, respectively; AUCs were 0.75, 0.89, and 0.77 for total cost, LOS, and disposition, respectively. The overall acute/traumatic conditions model (rTSA, HSA) had accuracies of 70.3%, 79.1%, and 72.0% and AUCs of 0.72, 0.78, and 0.79 for total cost, LOS, and discharge disposition, respectively. CONCLUSION: Our ANN demonstrated fair to good accuracy and reliability for predicting inpatient cost, LOS, and discharge disposition in shoulder arthroplasty for both chronic/degenerative and acute/traumatic conditions. Machine learning has the potential to preoperatively predict costs, LOS, and disposition using patient-specific data for expectation management between health care providers, patients, and payers.

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.

Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Based Care, Mobile Health, and Remote Patient Monitoring.

BACKGROUND: Driven by the recent ubiquity of big data and computing power, we established the Machine Learning Arthroplasty Laboratory (MLAL) to examine and apply artificial intelligence (AI) to musculoskeletal medicine. METHODS: In this review, we discuss the 2 core objectives of the MLAL as they relate to the practice and progress of orthopedic surgery: (1) patient-specific, value-based care and (2) human movement. RESULTS: We developed and validated several machine learning-based models for primary lower extremity arthroplasty that preoperatively predict patient-specific, risk-adjusted value metrics, including cost, length of stay, and discharge disposition, to provide improved expectation management, preoperative planning, and potential financial arbitration. Additionally, we leveraged passive, ubiquitous mobile technologies to build a small data registry of human movement surrounding TKA that permits remote patient monitoring to evaluate therapy compliance, outcomes, opioid intake, mobility, and joint range of motion. CONCLUSION: The rapid rate with which we in arthroplasty are acquiring and storing continuous data, whether passively or actively, demands an advanced processing approach: AI. By carefully studying AI techniques with the MLAL, we have applied this evolving technique as a first step that may directly improve patient outcomes and practice of orthopedics.

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.