Anterior approach total hip arthroplasty (AA-THA) has a steep learning curve, with higher complication rates in initial cases. Proper surgical case selection during the learning curve can reduce early risk. This study aims to identify patient and radiographic factors associated with AA-THA difficulty using Machine Learning (ML).

Consecutive primary AA-THA patients from two centres, operated by two expert surgeons, were enrolled (excluding patients with prior hip surgery and first 100 cases per surgeon). K- means prototype clustering – an unsupervised ML algorithm – was used with two variables - operative duration and surgical complications within 6 weeks - to cluster operations into difficult or standard groups.

Radiographic measurements (neck shaft angle, offset, LCEA, inter-teardrop distance, Tonnis grade) were measured by two independent observers. These factors, alongside patient factors (BMI, age, sex, laterality) were employed in a multivariate logistic regression analysis and used for k-means clustering. Significant continuous variables were investigated for predictive accuracy using Receiver Operator Characteristics (ROC).

Out of 328 THAs analyzed, 130 (40%) were classified as difficult and 198 (60%) as standard. Difficult group had a mean operative time of 106mins (range 99–116) with 2 complications, while standard group had a mean operative time of 77mins (range 69–86) with 0 complications. Decreasing inter-teardrop distance (odds ratio [OR] 0.97, 95% confidence interval [CI] 0.95–0.99, p = 0.03) and right-sided operations (OR 1.73, 95% CI 1.10–2.72, p = 0.02) were associated with operative difficulty. However, ROC analysis showed poor predictive accuracy for these factors alone, with area under the curve of 0.56. Inter-observer reliability was reported as excellent (ICC >0.7).

Right-sided hips (for right-hand dominant surgeons) and decreasing inter-teardrop distance were associated with case difficulty in AA-THA. These data could guide case selection during the learning phase. A larger dataset with more complications may reveal further factors.

Total knee and hip arthroplasty (TKA and THA) are the most commonly performed surgical procedures, the costs of which constitute a significant healthcare burden. Improving access to care for THA/TKA requires better efficiency. It is hypothesized that this may be possible through a two-stage approach that utilizes prediction of surgical time to enable optimization of operating room (OR) schedules.

Data from 499,432 elective unilateral arthroplasty procedures, including 302,490 TKAs, and 196,942 THAs, performed from 2014-2019 was extracted from the American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database. A deep multilayer perceptron model was trained to predict duration of surgery (DOS) based on pre-operative clinical and biochemical patient factors. A two-stage approach, utilizing predicted DOS from a held out “test” dataset, was utilized to inform the daily OR schedule. The objective function of the optimization was the total OR utilization, with a penalty for overtime. The scheduling problem and constraints were simulated based on a high-volume elective arthroplasty centre in Canada. This approach was compared to current patient scheduling based on mean procedure DOS. Approaches were compared by performing 1000 simulated OR schedules.

The predict then optimize approach achieved an 18% increase in OR utilization over the mean regressor. The two-stage approach reduced overtime by 25-minutes per OR day, however it created a 7-minute increase in underutilization. Better objective value was seen in 85.1% of the simulations.

With deep learning prediction and mathematical optimization of patient scheduling it is possible to improve overall OR utilization compared to typical scheduling practices. Maximizing utilization of existing healthcare resources can, in limited resource environments, improve patient's access to arthritis care by increasing patient throughput, reducing surgical wait times and in the immediate future, help clear the backlog associated with the COVID-19 pandemic.

Knee pain is common, representing a significant socioeconomic burden. Caused by a variety of pathologies, its evaluation in primary-care is challenging. Subsequently, an over-reliance on magnetic resonance imaging (MRI) exists. Prior to orthopaedic surgeon referral, many patients receive no, or incorrect, imaging. Electronic-triage (e-triage) tools represent an innovative solution to address this problem. The primary aim of this study was to ascertain whether an e-triage tool is capable of outperforming existing clinical pathways to determine the correct pre-hospital imaging based on knee pain diagnosis.

Patients ≥18 years with a new presentation of knee pain were retrospectively identified. The timing and appropriateness of imaging was assessed. A symptom-based e-triage tool was developed, using the Amazon LEXbotplatform, and piloted to predict five common knee pathologies and suggest appropriate imaging.

1462 patients were identified. 17% of arthroplasty patients received an ‘unnecessary MRI’, whilst 28% of arthroscopy patients did not have a ‘necessary MRI’, thus requiring a follow-up appointment, with a mean delay of three months (SD 2.6, range 0.2-20.2). Using NHS tariffs, a wasted cost through unnecessary/necessary MRIs and subsequent follow-up appointments was estimated at £45,816. The e-triage pilot was trialled with 41 patients (mean age:58.4 years, 58.5% female). Preliminary diagnoses were available for 34 patients. Using the highest proportion of reported symptoms in the corresponding group, the e-triage tool correctly identified three of the four knee pathologies. The e-triage tool did not correctly identify anterior cruciate ligament injuries (n=3). 79.2% of participants would use the tool again.

A significant number of knee pathology patients received incorrect imaging prior to their initial hospital appointment, incurring delays and unnecessary costs. A symptom-based e-triage tool was developed, with promising pilot data and user feedback. With refinement, this tool has the potential to improve wait-times and referral quality, whilst reducing costs.

Immigrated Canadians make up approximately 20% of the total population in Canada, and 30% of the population in Ontario. Despite universal health coverage and an equal prevalence of severe arthritis in immigrants relative to non-immigrants, the former may be underrepresented amongst arthroplasty recipients secondary to challenges navigating the healthcare system. The primary aim of this study was to determine if utilization of arthroplasty differs between immigrant populations and persons born in Canada. The secondary aim was to determine differences in outcomes following total hip and knee arthroplasty (THA and TKA, respectively).

This is a retrospective population-based cohort study using health administrative databases. All patients aged ≥18 in Ontario who underwent their first primary elective THA or TKA between 2002 and 2016 were identified. Immigration status for each patient was identified via linkage to the ‘Immigration, Refugee and Citizenship Canada’ database. Outcomes included all-cause and septic revision surgery within 12-months, dislocation (for THA) and total post-operative case cost and were compared between groups. Cochrane-Armitage Test for Trend was utilized to determine if the uptake of arthroplasty by immigrants changed over time.

There was a total of 186,528 TKA recipients and 116,472 THA recipients identified over the study period. Of these, 10,193 (5.5%) and 3,165 (2.7%) were immigrants, respectively. The largest proportion of immigrants were from the Asia and Pacific region for those undergoing TKA (54.0%) and Europe for THA recipients (53.4%). There was no difference in the rate of all-cause revision or septic revision at 12 months between groups undergoing TKA (p=0.864, p=0.585) or THA (p=0.527, p=0.397), respectively. There was also no difference in the rate of dislocations between immigrants and people born in Canada (p=0.765, respectively).

Despite having similar complication rates and costs, immigrants represent a significantly smaller proportion of joint replacement recipients than they represent in the general population in Ontario. These results suggest significant underutilization of surgical management for arthritis among Canada's immigrant populations. Initiatives to improve access to total joint arthroplasty are warranted.

Demand for total knee arthroplasty (TKA) is increasing as it remains the gold-standard treatment for end-stage osteoarthritis (OA) of the knee. While magnetic-resonance imaging (MRI) scans of the knee are not indicated for diagnosing knee OA, they are commonly ordered prior to the referral to an orthopaedic surgeon. The purpose of this study was to determine the proportion of patients who underwent an MRI in the two years prior to their primary TKA for OA. Secondary outcomes included determining patient and physician associations with increased MRI usage.

This is a population-based cohort study using billing codes in Ontario, Canada. All patients over 40 years-old who underwent a primary TKA between April 1, 2008 and March 31, 2017 were included. Statistical analyses were performed using SAS and included the Cochran-Armitage test for trend of MRI prior to surgery, and predictive multivariable regression model. Significance was set to p<0.05.

There were 172,689 eligible first-time TKA recipients, of which 34,140 (19.8%) received an MRI in the two years prior to their surgery. The majority of these (70.8%) were ordered by primary care physicians, followed by orthopaedic surgeons (22.5%). Patients who received an MRI were younger and had fewer comorbidities than patients who did not (p<0.001). MRI use prior to TKA increased from 15.9% in 2008 to 20.1% in 2017 (p<0.0001).

Despite MRIs rarely being indicated for the work-up of knee OA, nearly one in five patients have an MRI in the two years prior to their TKA. Reducing the use of this prior to TKA may help reduce wait-times for surgery.

Knee arthroscopy with meniscectomy is the third most common Orthopaedic surgery performed after TKA and THA, comprising up to 16.6% of all procedures. The efficiency of Orthopaedic care delivery with respect to waiting times and systemic costs is extremely concerning. Canadian Orthopaedic patients experience the longest wait times of any G7 country, yet perioperative surgical care constitutes a significant portion of a hospital's budget.

In-Office Needle Arthroscopy (IONA) is an emerging technology that has been primarily studied as a diagnostic tool. Recent evidence shows that it is a cost-effective alternative to hospital- and community-based MRI with comparable accuracy. Recent procedure guides detailing IONA medial meniscectomy suggest a potential node for OR diversion. Given the high case volume of knee arthroscopy as well as the potential amenability to be diverted away from the OR to the office setting, IONA has the potential to generate considerable improvements in healthcare system efficiency with respect to throughput and cost savings. As such, the purpose of this study is to investigate the cost savings and impact on waiting times on a mid-sized Canadian community hospital if IONA is offered as an alternative to traditional operating room (OR) arthroscopy for medial meniscal tears.

In order to develop a comprehensive understanding and accurate representation of the quantifiable operations involved in the current state for medial meniscus tear care, process mapping was performed that describes the journey of a patient from when they present with knee pain to their general practitioner until case resolution. This technique was then repeated to create a second process map describing the hypothetical proposed state whereby OR diversion may be conducted utilizing IONA. Once the respective process maps for each state were determined, each process map was translated into a Dupont decision tree. In order to accurately determine the total number of patients which would be eligible for this care pathway at our institution, the OR booking scheduling for arthroscopy and meniscectomy/repair over a four year time period (2016-2020) were reviewed. A sensitivity analysis was performed to examine the effect of the number of patients who select IONA over meniscectomy and the number of revision meniscectomies after IONA on 1) the profit and profit margin determined by the MCS-Dupont financial model and 2) the throughput (percentage and number) determined by the MCS-throughput model.

Based on historic data at our institution, an average of 198 patients (SD 31) underwent either a meniscectomy or repair from years 2016-2020. Revenue for both states was similar (p = .22), with the current state revenue being $ 248,555.99 (standard deviation $ 39,005.43) and proposed state of $ 249,223.86 (SD $ 39,188.73). However, the reduction in expenses was significant (p < .0001) at 5.15%, with expenses in the current state being $ 281,415.23 (SD $ 44,157.80) and proposed state of $ 266,912.68 (SD $ 42,093.19), representing $14,502.95 in savings. Accordingly, profit improvement was also significant (p < .0001) at 46.2%, with current state profit being $ (32,859.24) (SD $ 5,153.49) and proposed state being $ (17,678.82) (SD $ 2,921.28). The addition of IONA into the care pathway of the proposed state produced an average improvement in throughput of 42 patients (SD 7), representing a 21.2% reduction in the number of patients that require an OR procedure. Financial sensitivity analysis revealed that the proposed state profit was higher than the current state profit if as few as 10% of patients select IONA, with the maximum revision rate needing to remain below 40% to achieve improved profits.

The most important finding from this study is that IONA is a cost-effective alternative to traditional surgical arthroscopy for medial meniscus meniscectomy. Importantly, IONA can also be used as a diagnostic procedure. It is shown to be a cost-effective alternative to MRI with similar diagnostic accuracy. The role of IONA as a joint diagnostic-therapeutic tool could positively impact MRI waiting times and MRI/MRA costs, and further reduce indirect costs to society. Given the well-established benefit of early meniscus treatment, accelerating both diagnosis and therapy is bound to result in positive effects.

Novel immersive virtual reality (IVR) technologies are revolutionizing medical education. Virtual anatomy education using head-mounted displays allows users to interact with virtual anatomical objects, move within the virtual rooms, and interact with other virtual users. While IVR has been shown to be more effective than textbook learning and 3D computer models presented in 2D screens, the effectiveness of IVR compared to cadaveric models in anatomy education is currently unknown. In this study, we aim to compare the effectiveness of IVR with direct cadaveric bone models in teaching upper and lower limb anatomy for first-year medical students.

A randomized, double-blind crossover non-inferiority trial was conducted. Participants were first-year medical students from a single University. Exclusion criteria included students who undertook prior undergraduate or graduate degrees in anatomy. In the first stage of the study, students were randomized in a 1:1 ratio to IVR or cadaveric bone groups studying upper limb skeletal anatomy. All students were then crossed over and used cadaveric bone or IVR to study lower limb skeletal anatomy. All students in both groups completed a pre-and post-intervention knowledge test. The educational content was based on the University of Toronto Medical Anatomy Curriculum. The Oculus Quest 2 Headsets (Meta Technologies) and PrecisionOS Anatomy application (PrecisionOS Technology) were utilized for the virtual reality component. The primary endpoint of the study was student performance on the pre-and post-intervention knowledge tests. We hypothesized that student performance in the IVR groups would be comparable to the cadaveric bone group.

50 first-year medical students met inclusion criteria and were computer randomized (1:1 ratio) to IVR and cadaveric bone group for upper limb skeletal anatomy education. Forty-six students attended the study, 21 completed the upper limb modules, and 19 completed the lower limb modules. Among all students, average score on the pre-intervention knowledge test was 14.6% (Standard Deviation (SD)=18.2%) and 25.0% (SD=17%) for upper and lower limbs, respectively. Percentage increase in students’ scores between pre-and post-intervention knowledge test, in the upper limb for IVR, was 15 % and 16.7% for cadaveric bones (p = 0. 2861), and for the lower limb score increase was 22.6% in the IVR and 22.5% in the cadaveric bone group (p = 0.9356).

In this non-inferiority crossover randomized controlled trial, we found no significant difference between student performance in knowledge tests after using IVR or cadaveric bones. Immersive virtual reality and cadaveric bones were equally effective in skeletal anatomy education. Going forward, with advances in VR technologies and anatomy applications, we can expect to see further improvements in the effectiveness of these technologies in anatomy and surgical education. These findings have implications for medical schools having challenges in acquiring cadavers and cadaveric parts.

It has been established that a dedicated orthopaedic trauma room (DOTR) provides significant clinical and organizational benefits to the management of trauma patients. After-hours care is associated with surgeon fatigue, a high risk of patient complications, and increased costs related to staffing. However, hesitation due to concerns of the associated opportunity cost at the hospital leadership level is a major barrier to wide-spread adoption. The primary aim of this study is to determine the impact of dedicated orthopaedic trauma room (DOTR) implementation on operating room efficiency. Secondly, we sought to evaluate the associated financial impact of the DOTR, with respect to both after-hours care costs as well as the opportunity cost of displaced elective cases.

This was a retrospective cost-analysis study performed at a single academic-affiliated community hospital in Toronto, Canada. All patients that underwent the most frequently performed orthopedic trauma procedures (hip hemiarthroplasty, open reduction internal fixation of the ankle, femur, elbow and distal radius), over a four-year period from 2016-2019 were included. Patient data acquired for two-years prior and two-years after the implementation of a DOTR were compared, adjusting for the number of cases performed. Surgical duration and number of day-time and after-hours cases was recorded pre- and post-implementation. Cost savings of performing trauma cases during daytime and the opportunity cost of displacing elective cases by performing cases during the day was calculated. A sensitivity analysis accounting for varying overtime costs and hospital elective case profit was also performed.

1960 orthopaedic cases were examined pre- and post-DOTR. All procedures had reduced total operative time post-DOTR. After accounting for the total number of each procedure performed, the mean weighted reduction was 31.4% and the mean time saved was 29.6 minutes per surgery. The number of daytime surgical hours increased 21%, while nighttime hours decreased by 37.8%. Overtime staffing costs were reduced by $24,976 alongside increase in opportunity costs of $22,500. This resulted in a net profit of $2,476.

Our results support the premise that DOTRs improve operating room efficiency and can be cost efficient. Through the regular scheduling of a DOTR at a single hospital in Canada, the number of surgeries occurring during daytime hours increased while the number of after-hours cases decreased. The same surgeries were also completed nearly one-third faster (30 minutes per case) on average. Our study also specifically addresses the hesitation regarding potential loss of profit from elective surgeries. Notably, the savings partially stem from decreased OR time as well as decreased nurse overtime. Widespread implementation can improve patient care while still remaining financially favourable.

Single level discectomy (SLD) is one of the most commonly performed spinal surgery procedures. Two key drivers of their cost-of-care are duration of surgery (DOS) and postoperative length of stay (LOS). Therefore, the ability to preoperatively predict SLD DOS and LOS has substantial implications for both hospital and healthcare system finances, scheduling and resource allocation. As such, the goal of this study was to predict DOS and LOS for SLD using machine learning models (MLMs) constructed on preoperative factors using a large North American database.

The American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database was queried for SLD procedures from 2014-2019. The dataset was split in a 60/20/20 ratio of training/validation/testing based on year. Various MLMs (traditional regression models, tree-based models, and multilayer perceptron neural networks) were used and evaluated according to 1) mean squared error (MSE), 2) buffer accuracy (the number of times the predicted target was within a predesignated buffer), and 3) classification accuracy (the number of times the correct class was predicted by the models). To ensure real world applicability, the results of the models were compared to a mean regressor model.

A total of 11,525 patients were included in this study. During validation, the neural network model (NNM) had the best MSEs for DOS (0.99) and LOS (0.67). During testing, the NNM had the best MSEs for DOS (0.89) and LOS (0.65). The NNM yielded the best 30-minute buffer accuracy for DOS (70.9%) and ≤120 min, >120 min classification accuracy (86.8%). The NNM had the best 1-day buffer accuracy for LOS (84.5%) and ≤2 days, >2 days classification accuracy (94.6%). All models were more accurate than the mean regressors for both DOS and LOS predictions.

We successfully demonstrated that MLMs can be used to accurately predict the DOS and LOS of SLD based on preoperative factors. This big-data application has significant practical implications with respect to surgical scheduling and inpatient bedflow, as well as major implications for both private and publicly funded healthcare systems. Incorporating this artificial intelligence technique in real-time hospital operations would be enhanced by including institution-specific operational factors such as surgical team and operating room workflow.

Total knee and hip arthroplasty (TKA and THA) are two of the highest volume and resource intensive surgical procedures. Key drivers of the cost of surgical care are duration of surgery (DOS) and postoperative inpatient length of stay (LOS). The ability to predict TKA and THA DOS and LOS has substantial implications for hospital finances, scheduling and resource allocation. The goal of this study was to predict DOS and LOS for elective unilateral TKAs and THAs using machine learning models (MLMs) constructed on preoperative patient factors using a large North American database.

The American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database was queried for elective unilateral TKA and THA procedures from 2014-2019. The dataset was split into training, validation and testing based on year. Multiple conventional and deep MLMs such as linear, tree-based and multilayer perceptrons (MLPs) were constructed. The models with best performance on the validation set were evaluated on the testing set. Models were evaluated according to 1) mean squared error (MSE), 2) buffer accuracy (the number of times the predicted target was within a predesignated buffer of the actual target), and 3) classification accuracy (the number of times the correct class was predicted by the models). To ensure useful predictions, the results of the models were compared to a mean regressor.

A total of 499,432 patients (TKA 302,490; THA 196,942) were included. The MLP models had the best MSEs and accuracy across both TKA and THA patients. During testing, the TKA MSEs for DOS and LOS were 0.893 and 0.688 while the THA MSEs for DOS and LOS were 0.895 and 0.691. The TKA DOS 30-minute buffer accuracy and ≤120 min, >120 min classification accuracy were 78.8% and 88.3%, while the TKA LOS 1-day buffer accuracy and ≤2 days, >2 days classification accuracy were 75.2% and 76.1%. The THA DOS 30-minute buffer accuracy and ≤120 min, >120 min classification accuracy were 81.6% and 91.4%, while the THA LOS 1-day buffer accuracy and ≤2 days, >2 days classification accuracy were 78.3% and 80.4%. All models across both TKA and THA patients were more accurate than the mean regressors for both DOS and LOS predictions across both buffer and classification accuracies.

Conventional and deep MLMs have been effectively implemented to predict the DOS and LOS of elective unilateral TKA and THA patients based on preoperative patient factors using a large North American database with a high level of accuracy. Future work should include using operational factors to further refine these models and improve predictive accuracy. Results of this work will allow institutions to optimize their resource allocation, reduce costs and improve surgical scheduling.

Novel immersive virtual reality (IVR) technologies are revolutionizing medical education. Virtual anatomy education using head-mounted displays allows users to interact with virtual anatomical objects, move within the virtual rooms, and interact with other virtual users. While IVR has been shown to be more effective than textbook learning and 3D computer models presented in 2D screens, the effectiveness of IVR compared to cadaveric models in anatomy education is currently unknown. In this study, we aim to compare the effectiveness of IVR with direct cadaveric bone models in teaching upper and lower limb anatomy for first-year medical students.

A randomized, double-blind crossover non-inferiority trial was conducted. Participants were first-year medical students from a single University. Exclusion criteria included students who undertook prior undergraduate or graduate degrees in anatomy. In the first stage of the study, students were randomized in a 1:1 ratio to IVR or cadaveric bone groups studying upper limb skeletal anatomy. All students were then crossed over and used cadaveric bone or IVR to study lower limb skeletal anatomy. All students in both groups completed a pre-and post-intervention knowledge test. The educational content was based on the University of Toronto Medical Anatomy Curriculum. The Oculus Quest 2 Headsets (Meta Technologies) and PrecisionOS Anatomy application (PrecisionOS Technology) were utilized for the virtual reality component. The primary endpoint of the study was student performance on the pre-and post-intervention knowledge tests. We hypothesized that student performance in the IVR groups would be comparable to the cadaveric bone group.

50 first-year medical students met inclusion criteria and were computer randomized (1:1 ratio) to IVR and cadaveric bone group for upper limb skeletal anatomy education. Forty-six students attended the study, 21 completed the upper limb modules, and 19 completed the lower limb modules. Among all students, average score on the pre-intervention knowledge test was 14.6% (Standard Deviation (SD)=18.2%) and 25.0% (SD=17%) for upper and lower limbs, respectively. Percentage increase in students’ scores between pre-and post-intervention knowledge test, in the upper limb for IVR, was 15 % and 16.7% for cadaveric bones (p = 0. 2861), and for the lower limb score increase was 22.6% in the IVR and 22.5% in the cadaveric bone group (p = 0.9356).

In this non-inferiority crossover randomized controlled trial, we found no significant difference between student performance in knowledge tests after using IVR or cadaveric bones. Immersive virtual reality and cadaveric bones were equally effective in skeletal anatomy education. Going forward, with advances in VR technologies and anatomy applications, we can expect to see further improvements in the effectiveness of these technologies in anatomy and surgical education. These findings have implications for medical schools having challenges in acquiring cadavers and cadaveric parts.