Excessive resident duty hours (RDH) are a recognized issue with implications for physician well-being and patient safety. A major component of the RDH concern is on-call duty. While considerable work has been done to reduce resident call workload, there is a paucity of research in optimizing resident call scheduling. Call coverage is scheduled manually rather than demand-based, which generally leads to over-scheduling to prevent a service gap. Machine learning (ML) has been widely applied in other industries to prevent such issues of a supply-demand mismatch. However, the healthcare field has been slow to adopt these innovations. As such, the aim of this study was to use ML models to 1) predict demand on orthopaedic surgery residents at a level I trauma centre and 2) identify variables key to demand prediction.

Daily surgical handover emails over an eight year (2012-2019) period at a level I trauma centre were collected. The following data was used to calculate demand: spine call coverage, date, and number of operating rooms (ORs), traumas, admissions and consults completed. Various ML models (linear, tree-based and neural networks) were trained to predict the workload, with their results compared to the current scheduling approach. Quality of models was determined by using the area under the receiver operator curve (AUC) and accuracy of the predictions. The top ten most important variables were extracted from the most successful model.

During training, the model with the highest AUC and accuracy was the multivariate adaptive regression splines (MARS) model, with an AUC of 0.78±0.03 and accuracy of 71.7%±3.1%. During testing, the model with the highest AUC and accuracy was the neural network model, with an AUC of 0.81 and accuracy of 73.7%. All models were better than the current approach, which had an AUC of 0.50 and accuracy of 50.1%. Key variables used by the neural network model were (descending order): spine call duty, year, weekday/weekend, month, and day of the week.

This was the first study attempting to use ML to predict the service demand on orthopaedic surgery residents at a major level I trauma centre. Multiple ML models were shown to be more appropriate and accurate at predicting the demand on surgical residents as compared to the current scheduling approach. Future work should look to incorporate predictive models with optimization strategies to match scheduling with demand in order to improve resident well being and patient care.

The primary objective is to compare revision rates for lumbar disc replacement (LDR) and fusion at the same or adjacent levels in Ontario, Canada. The secondary objectives include acute complications during hospitalization and in 30 days, and length of hospital stay.

A population-based cohort study was conducted using health administrative databases including patients undergoing LDR or single level fusion between October 2005 to March 2018. Patients receiving LDR or fusion were identified using physician claims recorded in the Ontario Health Insurance Program database. Additional details of surgical procedure were obtained from the Canadian Institute for Health Information hospital discharge abstract. Primary outcome measured was presence of revision surgery in the lumbar spine defined as operation greater than 30 days from index procedure. Secondary outcomes were immediate/ acute complications within the first 30 days of index operation.

A total of 42,024 patients were included. Mean follow up in the LDR and fusion groups were 2943 and 2301 days, respectively. The rates of revision surgery at the same or adjacent levels were 4.7% in the LDR group and 11.1% in the fusion group (P=.003). Multivariate analysis identified risk factors for revision surgery as being female, hypertension, and lower surgeon volume. More patients in the fusion group had dural tears (p<.001), while the LDR group had more “other” complications (p=.037). The LDR group had a longer mean hospital stay (p=.018).

In this study population, the LDR group had lower rates of revision compared to the fusion group. Caution is needed in concluding its significance due to lack of clinical variables and possible differences in indications between LDR and posterior decompression and fusion.

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.

The 2020-2021 Canadian Residency Matching Service (CaRMS) match year was altered on an unprecedented scale. Visiting electives were cancelled at a national level, and the CaRMS interview tour was moved to a virtual model. These changes posed a significant challenge to both prospective students and program directors (PDs), requiring each party to employ alternative strategies to distinguish themselves throughout the match process. For a variety of reasons, including a decline in applicant interest secondary to reduced job prospects, the field of orthopaedic surgery was identified as vulnerable to many of these changes, creating a window of opportunity to evaluate their impacts on students and recruiting residency programs.

This longitudinal survey study was disseminated to match-year medical students (3rd and 4th year) with an interest in orthopaedic surgery, as well as orthopaedic surgery program directors. Responses to the survey were collected using an electronic form designed in Qualtrics (Qualtrics, 2021, Provo, Utah, USA). Students were contacted through social media posts, as well as by snowball sampling methods through appropriate medical student leadership intermediates. The survey was disseminated to all 17 orthopedic surgery program directors in Canada.

A pre-match and post-match iteration of this survey were designed to identify whether expectations differed from reality regarding the effect of the COVID-19 pandemic on the CaRMS match 2020-2021 process. A similar package was disseminated to Canadian orthopaedic surgery program directors pre-match, with an option to opt-in for a post-match survey follow-up. This survey had a focus on program directors’ opinions of various novel communication, recruitment, and assessment strategies, in the wake of the COVID-19 pandemic.

Students’ responses to the loss of visiting electives were negative. Despite a reduction in financial stress associated with reduced need to travel (p=0.001), this was identified as a core component of the clerkship experience. In the case of virtual interviews, students’ initial trepidation pre-CaRMS turned into a positive outlook post-CaRMS (significant improvement, p=0.009) indicating an overall satisfaction with the virtual interview format, despite some concerns about a reduction in their capacity to network. Program directors and selection committee faculty also felt positively about the virtual interview format. Both students and program directors were overwhelmingly positive about virtual events put on by both school programs and student-led initiatives to complement the CaRMS tour.

CaRMS was initially developed to facilitate the matching process for both students and programs alike. We hope to continue this tradition of student-led and student-informed change by providing three evidence-based recommendations. First, visiting electives should not be discontinued in future iterations of CaRMS if at all possible. Second, virtual interviews should be considered as an alternative approach to the CaRMS interview tour moving forward. And third, ongoing virtual events should be associated with a centralized platform from which programs can easily communicate virtual sessions to their target audience.

Simulation is an effective adjunct to the traditional surgical curriculum, though access to these technologies is often limited and costly. The objectives of this work were to develop a freely accessible virtual pedicle screw simulator and to improve the clinical authenticity of the simulator through integration of low-cost motion tracking. The open-source medical imaging and visualisation software, 3D Slicer, was used as the development platform for the virtual simulation. 3D Slicer contains many features for quickly rendering and transforming 3D models of the bony spine anatomy from patient-specific CT scans. A step-wise pedicle screw insertion workflow module was developed which emulated typical pre-operative planning steps. This included taking anatomic measurements, identifying insertion landmarks, and choosing appropriate screw sizes. Monitoring of the surgeon's simulated tool was assessed with a low-cost motion tracking sensor in real-time. This allowed for the surgeon's physical motions to be tracked as they defined the virtual screw's insertion point and trajectory on the rendered anatomy. Screw insertion was evaluated based on bone density contact and cortical breaches. Initial surgeon feedback of the virtual simulator with integrated motion tracking was positive, with no noticeable lag and high accuracy between the real-world and virtual environments. The software yields high fidelity 3D visualisation of the complex geometry and the tracking enabled coordination of motion to small changes in both translational and angular positioning. Future work will evaluate the benefit of this simulation platform with use over the course of resident spine rotations to improve planning and surgical competency.