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.

Conferences centered around surgery suffers from gender disparity with male faculty having a more dominant presence in meetings compared to female faculty. Orthopedic Surgery possibly suffers the most from this problem of all surgical specialties, and is reflective of a gender disparity in the field. The objective of this study was to investigate the prevalence of “manels”, or male-only sessions, in eight major Orthopedic Surgery meetings hosted in 2021 and to quantify the differences in location of practice, academic position, years of practice, and research qualifications between male and female faculty.

Eight Orthopedic conferences organized by major Orthopedic associations (AAOS, COA, OTA, EFORT, AAHKS, ORS, NASS, and AOSSM) from February 2021 to November 2021 were analyzed. Meeting information was retrieved from the conference agendas, and details of chairs and speakers were obtained from Linkedin, Doximity, CPSO, personal websites, and Web of Science. Primary outcomes included: one) percentage of male faculty in all included sessions and two) overall percentage of manels. Secondary outcomes included one) percentage of male speakers and chairs in all included sessions, two) overall percentage of male-chair and male-speaker only sessions. Comparisons for outcomes were made between conferences and session topics (adult reconstruction hip, adult reconstruction knee, practice management/rehabilitation, trauma, sports, general, pediatrics, upper extremity, musculoskeletal oncology, foot and ankle, spine, and miscellaneous). Mean number of sessions for male and female were compared after being stratified into quartiles based on publications, sum of times cited, and H-indexes. Data was analyzed with non-parametric analysis, chi-square tests, or independent samples t-tests using SPSS version 28.0.0.0 with a p-value of < 0 .05 being considered statistically significant.

Of 193 included sessions, 121 (62.3%) were manels and the mean percentage of included faculty that was male was 88.9% Apart from the topics of practice management/rehabilitation and musculoskeletal oncology, male representation was very high. Additionally, most included conferences had an extremely high percentage of male representation apart from meetings hosted by the COA and ORS. Non-manel sessions had a greater mean number of chairs (p=0.006), speakers (p < 0 .001), and faculty (p < 0 .001) than manel sessions. Of 1080 total included faculty members, 960 (88.9%) were male. Male faculty were more likely to be Orthopedic surgeons than female faculty (p < 0 .001) while also more likely to hold academic rank as a professor. Mean number of sessions between male and female faculty within their respective quartiles of H-indexes, sum of times cited, and number of publications did not reach statistical significance. Mean years of practice between male and female faculty was also not significantly different.

There is a high prevalence of manels and an overall lack of female representation in Orthopedic meetings. Orthopedic associations should aim to make efforts to increase gender equity in future meetings.

In the current healthcare environment, cost containment has become more important than ever. Perioperative services are often scrutinized as they consume more than 30% of North American hospitals’ budgets. The procurement, processing, and use of sterile surgical inventory is a major component of the perioperative care budget and has been recognized as an area of operational inefficiency. Although a recent systematic review supported the optimization of surgical inventory reprocessing as a means to increase efficiency and eliminate waste, there is a paucity of data on how to actually implement this change. A well-studied and established approach to implementing organizational change is Kotter's Change Model (KCM). The KCM process posits that organizational change can be facilitated by a dynamic 8-step approach and has been increasingly applied to the healthcare setting to facilitate the implementation of quality improvement (QI) interventions. We performed an inventory optimization (IO) to improve inventory and instrument reprocessing efficiency for the purpose of cost containment using the KCM framework. The purpose of this quality improvement (QI) project was to implement the IO using KCM, overcome organizational barriers to change, and measure key outcome metrics related to surgical inventory and corresponding clinician satisfaction. We hypothesized that the KCM would be an effective method of implementing the IO.

This study was conducted at a tertiary academic hospital across the four highest-volume surgical services - Orthopedics, Otolaryngology, General Surgery, and Gynecology. The IO was implemented using the steps outlined by KCM (Figure 1): 1) create coalition, 2) create vision for change, 3) establish urgency, 4) communicate the vision, 5) empower broad based action, 6) generate general short term wins, 7) consolidate gains, and 8) anchor change. This process was evaluated using inventory metrics - total inventory reduction and depreciation cost savings; operational efficiency metrics - reprocessing labor efficiency and case cancellation rate; and clinician satisfaction.

The implementation of KCM is described in Table 1. Total inventory was reduced by 37.7% with an average tray size reduction of 18.0%. This led to a total reprocessing time savings of 1333 hours per annum and labour cost savings of $39 995 per annum. Depreciation cost savings was $64 320 per annum. Case cancellation rate due to instrument-related errors decreased from 3.9% to 0.2%. The proportion of staff completely satisfied with the inventory was 1.7% pre-IO and 80% post-IO.

This was the first study to show the success of applying KCM to facilitate change in the perioperative setting with respect to surgical inventory. We have outlined the important organizational obstacles faced when making changes to surgical inventory. The same KCM protocol can be followed for optimization processes for disposable versus reusable surgical device purchasing or perioperative scheduling. Although increasing efforts are being dedicated to quality improvement and efficiency, institutions will need an organized and systematic approach such as the KCM to successfully enact changes.

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