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.

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.

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.

Most cost containment efforts in public health systems have focused on regulating the use of hospital resources, especially operative time. As such, attempting to maximize the efficiency of limited operative time is important. Typically, hospital operating room (OR) scheduling of time is performed in two tiers: 1) master surgical scheduling (annual allocation of time between surgical services and surgeons) and 2) daily scheduling (a surgeon's selection of cases per operative day). Master surgical scheduling is based on a hospital's annual case mix and depends on the annual throughput rate per case type. This throughput rate depends on the efficiency of surgeons’ daily scheduling. However, daily scheduling is predominantly performed manually, which requires that the human planner simultaneously reasons about unknowns such as case-specific length-of-surgery and variability while attempting to maximize throughput. This often leads to OR overtime and likely sub-optimal throughput rate. In contrast, scheduling using mathematical and optimization methods can produce maximum systems efficiency, and is extensively used in the business world. As such, the purpose of our study was to compare the efficiency of 1) manual and 2) optimized OR scheduling at an academic-affiliated community hospital representative of most North American centres.

Historic OR data was collected over a four year period for seven surgeons. The actual scheduling, surgical duration, overtime and number of OR days were extracted. This data was first configured to represent the historic manual scheduling process. Following this, the data was then used as the input to an integer linear programming model with the goal of determining the minimum number of OR days to complete the same number of cases while not exceeding the historic overtime values. Parameters included the use of a different quantile for each case type's surgical duration in order to ensure a schedule within five percent of the historic overtime value per OR day.

All surgeons saw a median 10% (range: 9.2% to 18.3%) reduction in the number of OR days needed to complete their annual case-load compared to their historical scheduling practices. Meanwhile, the OR overtime varied by a maximum of 5%. The daily OR configurations differed from historic configurations in 87% of cases. In addition, the number of configurations per surgeon was reduced from an average of six to four.

Our study demonstrates a significant increase in OR throughput rate (10%) with no change in operative time required. This has considerable implications in terms of cost reduction, surgical wait lists and surgeon satisfaction. A limitation of this study was that the potential gains are based on the efficiency of the pre-existing manual scheduling at our hospital. However, given the range of scenarios tested, number of surgeons included and the similarity of our hospital size and configuration to the majority of North American hospitals with an orthopedic service, these results are generalizable. Further optimization may be achieved by taking into account factors that could predict case duration such as surgeon experience, patients characteristics, and institutional attributes via machine learning.