Increasing operating room productivity by duration categories and a newsvendor model

Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic Review

Background

There is a growing demand for total joint arthroplasty (TJA) surgery. The applications of machine learning (ML), mathematical optimization, and computer simulation have the potential to improve efficiency of TJA care delivery through outcome prediction and surgical scheduling optimization, easing the burden on health-care systems. The purpose of this study was to evaluate strategies using advances in analytics and computational modeling that may improve planning and the overall efficiency of TJA care.

Methods

A systematic review including MEDLINE, Embase, and IEEE Xplore databases was completed from inception to October 3, 2022, for identification of studies generating ML models for TJA length of stay, duration of surgery, and hospital readmission prediction. A scoping review of optimization strategies in elective surgical scheduling was also conducted.

Results

Twenty studies were included for evaluating ML predictions and 17 in the scoping review of scheduling optimization. Among studies generating linear or logistic control models alongside ML models, only 1 found a control model to outperform its ML counterpart. Furthermore, neural networks performed superior to or at the same level as conventional ML models in all but 1 study. Implementation of mathematical and simulation strategies improved the optimization efficiency when compared to traditional scheduling methods at the operational level.

Conclusions

High-performing predictive ML-based models have been developed for TJA, as have mathematical strategies for elective surgical scheduling optimization. By leveraging artificial intelligence for outcome prediction and surgical optimization, there exist greater opportunities for improved resource utilization and cost-savings in TJA than when using traditional modeling and scheduling methods.

Ability of a Complexity Scoring System to Predict Veterinary Student Surgical Procedure and Clinic Duration

The Midwestern University College of Veterinary Medicine hosts student-run free clinics that offer surgical sterilization of male and female dogs and cats, with the goal of 20 surgical cases per clinic. Surgical complexity varies significantly between the surgical procedures for males (castration) and females (ovariohysterectomy) and is also influenced by weight and age for dogs. A surgical complexity scoring system was implemented to ensure the minimum number of patients while providing a diverse mix of cases. The aim of this study was to determine whether the surgical complexity scoring system accurately predicted procedure duration. Surgical records were collected between August 2016 and October 2019. Points (1-5) were assigned to each patient at the time of appointment based on species, sex, additional procedure, age and weight, and the schedule was targeted for 50 points. Each point was predicted to account for 15 minutes of surgical time. The duration for each point category was assessed via rank-sum against the predicted median. Sixteen clinics occurred during the study period, having a mean of 40.4 points and 17 patients, 29.5 (74%) of which were allocated to students. There were 264 surgeries, with 241 (91%) having complete start and end times. Surgical duration for student surgeries was not different from the estimate for each point category, with the exception of 2-points, which had a median 5.0 minutes longer than anticipated (p = .0004). The surgical complexity scoring system is an effective tool to optimize scheduling of educational spay/neuter mobile clinics.

A two-stage robust optimization approach for the master surgical schedule problem under uncertainty considering downstream resources

This paper addresses a planning decision for operating rooms (ORs) that aim at supporting hospital management. Focusing on elective patients, we determined the master surgical schedule (MSS) on a one-week time horizon. We assigned the specialties to available sessions and allocated surgeries to them while taking into consideration the priorities of the outpatients in the ambulatory surgical discipline. Surgeries were selected from the waiting lists according to their priorities. The proposed approach considered operating theater (OT) restrictions, patients' priorities and accounted for the availability of both intensive care unit (ICU) beds and post-surgery beds. Since the management decisions of hospitals are usually made in an uncertain environment, our approach considered the uncertainty of surgery duration and availability of ICU bed. Two robust optimization approaches that kept the model computationally tractable are described and applied to deal with uncertainty. Computational results based on a medium-sized French hospital archives have been presented to compare the robust models to the deterministic counterpart and to demonstrate the price of robustness.

Operating Rooms Scheduling for Elective Surgeries in a Hospital Affected by War-Related Incidents

Hospital scheduling presents huge challenges for the healthcare industry. Various studies have been conducted in many different countries with focus on both elective and non-elective surgeries. There are important variables and factors that need to be taken into considerations. Different methods and approaches have also been used to examine hospital scheduling. Notwithstanding the continuous changes in modern healthcare services and, in particular, hospital operations, consistent reviews and further studies are still required. The importance of hospital scheduling, particularly, has become more critical as the trade-off between limited resources and overwhelming demand is becoming more evident. This situation is even more pressing in a volatile country where shootings and bombings in public areas happened. Hospital scheduling for elective surgeries in volatile country such as Iraq is therefore often interrupted by non-elective surgeries due to war-related incidents. Hence, this paper intends to address this issue by proposing a hospital scheduling model with focus on neuro-surgery department. The aim of the model is to maximize utilization of operating room while concurrently minimizing idle time of surgery. The study focused on neurosurgery department in Al-Shahid Ghazi Al-Hariri hospital in Baghdad, Iraq. In doing so, a Mixed-integer linear programming (MILP) model is formulated where interruptions of non-elective surgery are incorporated into the main elective surgery based model. Computational experiment is then carried out to test the model. The result indicates that the model is feasible and can be solved in reasonable times. Nonetheless, its feasibility is further tested as the problems size and the computation times is getting bigger and longer. Application of heuristic methods is the way forward to ensure better practicality of the proposed model. In the end, the potential benefit of this study and the proposed model is discussed.

Sizing capacity levels in emergency medical services dispatch centers: Using the newsvendor approach

BACKGROUND

The increased volume in demand worldwide in the present day has led to the need for the establishment of effective ambulance services. As call centers have become the primary contact point between patients and emergency service providers, the planning of the call center has become a key task for administrators.

OBJECTIVES

The aim of this study is to apply a widely used operations management method, the newsvendor model, for optimizing the capacity level in EMS call centers with a minimum cost in order to efficiently meet the calls arriving.

METHODS

Real-life data from a call center for ambulance services in a major city in Turkey was used. We propose using the newsvendor model for optimizing this call center's capacity level based on the forecasts of periodic call volumes via basic methods.

RESULTS

Ambulance service call volumes vary during the day and weekday call profiles are different from weekends. By separating the analysis into weekdays and weekends and illustrating shorter time intervals within the days, call volume can be forecast. Taking not only the point forecast but also the variation of the forecast into account, the capacity level of each period can be planned in a cost-effective way.

CONCLUSIONS

This paper provides a basis for operation planning strategies of ambulance services by reconsidering the uncertainties of demand. The newsvendor model, which works well under parameter uncertainty, can be used in planning the capacities of health care services, especially when high service levels are required.

"Knife to skin" time is a poor marker of operating room utilization and efficiency in cardiac surgery

BACKGROUND

Markers of operation room (OR) efficiency in cardiac surgery are focused on "knife to skin" and "start time tardiness." These do not evaluate the middle and later parts of the cardiac surgical pathway. The purpose of this analysis was to evaluate knife to skin time as an efficiency marker in cardiac surgery.

METHODS

We looked at knife to skin time, procedure time, and transfer times in the cardiac operational pathway for their correlation with predefined indices of operational efficiency (Index of Operation Efficiency - InOE, Surgical Index of Operational Efficiency - sInOE). A regression analysis was performed to test the goodness of fit of the regression curves estimated for InOE relative to the times on the operational pathway.

RESULTS

The mean knife to skin time was 90.6 ± 13 minutes (23% of total OR time). The mean procedure time was 282 ± 123 minutes (71% of total OR time). Utilization efficiencies were highest for aortic valve replacement and coronary artery bypass grafting and least for complex aortic procedures. There were no significant procedure-specific or team-specific differences for standard procedures. Procedure times correlated the strongest with InOE (r = -0.98, p < 0.01). Compared to procedure times, knife to skin is not as strong an indicator of efficiency. A statistically significant linear dependence on InOE was observed with "procedure times" only.

CONCLUSIONS

Procedure times are a better marker of OR efficiency than knife to skin in cardiac cases. Strategies to increase OR utilization and efficiency should address procedure times in addition to knife to skin times.

The influence of anesthesia-controlled time on operating room scheduling in Dutch university medical centres

BACKGROUND

Predicting total procedure time (TPT) entails several elements subject to variability, including the two main components: surgeon-controlled time (SCT) and anesthesia-controlled time (ACT). This study explores the effect of ACT on TPT as a proportion of TPT as opposed to a fixed number of minutes. The goal is to enhance the prediction of TPT and improve operating room scheduling.

METHODS

Data from six university medical centres (UMCs) over seven consecutive years (2005-2011) were included, comprising 330,258 inpatient elective surgical cases. Based on the actual ACT and SCT, the revised prediction of TPT was determined as SCT × 1.33. Differences between actual and predicted total procedure times were calculated for the two methods of prediction.

RESULTS

The predictability of TPT improved when the scheduling of procedures was based on predicting ACT as a proportion of SCT.

CONCLUSIONS

Efficient operating room (OR) management demands the accurate prediction of the times needed for all components of care, including SCT and ACT, for each surgical procedure. Supported by an extensive dataset from six UMCs, we advise grossing up the SCT by 33% to account for ACT (revised prediction of TPT = SCT × 1.33), rather than employing a methodology for predicting ACT based on a fixed number of minutes. This recommendation will improve OR scheduling, which could result in reducing overutilized OR time and the number of case cancellations and could lead to more efficient use of limited OR resources.