Overutilization and underutilization of operating rooms - insights from behavioral health care operations management

A machine learning prediction model for total shoulder arthroplasty procedure duration: an evaluation of surgeon, patient, and shoulder-specific factors

BACKGROUND

Operating room efficiency is of paramount importance for scheduling, cost efficiency, and to allow for the high operating volume required to address the growing demand for arthroplasty. The purpose of this study was to develop a machine learning predictive model for total shoulder arthroplasty (TSA) procedure duration and to identify factors which are predictive of a prolonged procedure.

METHODS

A retrospective review was undertaken of all TSA between 2013 and 2021 in a large academic institution. Patient, surgeon, anesthetic, and shoulder-specific factors were assessed. The duration of time in the operating room was recorded and compared to the human scheduler and electronic health record predicted procedure duration. Two gradient-boosted decision tree regression models were created with both training and validation datasets. The mean squared logarithmic error was chosen as the loss function. The first model (M1) considered patient, surgeon, and anesthetic factors, while the second model (M2) considered shoulder anatomy and pathology specific factors in addition.

RESULTS

Human schedulers' predicted 64.1% of cases accurately, with 26.7% underpredicted and 9.2% overpredicted. M1 successfully predicted 79.7% of cases, with 6.9% underpredicted and 13.4% overpredicted. M2 successfully predicted 82.5% of cases with 8.8% underpredicted and 8.8% overpredicted. M2 was significantly more accurate in predicting anatomic total shoulder arthroplasty compared to reverse (rTSA) (90.6% vs. 78.1%, P < .001).The feature with the greatest impact on the shoulder-specific model's prediction was the historical median procedure duration; followed by the electronic health record prediction, surgeon prediction, patient age, and a traumatic indication. Factors which were associated with underpredicting procedure duration included younger age, traumatic indication, male sex, greater body mass index, and a B2 glenoid.

CONCLUSION

Machine learning predictive models outperformed traditional scheduling, with a model incorporating general and shoulder-specific data providing the most accurate prediction of TSA procedure duration. Integration of modeling has the potential to optimize theater utilization and improve efficiency.

Assessing the Precision of Surgery Duration Estimation: A Retrospective Study

Background and Objectives

The operating room (OR) is considered the highest source of cost and earnings. Therefore, measuring OR efficiency, which means how time and resources are allocated precisely for their intended purposes in the operating room is crucial. Both overestimation and underestimation negatively impact OR efficiency Therefore, hospitals defined metrics to Measuring OR Effeciency. Many studies have discussed OR efficiency and how surgery scheduling accuracy plays a vital role in increasing OR efficiency. This study aims to evaluate OR efficiency using surgery duration accuracy.

Methods

This retrospective, quantitative study was conducted at King Abdulaziz Medical City. We extracted data on 97,397 surgeries from 2017 to 2021 from the OR database. The accuracy of surgery duration was identified by calculating the duration of each surgery in minutes by subtracting the time of leaving the OR from the time of entering the OR. Based on the scheduled duration, the calculated durations were categorized as either underestimation or overestimation. Descriptive and bivariate analyses (Chi-square test) were performed using the Statistical Package for the Social Sciences (SPSS) software.

Results

Sixty percent out of the 97,397 surgeries performed were overestimated compared to the time scheduled by the surgeons. Patient characteristics, surgical division, and anesthesia type showed statistically significant differences (p <0.05) in their OR estimation.

Conclusion

Significant proportion of procedures have overestimated. This finding provides insight into the need for improvement.

Recommendations

It is recommended to enhance the surgical scheduling method using machine learning (ML) models to include patient characteristics, department, anesthesia type, and even the performing surgeon increases the accuracy of duration estimation. Then, evaluate the performance of an ML model in future studies.

Operating Room Performance Optimization Metrics: a Systematic Review

Literature proposes numerous initiatives for optimization of the Operating Room (OR). Despite multiple suggested strategies for the optimization of workflow on the OR, its patients and (medical) staff, no uniform description of 'optimization' has been adopted. This makes it difficult to evaluate the proposed optimization strategies. In particular, the metrics used to quantify OR performance are diverse so that assessing the impact of suggested approaches is complex or even impossible. To secure a higher implementation success rate of optimisation strategies in practice we believe OR optimisation and its quantification should be further investigated. We aim to provide an inventory of the metrics and methods used to optimise the OR by the means of a structured literature study. We observe that several aspects of OR performance are unaddressed in literature, and no studies account for possible interactions between metrics of quality and efficiency. We conclude that a systems approach is needed to align metrics across different elements of OR performance, and that the wellbeing of healthcare professionals is underrepresented in current optimisation approaches.

Case-Mix Moderation of the Relationship Between OR Performance Metrics and Utilization

We investigated the impact of the case-mix ratio of inpatients to outpatients on the relationships between OR utilization and late starts, turnover time, delays, cancellations, and idle time at an academic medical center in the southeastern United States. After extracting 55 months of data from the surgical repository, we used simple and multiple linear regression models to analyze the data and determine the strength and direction of the relationships among the variables. We compared models comprising proportionally more inpatients to models comprising proportionally more outpatients for each metric to ascertain the effects of case mix on OR utilization. Idle time had the greatest effect on OR utilization, followed by late starts and turnover time. Case mix moderated the relationship between OR utilization and the metrics of cancellations and turnover time. Perioperative leaders may enhance OR utilization by monitoring and addressing idle time and late starts and scheduling an appropriate mix of inpatients and outpatients.

Surgeons' physical workload in open surgery versus robot-assisted surgery and nonsurgical tasks

BACKGROUND

Musculoskeletal disorders (MSDs) are common among surgeons, and its prevalence varies among surgical modalities. There are conflicting results concerning the correlation between adverse work exposures and MSD prevalence in different surgical modalities. The progress of rationalization in health care may lead to job intensification for surgeons, but the literature is scarce regarding to what extent such intensification influences the physical workload in surgery. The objectives of this study were to quantify the physical workload in open surgery and compare it to that in (1) nonsurgical tasks and (2) two surgeon roles in robot-assisted surgery (RAS).

METHODS

The physical workload of 22 surgeons (12 performing open surgery and 10 RAS) was measured during surgical workdays, which includes trapezius muscle activity from electromyography, and posture and movement of the head, upper arms and trunk from inertial measurement units. The physical workload of surgeons in open surgery was compared to that in nonsurgical tasks, and to the chief and assistant surgeons in RAS, and to the corresponding proposed action levels. Mixed-effects models were used to analyze the differences.

RESULTS

Open surgery constituted more than half of a surgical workday. It was associated with more awkward postures of the head and trunk than nonsurgical tasks. It was also associated with higher trapezius muscle activity levels, less muscle rest time and a higher proportion of sustained low muscle activity than nonsurgical tasks and the two roles in RAS. The head inclination and trapezius activity in open surgery exceeded the proposed action levels.

CONCLUSIONS

The physical workload of surgeons in open surgery, which exceeded the proposed action levels, was higher than that in RAS and that in nonsurgical tasks. Demands of increased operation time may result in higher physical workload for open surgeons, which poses an increased risk of MSDs. Risk-reducing measures are, therefore, needed.

Revisiting the Causes of the Pull-to-Centre Effect: Evidence From China

Prior experimental studies have shown that individuals' actual ordering decisions significantly deviate from the theoretical optimum in newsvendor problems and show the robust pull-to-centre (PTC) effect. Several human behaviours have been confirmed to be the causes of the PTC. However, most newsvendor experiments have been conducted in multicultural countries (e.g., the United States and Germany). As there exist mutual influences between culture and behaviour, in this study, we revisit the ordering biases in a monocultural country to examine the robustness of the PTC and whether the causes can still explain this phenomenon. Our results show that the PTC still prevails and heuristics still work. However, overconfidence cannot perfectly interpret the PTC in China for probable inconsistent confidence levels in individual judgments and decisions. Moreover, the "centre" may no longer be the mean demand but the average value of the realised demand. We explain these changes from the perspective of cultural differences. To be more specific, collectivism, holistic style, and Doctrine of the Mean play vital roles in Chinese newsvendors' decisions.

Operating room efficiency in a low resource setting: a pilot study from a large tertiary referral center in Ethiopia

BACKGROUND

The operating room (OR) is one of the most expensive areas of a hospital, requiring large capital and recurring investments, and necessitating efficient throughput to reduce costs per patient encounter. On top of increasing costs, inefficient utilization of operating rooms results in prolonged waiting lists, high rate of cancellation, frustration of OR personnel as well as increased anxiety that negatively impacts the health of patients. This problem is magnified in developing countries, where there is a high unmet surgical need. However, no system currently exists to assess operating room utilization in Ethiopia.

METHODOLOGY

A prospective study was conducted over a period of 3 months (May 1 to July 31, 2019) in a tertiary hospital. Surgical case start time, end time, room turnover time, cancellations and reason for cancellation were observed to evaluate the efficiency of eight operating rooms.

RESULTS

A total of 933 elective procedures were observed during the study period. Of these, 246 were cancelled, yielding a cancellation rate of 35.8%. The most common reasons for cancellation were related to lack of OR time and patient preparation (8.7% and 7.7% respectively). Shortage of facilities (instrument, blood, ICU bed) were causes of cancelation in 7.7%. Start time was delayed in 93.4% (mean 8:56 am ± 52 min) of cases. Last case completion time was early in 47.9% and delayed in 20.6% (mean 2:54 pm ± 156 min). Turnover time was prolonged in 34.5% (mean 25 min ± 49 min). Total operating room utilization ranged from 10.5% to 174%. Operating rooms were underutilized in 42.7% while overutilization was found in 14.6%.

CONCLUSION

We found a high cancellation rate, most attributable to late start times leading to delays for the remainder of cases, and lack of preoperative patient preparation. In a setting with a high unmet burden of surgical disease, OR efficiency must be maximized with improved patient evaluation workflows, adequate OR staffing and commitment to punctual start times. We recommend future quality improvement projects focusing on these areas to increase OR efficiency.

[Time management in operating rooms-a cross-sectional study to evaluate estimated and objective durations of otorhinolaryngologic surgical procedures]

Hintergrund

Die Gestaltung des Operations(Op.)-Programms im klinischen Alltag ist von hoher Wichtigkeit für die Wirtschaftlichkeit. Gleichzeitig muss die Einhaltung von Arbeitszeiten unterschiedlicher Berufsgruppen berücksichtigt werden.

Ziel der Arbeit

Um Fehlerquellen bei der Planung aufzudecken, wurden durch Chirurgen geschätzte mit objektiv erhobenen Zeiten (u. a. Schnitt-Naht-Zeit) verglichen.

Material und Methoden

In einer retrospektiven Analyse wurden 1809 Operationen im Jahr 2018 (22 verschiedene Op.-Arten) durch 31 Operateure (12 Fach- [FÄ] und 19 Assistenzärzte [AÄ]) hinsichtlich ihrer Dauer verglichen und mittels Mann-Whitney-U-Test auf Signifikanz geprüft.

Ergebnisse

Der Vergleich der objektiven Zeiten von FÄ und AÄ zeigt signifikante Unterschiede in der Schnitt-Naht-Zeit bzw. der Summe aus Schnitt-Naht-Zeit und Zeit der chirurgischen Maßnahmen für 6 von 15 Op.-Arten (p < 0,001). Die durch FÄ geschätzte Nachbereitungszeit wich bei 2 von 22 Op.-Arten von der objektiven Zeit ab (p < 0,05), die durch AÄ geschätzte Zeit bei 7 von 15 Op.-Arten (p < 0,05). Hinsichtlich der Schnitt-Naht-Zeit verschätzten sich FÄ bei 7 von 22 (p < 0,05), AÄ bei 3 von 15 (p < 0,05) Op.-Arten. Die durch FÄ geschätzte Vorbereitungszeit wich bei 16 von 22 Op.-Arten signifikant von der objektiven Zeit ab (p < 0,05), bei AÄ bei 7 von 15 (p < 0,001). Vor- und Nachbereitungszeiten wurden durch FÄ unter‑, Schnitt-Naht-Zeiten überschätzt. AÄ unterschätzten alle Zeiten.

Schlussfolgerung

Bei der OP-Planung muss die Erfahrung des durchführenden Chirurgen berücksichtigt werden. Eine Verbesserung durch verminderte subjektive Fehleinschätzung kann möglicherweise mithilfe spezieller Algorithmen gelingen.

A heuristic algorithm for medical staff's scheduling problems with multiskills and vacation control

INTRODUCTION

The main issue related to the duty schedule is to allocate medical staff to each medical department by considering personnel skills and personal vocation preferences. However, how to effectively use staff's multiskill characteristics and how to execute vocation control have not been well investigated.

OBJECTIVES

This article aims to develop duty scheduling and vacation permission decisions to minimize the sum of customers' waiting costs, the overtime cost of medical staff, the cost of failing to meet medical staff' vacation requirements, and the cost of mutual support between departments.

METHODS

This study formulated the problem as a multiperiod mixed integer nonlinear programming model and developed a hybrid heuristic based on evolutionary mechanism of genetic algorithm and linear programming to efficiently solve the proposed model.

RESULTS

Five types of problems were solved through Lingo optimization and the proposed approach. For small-scale problems, both methods can find the optimal solutions. For a slightly larger problem, the solutions found by the proposed approach are superior those of Lingo.

CONCLUSION

This research discusses the complex decision-making problem of on-duty arrangement and vacation control of medical staff in a multidepartmental medical center. This research formulates the medical staff's scheduling and vacation control problems as constrained mixed integer quadratic programming problems. Computational results indicate that the proposed approach can efficiently produce compromise solutions that outperform the solutions of the Lingo optimization software.

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.

Managing admission and discharge processes in intensive care units

The intensive care unit (ICU) is one of the most crucial and expensive resources in a health care system. While high fixed costs usually lead to tight capacities, shortages have severe consequences. Thus, various challenging issues exist: When should an ICU admit or reject arriving patients in general? Should ICUs always be able to admit critical patients or rather focus on high utilization? On an operational level, both admission control of arriving patients and demand-driven early discharge of currently residing patients are decision variables and should be considered simultaneously. This paper discusses the trade-off between medical and monetary goals when managing intensive care units by modeling the problem as a Markov decision process. Intuitive, myopic rule mimicking decision-making in practice is applied as a benchmark. In a numerical study based on real-world data, we demonstrate that the medical results deteriorate dramatically when focusing on monetary goals only, and vice versa. Using our model, we illustrate the trade-off along an efficiency frontier that accounts for all combinations of medical and monetary goals. Coming from a solution that optimizes monetary costs, a significant reduction of expected mortality can be achieved at little additional monetary cost.

How hospitals can improve their public quality metrics: a decision-theoretic model

The public reporting of hospitals' quality of care is providing additional motivation for hospitals to deliver high-quality patient care. Hospital Compare, a consumer-oriented website by the Centers for Medicare and Medicaid Services (CMS), provides patients with detailed quality of care data on most US hospitals. Given that many quality metrics are the aggregate result of physicians' individual clinical decisions, the question arises if and how hospitals could influence their physicians so that their decisions positively contribute to hospitals' quality goals. In this paper, we develop a decision-theoretic model to explore how three different hospital interventions-incentivization, training, and nudging-may affect physicians' decisions. We focus our analysis on Outpatient Measure 14 (OP-14), which is an imaging quality metric that reports the percentage of outpatients with a brain computed tomography (CT) scan, who also received a same-day sinus CT scan. In most cases, same-day brain and sinus CT scans are considered unnecessary, and high utilizing hospitals aim to reduce their OP-14 metric. Our model captures the physicians' imaging decision process accounting for medical and behavioral factors, in particular the uncertainty in clinical assessment and a physician's diagnostic ability. Our analysis shows how hospital interventions of incentivization, training, and nudging affect physician decisions and consequently OP-14. This decision-theoretic model provides a foundation to develop insights for policy makers on the multi-level effects of their policy decisions.

Inventory and ordering decisions: a systematic review on research driven through behavioral experiments

Purpose

The success of a supply chain is highly reliant on effective inventory and ordering decisions. This paper systematically reviews and analyzes the literature on inventory ordering decisions conducted using behavioral experiments to inform the state-of-the-art.

Design/methodology/approach

This paper presents the first systematic review of this literature. We systematically identify a body of 101 papers from an initial pool of over 12,000.

Findings

Extant literature and industry observations posit that decision makers often deviate from optimal ordering behavior prescribed by the quantitative models. Such deviations are often accompanied by excessive inventory costs and/or lost sales. Understanding how humans make inventory decisions is paramount to minimize the associated consequences. To address this, the field of behavioral operations management has produced a rich body of research on inventory decision-making using behavioral experiments. Our analysis identifies primary research clusters, summarizes key learnings and highlights opportunities for future research in this critical decision-making area.

Practical implications

The findings will have a significant impact on future research on behavioral inventory ordering decisions while informing practitioners to reach better ordering decisions.

Originality/value

Previous systematic reviews have explored behavioral operations broadly or its subdisciplines such as judgmental forecasting. This paper presents a systematic review that specifically investigates the state-of-the-art of inventory ordering decisions using behavioral experiments.

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.