Which algorithm for scheduling add-on elective cases maximizes operating room utilization? Use of bin packing algorithms and fuzzy constraints in operating room management

Alignment of RVU Targets With Operating Room Block Time

Background

Surgeon productivity is measured in relative value units (RVUs). The feasibility of attaining RVU productivity targets requires surgeons to have enough allocated block time to generate RVUs. However, it is unknown how much block time is required for surgeons to attain specific RVU targets. We aimed to estimate the effect of surgeon and practice environment characteristics (SPECs) on block time needed to attain fixed RVU targets.

Methods

We computationally simulated individual surgeons' annual caseloads under a variety of SPECs in the following way. First, empirical case data were sampled from ACS NSQIP in accordance with surgeon specialty, case-mix complexity, and RVU target. Surgeons' operating schedules were then constructed according to the block length, turnover time, and scheduling flexibility of the practice environment. These 6 SPECs were concurrently varied over their ranges for a 6-way sensitivity analysis.

Results

Annual operating schedules for 60,000,000 surgeons were simulated. The number of blocks required to attain RVU targets varied significantly with surgeon specialty and increased with increased case-mix complexity, increased turnover time, and decreased scheduling flexibility. Intraspecialty variation in block requirement with variation in environmental characteristics exceeded interspecialty variation with fixed environmental characteristics. Multivariate linear models predicted block utilization across surgical specialties with consideration for the stated factors. An online tool is shared with which to apply these results to one's particular practice.

Conclusions

Block time required to attain RVU targets varies widely with SPECs; intraspecialty variation exceeds interspecialty variation. The feasibility of attaining RVU targets requires alignment between targets and allocated operating time with consideration for surgical specialty and other practice conditions.

Metaheuristic algorithms for one-dimensional bin-packing problems: A survey of recent advances and applications

The bin-packing problem (BPP) is an age-old NP-hard combinatorial optimization problem, which is defined as the placement of a set of different-sized items into identical bins such that the number of containers used is optimally minimized. Besides, different variations of the problem do exist in practice depending on the bins dimension, placement constraints, and priority. More so, there are several important real-world applications of the BPP, especially in cutting industries, transportation, warehousing, and supply chain management. Due to the practical relevance of this problem, researchers are consistently investigating new and improved techniques to solve the problem optimally. Nature-inspired metaheuristics are powerful algorithms that have proven their incredible capability of solving challenging and complex optimization problems, including several variants of BPPs. However, no comprehensive literature review exists on the applications of the metaheuristic approaches to solve the BPPs. Therefore, to fill this gap, this article presents a survey of the recent advances achieved for the one-dimensional BPP, with specific emphasis on population-based metaheuristic algorithms. We believe that this article can serve as a reference guide for researchers to explore and develop more robust state-of-the-art metaheuristics algorithms for solving the emerging variants of the bin-parking problems.

Caseload is increased by resequencing cases before and on the day of surgery at ambulatory surgery centers where initial patient recovery is in operating rooms and cleanup times are longer than typical

Study objective

The coronavirus disease 2019 (COVID-19) pandemic impacts operating room (OR) management in regions with high prevalence (e.g., >1.0% of asymptomatic patients testing positive). Cases with aerosol producing procedures are isolated to a few ORs, initial phase I recovery of those patients is in the ORs, and multimodal environmental decontamination applied. We quantified the potential increase in productivity from also resequencing these cases among those 2 or 3 ORs.

Design

Computer simulation provided sample sizes requiring >100 years experimentally. Resequencing was limited to changes in the start times of surgeons' lists of cases.

Setting

Ambulatory surgery center or hospital outpatient department.

Main results

With case resequencing applied before and on the day of surgery, there were 5.6% and 5.5% more cases per OR per day for the 2 ORs and 3 ORs, respectively, both standard errors (SE) < 0.1%. Resequencing cases among ORs to start cases earlier permitted increases in the hours into which cases could be scheduled from 10.5 to 11.0 h, while assuring >90% probability of each OR finishing within the prespecified 12-h shift. Thus, the additional cases were all scheduled before the day of surgery. The greater allocated time also resulted in less overutilized time, a mean of 4.2 min per OR per day for 2 ORs (SE 0.5) and 6.3 min per OR per day for 3 ORs (SE 0.4). The benefit could be achieved while limiting application of resequencing to days when the OR with the fewest estimated hours of cases has ≤8 h.

Conclusions

Some ambulatory surgery ORs have unusually long OR times and/or room cleanup times (e.g., infection control efforts because of the pandemic). Resequencing cases before and on the day of surgery should be considered, because moving 1 or 2 cases occasionally has little to no cost with substantive benefit.

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COVID-19 influences management for aerosol producing procedures.

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Simulation studied case resequencing applied before and on the day of surgery.

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>5% more queued cases can be done per OR per day with practical heuristic.

Importance of operating room case scheduling on analyses of observed reductions in surgical site infections from the purchase and installation of capital equipment in operating rooms

BACKGROUND

We review the impact of the consequences of operating room (OR) management decision making on power analyses for observational studies of surgical site infections (SSIs) among patients receiving care in ORs with interventions versus without interventions involving physical changes to ORs. Examples include ventilation systems, bactericidal lighting, and physical alterations to ORs.

METHODS

We performed a narrative review of operating room management and surgical site infection articles. We used 10-years of operating room data to estimate parameters for use in statistical power analyses.

RESULTS

Creating pivot tables or monthly control charts of SSI per case by OR and comparing among ORs with or without intervention is not recommended. This approach has low power to detect a difference in SSI rates among the ORs with or without the intervention. The reason is that appropriate OR case scheduling decision making causes risk factors for SSI to differ among ORs, even when stratifying by surgical specialty. Such risk factors include case duration, urgency, and American Society of Anesthesiologists' Physical Status. Instead, analyze SSI controlling for the OR, where the patient had surgery, and matching patients using these variables is preferable. With α = 0.05, 600 cases per OR, 5 intervention ORs, and 5 or 1 control patients for each intervention patient, reasonable power (≅94% or 78%, respectively) can be achieved to detect reductions (3.6% to 2.4%) in the incidence of SSI between ORs with or without the intervention.

CONCLUSIONS

By using this matched cohort design, the effect of the purchase and installation of capital equipment in ORs on SSI can be evaluated meaningfully.

A hybrid genetic algorithm for operating room scheduling

In this research, we studied operating room scheduling problem of assigning a set of surgeries to several multifunctional operating rooms. The objectives are to maximize the utilization of the operating rooms, to minimize the overtime-operating cost, and to minimize the wasting cost for the unused time. To begin with, a revised mathematical model is constructed to assign surgeries to operating rooms within one week. Then, we proposed four easy-to-implement heuristics that can guarantee to find feasible solutions for the studied problem efficiently. Furthermore, we presented four local search procedures that can improve a given solution significantly. Finally, a hybrid genetic algorithm (HGA) that incorporated with initial solutions, local search procedures and elite search procedure is applied to the studied problem. Computational results show that for small problem instances, the HGA can find near optimal solutions efficiently while for large problem instances, the HGA performs significantly better than the four proposed heuristics. We concluded that surgery schedules obtained by using HGA has less wasting cost for the unused time, much higher utilization of operating rooms, and produce less overtime-operating cost.

Association of Reduced Delay in Care With a Dedicated Operating Room in Pediatric Otolaryngology

Importance

Obtaining sufficient operating room time for inpatient consults requiring an operative intervention is a persistent challenge for otolaryngologists.

Objective

To examine the institution of an otolaryngology-specific operating room (OR) for unscheduled (add-on) cases for its association with time from initial consultation to surgery and, secondarily, to determine utilization of a dedicated block of time.

Design, Setting, and Participants

Retrospective review of medical records of a tertiary care pediatric hospital for patients treated between January 1, 2015, and March 31, 2016; analysis was concluded by June 2016. Included were all patients undergoing inpatient otolaryngology consultations who required nonemergency operative procedures.

Interventions

In August 2015, a once-weekly 5-hour block of OR time dedicated to inpatient otolaryngology consults was instituted. Prior to this, cases were placed on an add-on list shared between all surgical services.

Main Outcomes and Measures

It was hypothesized that institution of a dedicated block of OR time would decrease the time from initial consultation to operative intervention and would be utilized at a high rate. Operating room utilization was calculated by dividing scheduled OR time by actual OR time utilized. Time from initial consultation to OR intervention was compared before and after the institution of the dedicated OR block.

Results

A total of 316 inpatient add-on pediatric cases (including 108 patients from the intensive care unit [ICU]) were scheduled during the study period. The most common cases were microlaryngoscopy/bronchoscopy (79%) and tracheostomy (8%). Mean (SD) time between consultation and OR intervention was 7.8 (1.6) days prior to establishing the add-on OR and 4.4 (1.3) days after it was established (absolute difference of 3.4 days; 95% CI, 3.1-3.7 days). Mean (SD) time between consultation and OR intervention was 7.4 (5.0) days for ICU patients prior to intervention and 5.6 (3.0) days after intervention (absolute difference of 1.8 days; 95% CI, 1.6-2.0 days). Total utilization of the OR block time was 74%, and adjusted utilization was 86%. There was a 15% drop in the number of unscheduled add-on cases after the intervention (from 10 cases/mo to 8.5 cases/mo; absolute difference of 1.5 cases; 95% CI, 1.1-1.9 cases).

Conclusions and Relevance

Instituting a dedicated otolaryngology add-on OR was associated with significantly reduced time between initial consultation and operative care, by approximately 3 days, decreased the number of unscheduled add-on cases, and was utilized at a high level.

Discovering Healthcare Cognition: The Use of Cognitive Artifacts to Reveal Cognitive Work

Healthcare systems, especially hospital operating room suites, have properties that make them ideal for the study of the cognitive work using the naturalistic decision-making (NDM) approach. This variable, complex, high-tempo setting provides a unique opportunity to examine the ways that clinicians plan, monitor, and cope with the irreducible uncertainty that underlies this work domain. As frontline managers, anesthesia coordinators plan and manage anesthesia assignments for surgical procedures. As frontline managers, coordinators develop and use cognitive artifacts to distribute cognition across time and among members of the acute care staff. Examination of these cognitive artifacts and their use reveals the hidden subtleties of the coordinators’ work. The use of NDM methods including cognitive artifact analysis to understand cognitive work generates insights that extend beyond the operator level to the study of team-level cognition. Results can be used to create computer-based artifacts that aid individual and team cognition.

Costs and Utilization of Operating Rooms in a Public Hospital in Trinidad, West Indies

CONTEXT

A top-down evaluation of the costs of operating rooms (ORs) is not commonly done because it is relevant mostly in a publicly funded system.

OBJECTIVE

This study was conducted to determine the costs and utilization of ORs in a public hospital in Trinidad, West Indies, for two one-year periods using a top-down model.

DESIGN

Quantitative observational study.Main Outcome Measures: A "cost-block" model suggested for evaluation of intensive care unit costs was adapted to suit ORs. Data were obtained from personal interviews, records, and surveys from the appropriate hospital departments. Adjusted OR utilization times also were recorded for both years.

RESULTS

The total annual costs of 4 ORs for the years 2006 and 2009 were approximately US $2.2 and $3.2 million, respectively. Capital expenditure contributed to 70% of the costs, followed by consumables (15%) and medical staff salary (8%). The daily cost of running the ORs was US $6242 in 2006, which rose to $8873 in 2009. The cost of unutilized OR time was approximately US $298,342 in 2006 and was reduced to $198,315 during 2009.

CONCLUSION

The adapted cost-block model was useful to evaluate the costs of ORs in a public hospital in Trinidad and can be used from the government's expenditure perspective. Because the cost of running the ORs was high, efficiency must be improved to minimize waste.

Event-based knowledge elicitation of operating room management decision-making using scenarios adapted from information systems data

Background

No systematic process has previously been described for a needs assessment that identifies the operating room (OR) management decisions made by the anesthesiologists and nurse managers at a facility that do not maximize the efficiency of use of OR time. We evaluated whether event-based knowledge elicitation can be used practically for rapid assessment of OR management decision-making at facilities, whether scenarios can be adapted automatically from information systems data, and the usefulness of the approach.

Methods

A process of event-based knowledge elicitation was developed to assess OR management decision-making that may reduce the efficiency of use of OR time. Hypothetical scenarios addressing every OR management decision influencing OR efficiency were created from published examples. Scenarios are adapted, so that cues about conditions are accurate and appropriate for each facility (e.g., if OR 1 is used as an example in a scenario, the listed procedure is a type of procedure performed at the facility in OR 1). Adaptation is performed automatically using the facility's OR information system or anesthesia information management system (AIMS) data for most scenarios (43 of 45). Performing the needs assessment takes approximately 1 hour of local managers' time while they decide if their decisions are consistent with the described scenarios. A table of contents of the indexed scenarios is created automatically, providing a simple version of problem solving using case-based reasoning. For example, a new OR manager wanting to know the best way to decide whether to move a case can look in the chapter on "Moving Cases on the Day of Surgery" to find a scenario that describes the situation being encountered.

Results

Scenarios have been adapted and used at 22 hospitals. Few changes in decisions were needed to increase the efficiency of use of OR time. The few changes were heterogeneous among hospitals, showing the usefulness of individualized assessments.

Conclusions

Our technical advance is the development and use of automated event-based knowledge elicitation to identify suboptimal OR management decisions that decrease the efficiency of use of OR time. The adapted scenarios can be used in future decision-making.

Analysing management policies for operating room planning using simulation

In this paper we analyse the operating room planning at a department of orthopaedic surgery in Sweden. We focus on the problem of meeting the uncertainty in demand of patient arrival and surgery duration and at the same time maximizing the utilization of operating room (OR) time. With a discrete-event model we simulate how different management polices affect different performance metrics such as patient waiting time, cancellations and the utilization of OR time. The experiments show that the performance of the operating room department can be improved significantly by applying a different policy in reserving OR-capacity for emergency cases together with a policy to increase staff in stand-by. Moreover, the developed simulation model provides estimates for a what-if situation related to the prognosis of an increasing number of hip-joint replacements.

Influence of procedure classification on process variability and parameter uncertainty of surgical case durations

BACKGROUND

Predictive variability of operating room (OR) times influences decision making on the day of surgery including when to start add-on cases, whether to move a case from one OR to another, and where to assign relief staff. One contributor to predictive variability is process variability, which arises among cases of the same procedure(s). Another contributor is parameter uncertainty, which is caused by small sample sizes of historical data.

METHODS

Process variability was quantified using absolute percentage errors of surgeons' bias-corrected estimates of OR time. The influence of procedure classification on process variability was studied using a dataset of 61,353 cases, each with 1 to 5 scheduled and actual Current Procedural Terminology (CPT) codes (i.e., a standardized vocabulary). Parameter uncertainty's sensitivity to sample size was quantified by studying ratios of 90% prediction bounds to medians. That studied dataset of 65,661 cases was used previously to validate a Bayesian method to calculate 90% prediction bounds using combinations of surgeons' scheduled estimates and historical OR times.

RESULTS

(1) Process variability differed significantly among 11 groups of surgical specialty and case urgency (P < 0.0001). For example, absolute percentage errors exceeded the overall median of 22% for 57% of urgent spine surgery cases versus 42% of elective spine surgery cases. (2) Process variability was not increased when scheduled and actual CPTs differed (P = 0.23 without and P = 0.47 with stratification based on the 11 groups), because most differences represented known (planned) options inherent to procedures. (3) Process variability was not associated with incidence of procedures (P = 0.79), after excluding cataract surgery, a procedure with high relative variability. (4) Parameter uncertainty from uncommon procedures (0-2 historical cases) accounted for essentially all of the uncertainty in decisions dependent on estimates of OR times. The Bayesian method moderated the effect of small sample sizes on uncertainty in estimates of OR times. In contrast, from prior work, the use of broad categories of procedures reduces parameter uncertainty but at the expense of increased process variability.

CONCLUSIONS

For procedures with few historic data, the Bayesian method allows for effective case duration prediction, permitting use of detailed procedure descriptions. Although fine resolution of scheduling procedures increases the chance of performed procedure(s) differing from scheduled procedure(s), this does not increase process variability. Future studies need both to address differences in process variability among specialties and accept the limitation that findings from one may not apply to others.

Prospective trial of thoracic and spine surgeons' updating of their estimated case durations at the start of cases

BACKGROUND

Surgeon estimates of case durations are important for operating room (OR) management decision making because many cases are rare combinations of procedures with few or no historical data. Thoracic and spine surgeons updated their scheduled OR times on the day of surgery just before the "time out" in the OR.

METHODS

All elective (scheduled) general thoracic (n = 39) and spine surgery (n = 48) cases at 1 hospital were studied over 3-month and 1.5-month periods, respectively.

RESULTS

Among cases with a change in predicted duration, most changes were made based on updates to the surgical or anesthetic procedures (thoracic 85%, spine 86%). For thoracic surgery, there was overall no significant median reduction in absolute prediction error (median 0 minutes, 95% confidence interval [CI] 0-0 minutes). Among the 37% of cases with changed predicted durations, there was a significant reduction in absolute error (median 38 minutes, 95% CI >7.5 minutes). For spine surgery, there was overall no reduction in the absolute error (median 0 minutes, 95% CI 0-0 minutes). Among the 29% of cases with changed predicted durations, absolute error was no worse, but not significantly better (point estimate of median reduction 34 minutes, 95% CI >0 minutes). Secondary observations made were no effect of updates on bias, frequent rounding of scheduled durations to the nearest half hour, and increased predictive error caused by decisions that reduced expected overutilized OR time.

CONCLUSIONS

A systematic program of routinely and/or always asking for updated case duration predictions will not substantively improve OR management decision making. However, when a change in surgical approach, surgical procedure, or anesthetic procedure is identified (e.g., at the intraoperative briefing before case start), the updated estimate of case duration should be used, because such updates are not worse and often better than original estimates.

Modeling procedure and surgical times for current procedural terminology-anesthesia-surgeon combinations and evaluation in terms of case-duration prediction and operating room efficiency: a multicenter study

BACKGROUND

Gains in operating room (OR) scheduling may be obtained by using accurate statistical models to predict surgical and procedure times. The 3 main contributions of this article are the following: (i) the validation of Strum's results on the statistical distribution of case durations, including surgeon effects, using OR databases of 2 European hospitals, (ii) the use of expert prior expectations to predict durations of rarely observed cases, and (iii) the application of the proposed methods to predict case durations, with an analysis of the resulting increase in OR efficiency.

METHODS

We retrospectively reviewed all recorded surgical cases of 2 large European teaching hospitals from 2005 to 2008, involving 85,312 cases and 92,099 h in total. Surgical times tended to be skewed and bounded by some minimally required time. We compared the fit of the normal distribution with that of 2- and 3-parameter lognormal distributions for case durations of a range of Current Procedural Terminology (CPT)-anesthesia combinations, including possible surgeon effects. For cases with very few observations, we investigated whether supplementing the data information with surgeons' prior guesses helps to obtain better duration estimates. Finally, we used best fitting duration distributions to simulate the potential efficiency gains in OR scheduling.

RESULTS

The 3-parameter lognormal distribution provides the best results for the case durations of CPT-anesthesia (surgeon) combinations, with an acceptable fit for almost 90% of the CPTs when segmented by the factor surgeon. The fit is best for surgical times and somewhat less for total procedure times. Surgeons' prior guesses are helpful for OR management to improve duration estimates of CPTs with very few (<10) observations. Compared with the standard way of case scheduling using the mean of the 3-parameter lognormal distribution for case scheduling reduces the mean overreserved OR time per case up to 11.9 (11.8-12.0) min (55.6%) and the mean underreserved OR time per case up to 16.7 (16.5-16.8) min (53.1%). When scheduling cases using the 4-parameter lognormal model the mean overutilized OR time is up to 20.0 (19.7-20.3) min per OR per day lower than for the standard method and 11.6 (11.3-12.0) min per OR per day lower as compared with the biased corrected mean.

CONCLUSIONS

OR case scheduling can be improved by using the 3-parameter lognormal model with surgeon effects and by using surgeons' prior guesses for rarely observed CPTs. Using the 3-parameter lognormal model for case-duration prediction and scheduling significantly reduces both the prediction error and OR inefficiency.

Automatic updating of times remaining in surgical cases using bayesian analysis of historical case duration data and "instant messaging" updates from anesthesia providers

BACKGROUND

Operating room (OR) whiteboards (status displays) communicate times remaining for ongoing cases to perioperative stakeholders (e.g., postanesthesia care unit, anesthesiologists, holding area, and control desks). Usually, scheduled end times are shown for each OR. However, these displays are inaccurate for predicting the time that remains in a case. Once a case scheduled for 2 h has been on-going for 1.5 h, the median time remaining is not 0.5 h but longer, and the amount longer differs among procedures.

METHODS

We derived the conditional Bayesian lower prediction bound of a case's duration, conditional on the minutes of elapsed OR time. Our derivations make use of the posterior predictive distribution of OR times following an exponential of a scaled Student t distribution that depends on the scheduled OR time and several parameters calculated from historical case duration data. The statistical method was implemented using Structured Query Language (SQL) running on the anesthesia information management system (AIMS) database server. In addition, AIMS workstations were sent instant messages displaying a pop-up dialog box asking for anesthesia providers' estimates for remaining times. The dialogs caused negotiated interruptions (i.e., the anesthesia provider could reply immediately, keep the dialog displayed, or defer response). There were no announcements, education, or efforts to promote buy-in.

RESULTS

After a case had been in the OR longer than scheduled, the median remaining OR time for the case changes little over time (e.g., 35 min left at 2:30 pm and also at 3:00 pm while the case was still on-going). However, the remaining time differs substantially among surgeons and scheduled procedure(s) (16 min longer [10th percentile], 35 min [50th], and 86 min [90th]). We therefore implemented an automatic method to estimate the times remaining in cases. The system was operational for >119 of each day's 120 5-min intervals. When instant message dialogs appearing on AIMS workstations were used to elicit estimates of times remaining from anesthesia providers, acknowledgment was on average within 1.2 min (95% confidence interval [CI] 1.1-1.3 min). The 90th percentile of latencies was 6.5 min (CI: 4.4-7.0 min).

CONCLUSIONS

For cases taking nearly as long as or longer than scheduled, each 1 min progression of OR time reduces the median time remaining in a case by <1 min. We implemented automated calculation of times remaining for every case at a 29 OR hospital.

Improving operating room efficiency by applying bin-packing and portfolio techniques to surgical case scheduling

BACKGROUND

An operating room (OR) department has adopted an efficient business model and subsequently investigated how efficiency could be further improved. The aim of this study is to show the efficiency improvement of lowering organizational barriers and applying advanced mathematical techniques.

METHODS

We applied advanced mathematical algorithms in combination with scenarios that model relaxation of various organizational barriers using prospectively collected data. The setting is the main inpatient OR department of a university hospital, which sets its surgical case schedules 2 wk in advance using a block planning method. The main outcome measures are the number of freed OR blocks and OR utilization.

RESULTS

Lowering organizational barriers and applying mathematical algorithms can yield a 4.5% point increase in OR utilization (95% confidence interval 4.0%-5.0%). This is obtained by reducing the total required OR time.

CONCLUSIONS

Efficient OR departments can further improve their efficiency. The paper shows that a radical cultural change that comprises the use of mathematical algorithms and lowering organizational barriers improves OR utilization.

A three-phase approach for operating theatre schedules

In this paper we develop a three-phase, hierarchical approach for the weekly scheduling of operating rooms. This approach has been implemented in one of the surgical departments of a public hospital located in Genova (Genoa), Italy. Our aim is to suggest an integrated way of facing surgical activity planning in order to improve overall operating theatre efficiency in terms of overtime and throughput as well as waiting list reduction, while improving department organization. In the first phase we solve a bin packing-like problem in order to select the number of sessions to be weekly scheduled for each ward; the proposed and original selection criterion is based upon an updated priority score taking into proper account both the waiting list of each ward and the reduction of residual ward demand. Then we use a blocked booking method for determining optimal time tables, denoted Master Surgical Schedule (MSS), by defining the assignment between wards and surgery rooms. Lastly, once the MSS has been determined we use the simulation software environment Witness 2004 in order to analyze different sequencings of surgical activities that arise when priority is given on the basis of a) the longest waiting time (LWT), b) the longest processing time (LPT) and c) the shortest processing time (SPT). The resulting simulation models also allow us to outline possible organizational improvements in surgical activity. The results of an extensive computational experimentation pertaining to the studied surgical department are here given and analyzed.

A Norm Utilisation for Scarce Hospital Resources: Evidence from Operating Rooms in a Dutch University Hospital

BACKGROUND

Utilisation of operating rooms is high on the agenda of hospital managers and researchers. Many efforts in the area of maximising the utilisation have been focussed on finding the holy grail of 100% utilisation. The utilisation that can be realised, however, depends on the patient mix and the willingness to accept the risk of working in overtime.

MATERIALS AND METHODS

This is a mathematical modelling study that investigates the association between the utilisation and the patient mix that is served and the risk of working in overtime. Prospectively, consecutively, and routinely collected data of an operating room department in a Dutch university hospital are used. Basic statistical principles are used to establish the relation between realistic utilisation rates, patient mixes, and accepted risk of overtime.

RESULTS

Accepting a low risk of overtime combined with a complex patient mix results a low utilisation rate. If the accepted risk of overtime is higher and the patient mix is less complex, the utilisation rate that can be reached is closer to 100%.

CONCLUSION

Because of the inherent variability of healthcare processes, the holy grail of 100% utilisation is unlikely to be found. The method proposed in this paper calculates a realistic benchmark utilisation that incorporates the patient mix characteristics and the willingness to accept risk of overtime.

Visualizing the demand for various resources as a function of the master surgery schedule: a case study

This paper presents a software system that visualizes the impact of the master surgery schedule on the demand for various resources throughout the rest of the hospital. The master surgery schedule can be seen as the engine that drives the hospital. Therefore, it is very important for decision makers to have a clear image on how the demand for resources is linked to the surgery schedule. The software presented in this paper enables schedulers to instantaneously view the impact of, e.g., an exchange of two block assignments in the master surgery schedule on the expected resource consumption pattern. A case study entailing a large Belgian surgery unit illustrates how the software can be used to assist in building better surgery schedules.

Applying the variety reduction principle to management of ancillary services

BACKGROUND

As central diagnostic facilities, computer tomography (CT) scans appear to be bottlenecks in many patient-care processes. This study describes a case study concerning redesign of a CT scan department in the Academic Medical Center in Amsterdam, the Netherlands.

PURPOSES

The aim was to decrease access time for the CT-scan and simultaneously increase utilization level.

METHODOLOGY/APPROACH

An important cause of relatively low-capacity utilization is variability in the time needed for the scanning process. We performed a qualitative and quantitative analysis of current processes; identified bottlenecks and selected interventions with the greatest expected reduction of variability in flow time.

FINDINGS

The most promising and most feasible opportunity appeared to be to reallocate the insertion of intravenous access lines to a preparation room. The time needed for this activity was very hard to predict and needed a lot of slack in the lead time for appointments. By removing it from the CT room, lead time could be reduced by 5 minutes. The intervention resulted in a decrease of access time from 21 days to less than 5 days, and an increase of the utilization rate from 44% to 51%. This contributed directly to patient service and indirectly to cost reduction.

PRACTICE IMPLICATIONS

Our strategy is applicable in every appointment-based hospital facility with variation in the length of time of the process. It allows to simultaneously reduce costs and improve service for the patient.

The Impact of Service-Specific Staffing, Case Scheduling, Turnovers, and First-Case Starts on Anesthesia Group and Operating Room Productivity: A Tutorial Using Data from an Australian Hospital

BACKGROUND

In this tutorial, we consider the impact of operating room (OR) management on anesthesia group and OR labor productivity and costs. Most of the tutorial focuses on the steps required for each facility to refine its OR allocations using its own data collected during patient care.

METHODS

Data from a hospital in Australia are used throughout to illustrate the methods. OR allocation is a two-stage process. During the initial tactical stage of allocating OR time, OR capacity ("block time") is adjusted. For operational decision-making on a shorter-term basis, the existing workload can be considered fixed. Staffing is matched to that workload based on maximizing the efficiency of use of OR time.

RESULTS

Scheduling cases and making decisions on the day of surgery to increase OR efficiency are worthwhile interventions to increase anesthesia group productivity. However, by far, the most important step is the appropriate refinement of OR allocations (i.e., planning service-specific staffing) 2-3 mo before the day of surgery.

CONCLUSIONS

Reducing surgical and/or turnover times and delays in first-case-of-the-day starts generally provides small reductions in OR labor costs. Results vary widely because they are highly sensitive both to the OR allocations (i.e., staffing) and to the appropriateness of those OR allocations.

Estimating the duration of common elective operations: implications for operating list management

Over-running operating lists are known to be a common cause of cancellation of operations on the day of surgery. We investigated whether lists were overbooked because surgeons were optimistic in their estimates of the time that operations would take to complete. We used a questionnaire to assess the estimates of total operation time of 22 surgeons, 35 anaesthetists and 16 senior nursing staff for 31 common, general surgical and urological procedures. The response rate was 66%. We found no difference between the estimates of these three groups of staff, or between these estimates and times obtained from theatre computer records (p = 0.722). We then applied the average of the surgeons' estimates prospectively to 50 consecutive published surgical lists. Surgical estimates were very accurate in predicting the actual duration of the list (r2= 0.61; p < 0.001), but were poor at booking the list to within its scheduled duration: 50% of lists were predictably overbooked, 50% over-ran their scheduled time, and 34% of lists suffered a cancellation. We suggest that using the estimates of operating times to plan lists would reduce the incidence of predictable over-runs and cancellations.