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

Improving case duration accuracy of orthopedic surgery using bidirectional encoder representations from Transformers (BERT) on Radiology Reports

PURPOSE

A major source of inefficiency in the operating room is the mismatch between scheduled versus actual surgical time. The purpose of this study was to demonstrate a proof-of-concept study for predicting case duration by applying natural language processing (NLP) and machine learning that interpret radiology reports for patients undergoing radius fracture repair.

METHODS

Logistic regression, random forest, and feedforward neural networks were tested without NLP and with bag-of-words. Another NLP method tested used feedforward neural networks and Bidirectional Encoder Representations from Transformers specifically pre-trained on clinical notes (ClinicalBERT). A total of 201 cases were included. The data were split into 70% training and 30% test sets. The average root mean squared error (RMSE) were calculated (and 95% confidence interval [CI]) from 10-fold cross-validation on the training set. The models were then tested on the test set to determine proportion of times surgical cases would have scheduled accurately if ClinicalBERT was implemented versus historic averages.

RESULTS

The average RMSE was lowest using feedforward neural networks using outputs from ClinicalBERT (25.6 min, 95% CI: 21.5-29.7), which was significantly (P < 0.001) lower than the baseline model (39.3 min, 95% CI: 30.9-47.7). Using the feedforward neural network and ClinicalBERT on the test set, the percentage of accurately predicted cases, which was defined by the actual surgical duration within 15% of the predicted surgical duration, increased from 26.8 to 58.9% (P < 0.001).

CONCLUSION

This proof-of-concept study demonstrated the successful application of NLP and machine leaning to extract features from unstructured clinical data resulting in improved prediction accuracy for surgical case duration.

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.

On the use of partitioning for scheduling of surgeries in the inpatient surgical department

In hospitals, the efficient planning of the operating rooms (ORs) is difficult due to the uncertainty inherent to surgical services. This is especially true for the inpatient surgical department where complex and long surgeries are often performed along with surgeries on emergency patients. This paper aims to improve the scheduling of the inpatient department by partitioning the elective surgeries into the more predictable surgeries (MPS) group and the less predictable surgeries (LPS) group, based on surgery duration variability, and by scheduling each of the two surgery groups in different ORs. Through a simulation study that comprehensively investigates the impact of the partitioning on different performance measures under various environmental settings, we report important findings and insights. First, partitioning can effectively shorten the waiting times of elective patients for both MPS and LPS groups, but the option should be allowed to reassign patients from the MPS or LPS ORs to the other ORs when needed. Meanwhile, partitioning sometimes slightly increases the elective cancellation rate. Second, the ability to use the available capacity of the ORs as much as possible is key to reducing elective waiting times. Third, partitioning might slightly worsen the waiting times of emergency patients, while the slightly negative impact on emergency patients decreases when the number of ORs is higher. Fourth, the beneficial impact of partitioning on elective patients increases with an increased patient demand. Last, for the settings considered in this study there was no benefit in partitioning the elective patients into more than two groups.

Allocation scheduling leads to optimum utilization of operation theater time

Background

Cancellation of surgeries is a regular phenomenon in any hospital, and reasons may vary from clinical to managerial ones. The aim of the study is to suggest scheduling to address the problem of time over run related cancellations. This is an observational and descriptive study conducted in a tertiary care hospital with ophthalmology facilities. The sample size is calculated with 95% confidence interval using Epi Info 6 from the total surgeries performed in the last 5 years (n = 380). Simple random sampling technique was used.

Methods

Surgical time for all types of ophthalmic surgeries (n = 582) was observed. Allocation of listed cases to the available operating rooms (ORs) was carried out using the observed time using LEKIN software.

Results

The time over-run of 2 h and 6 h was noted for two units, whereas idle OR time was observed in other units. An average idle time of 19% was noted on each day. Reallocation of the cases to the ORs was carried out taking all the planned cases (of both the operating units of the day) as the number of jobs and all the available ORs as parallel machines using LEKIN software. All the planned cases could be accommodated; still, an average of 17% of the total available operation theater (OT) time was found idle on each day.

Conclusions

Planning of cases using procedure time and scheduling on a daily basis using allocation models with simple algorithms can provide optimal utilization of OTs and can address the time over-run and related cancellations.

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.

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.

Futility of Cluster Designs at Individual Hospitals to Study Surgical Site Infections and Interventions Involving the Installation of Capital Equipment in Operating Rooms

Anesthesia workspaces are integral components in the chains of many intraoperative bacterial transmission events resulting in surgical site infections (SSI). Matched cohort designs can be used to compare SSI rates among operating rooms (ORs) with or without capital equipment purchases (e.g., new anesthesia machines). Patients receiving care in intervention ORs (i.e., with installed capital equipment) are matched with similar patients receiving care in ORs lacking the intervention. We evaluate statistical power of an alternative design for clinical trials in which, instead, SSI incidences are compared directly among ORs (i.e., the ORs form the clusters) at single hospitals (e.g., the 5 ORs with bactericidal lights vs. the 5 other ORs). Data used for parameter estimates were SSI for 24 categories of procedures among 338 hospitals in the State of California, 2015. Estimated statistical power was ≅8.4% for detecting a reduction in the incidence of SSI from 3.6% to 2.4% over 1 year with 5 intervention ORs and 5 control ORs. For ≅80% statistical power, >20 such hospitals would be needed to complete a study in 1 year. Matched paired cluster designs pair similar ORs (e.g., 2 cardiac ORs, 1 to intervention and 1 to control). With 5 pairs, statistical power would be even less than the estimated 8.4%. Cluster designs (i.e., analyses by OR) are not suitable for comparing SSI among ORs at single hospitals. Even though matched cohort designs are non-randomized and thus have lesser validity, matching patients by their risk factors for SSI is more practical.

Machine Learning Can Improve Estimation of Surgical Case Duration: A Pilot Study

Operating room (OR) utilization is a significant determinant of hospital profitability. One aspect of this is surgical scheduling, which depends on accurate predictions of case duration. This has been done historically by either the surgeon based on personal experience, or by an electronic health record (EHR) based on averaged historical means for case duration. Here, we compare the predicted case duration (pCD) accuracy of a novel machine-learning algorithm over a 3-month period. A proprietary machine learning algorithm was applied utilizing operating room factors such as patient demographic data, pre-surgical milestones, and hospital logistics and compared to that of a conventional EHR. Actual case duration and pCD (Leap Rail vs EHR) was obtained at one institution over the span of 3 months. Actual case duration was defined as time between patient entry into an OR and time of exit. pCD was defined as case time allotted by either Leap Rail or EHR. Cases where Leap Rail was unable to generate a pCD were excluded. A total of 1059 surgical cases were performed during the study period, with 990 cases being eligible for the study. Over all sub-specialties, Leap Rail showed a 7 min improvement in absolute difference between pCD and actual case duration when compared to conventional EHR (p < 0.0001). In aggregate, the Leap Rail method resulted in a 70% reduction in overall scheduling inaccuracy. Machine-learning algorithms are a promising method of increasing pCD accuracy and represent one means of improving OR planning and efficiency.

Procedural Portfolio Planning in Plastic Surgery, Part 1: Strategic Changes in Clinical Practice to Increase Physician Revenue, Improve Operative Throughput, and Maintain Patient Satisfaction

PURPOSE

Portfolio planning in health care represents the strategic prioritization of services that permits an organization to better achieve its goals of margin and mission. Because of recent volatility in the economy, declining reimbursement, and rising costs of providing care, such strategic planning has become increasingly important if physicians want to remain leaders in health care. This project assesses the financial impact of procedural portfolio planning on an academic plastic surgery practice from the physician's perspective.

METHODS

We tracked the top 50 procedures, defined as total charges per CPT code, that were performed in our baseline year, for 6 providers in a stable plastic surgery practice. At the end of the first year, we implemented 3 types of strategic changes: growth of areas with high contribution margin (laser resurfacing of burn scars), curtailment of high-risk procedures with negative contribution margin (panniculectomy in smokers), and improved efficiency of mission-critical services with high resource consumption (free-flap breast reconstruction). During the 2-year study period, we had no turnover in faculty, did not pursue any formal marketing, did not change our surgical fees or billing system, provided care independent of payer mix, and maintained our commitment to indigent care. Outcome measures included procedural charges and revenue, collection rates, work relative value units, operating room times, idle times (room time less case time), receipts/minute in operating room, uncompensated charity care, and patient satisfaction (Press-Gainey scores). Before the study period, annual incremental growth in our practice was 1% to 2%, in terms of charges and receipts.

RESULTS

After implementation of the portfolio planning project, the financial position of our division improved significantly, with patient satisfaction rates increasing from 85.5% to 94.1% and charity care remaining constant at US $400,000 per year. Encounters, work relative value units, charges, and receipts all increased by 16% to 27%, with receipts/minute increasing from US $5.60 per minute to US $7.28 per minute. Interestingly, but not surprisingly, highest margin cases did not correspond with highest volume or highest revenue cases; portfolio analysis helped us to align these parameters, without sacrificing patient satisfaction or commitment to indigent care. The highest receipt/minute procedure was laser ablation of vascular lesions (US $23.87), whereas one of the lowest receipt/minute cases was muscle free flap (US $3.07).

CONCLUSIONS

Procedural portfolio analysis is a powerful tool that can guide strategy and positively impact the financial position and clinical value of the services provided by an academic plastic surgery practice. Identifying high margin procedures allows the surgeon to focus marketing efforts, target areas of future growth, and optimize the blend of margin and mission.

Procedural Portfolio Planning in Plastic Surgery, Part 2: Collaboration Between Surgeons and Hospital Administrators to Develop a Funds Flow Model for Procedures Performed at an Academic Medical Center

INTRODUCTION

Although plastic surgeons make important contributions to the clinical, educational, and research missions of academic medical centers (AMCs), determining the financial value of a plastic surgery service can be difficult, due to complex cost accounting systems. We analyzed the financial impact of plastic surgery on an AMC, by examining the contribution margins and operating income of surgical procedures.

METHODS

We collaborated with hospital administrators to implement 3 types of strategic changes: (1) growth of areas with high contribution margin, (2) curtailment of high-risk procedures with negative contribution margin, (3) improved efficiency of mission-critical services with high resource consumption. Outcome measures included: facility charges, hospital collections, contribution margin, operating margin, and operating room times. We also studied the top 50 Current Procedural Terminology codes (total case number × charge/case), ranking procedures for profitability, as determined by operating margin. During the 2-year study period, we had no turnover in faculty; did not pursue any formal marketing; did not change our surgical fees, billing system, or payer mix; and maintained our commitment to indigent care.

RESULTS

After rebalancing our case mix, through procedural portfolio planning, average hospital operating income/procedure increased from $-79 to $+816. Volume and diversity of cases increased, with no change in payer mix. Although charges/case decreased, both contribution margin and operating margin increased, due to improved throughput and decreased operating room times. The 5 most profitable procedures for the hospital were hernia repair, mandibular osteotomy, hand skin graft, free fibula flap, and head and neck flap, whereas the 5 least profitable were latissimus breast reconstruction, craniosynostosis repair, free-flap breast reconstruction, trunk skin graft, and cutaneous free flap. Total operating income for the hospital, from plastic surgery procedures, increased from $-115,103 to $+1,277,040, of which $350,000 (25%) was returned to the practice plan as enterprise funds to support program development.

CONCLUSIONS

Through focused strategic initiatives, plastic surgeons and hospital administrators can work together to unlock the latent value of a plastic surgery service to an AMC. Specific financial benefits to the hospital include increased contribution margin and operating income, the latter of which can be reinvested in the plastic surgery service through a gain-sharing model.

Operational strategies to manage non-elective orthopaedic surgical flows: a simulation modelling study

OBJECTIVES

To explore the value of simulation modelling in evaluating the effects of strategies to plan and schedule operating room (OR) resources aimed at reducing time to surgery for non-elective orthopaedic inpatients at a Swedish hospital.

METHODS

We applied discrete-event simulation modelling. The model was populated with real world data from a university hospital with a strong focus on reducing waiting time to surgery for patients with hip fracture. The system modelled concerned two patient groups that share the same OR resources: hip-fracture and other non-elective orthopaedic patients in need of surgical treatment. We simulated three scenarios based on the literature and interaction with staff and managers: (1) baseline; (2) reduced turnover time between surgeries by 20 min and (3) one extra OR during the day, Monday to Friday. The outcome variables were waiting time to surgery and the percentage of patients who waited longer than 24 hours for surgery.

RESULTS

The mean waiting time in hours was significantly reduced from 16.2 hours in scenario 1 (baseline) to 13.3 hours in scenario 2 and 13.6 hours in scenario 3 for hip-fracture surgery and from 26.0 hours in baseline to 18.9 hours in scenario 2 and 18.5 hours in scenario 3 for other non-elective patients. The percentage of patients who were treated within 24 hours significantly increased from 86.4% (baseline) to 96.1% (scenario 2) and 95.1% (scenario 3) for hip-fracture patients and from 60.2% (baseline) to 79.8% (scenario 2) and 79.8% (scenario 3) for patients with other non-elective patients.

CONCLUSIONS

Healthcare managers who strive to improve the timelines of non-elective orthopaedic surgeries may benefit from using simulation modelling to analyse different strategies to support their decisions. In this specific case, the simulation results showed that the reduction of surgery turnover times could yield the same results as an extra OR.

Scheduling elective surgeries: the tradeoff among bed capacity, waiting patients and operating room utilization using goal programming

Scheduling of surgeries in the operating rooms under limited competing resources such as surgical and nursing staff, anesthesiologist, medical equipment, and recovery beds in surgical wards is a complicated process. A well-designed schedule should be concerned with the welfare of the entire system by allocating the available resources in an efficient and effective manner. In this paper, we develop an integer linear programming model in a manner useful for multiple goals for optimally scheduling elective surgeries based on the availability of surgeons and operating rooms over a time horizon. In particular, the model is concerned with the minimization of the following important goals: (1) the anticipated number of patients waiting for service; (2) the underutilization of operating room time; (3) the maximum expected number of patients in the recovery unit; and (4) the expected range (the difference between maximum and minimum expected number) of patients in the recovery unit. We develop two goal programming (GP) models: lexicographic GP model and weighted GP model. The lexicographic GP model schedules operating rooms when various preemptive priority levels are given to these four goals. A numerical study is conducted to illustrate the optimal master-surgery schedule obtained from the models. The numerical results demonstrate that when the available number of surgeons and operating rooms is known without error over the planning horizon, the proposed models can produce good schedules and priority levels and preference weights of four goals affect the resulting schedules. The results quantify the tradeoffs that must take place as the preemptive-weights of the four goals are changed.

Studying and Incorporating Efficiency into Gastrointestinal Endoscopy Centers

Efficiency is defined as the use of resources in such a way as to maximize the production of goods and services. Improving efficiency has been the focus of management in many industries; however, it has not been until recently that incorporating efficiency models into healthcare has occurred. In particular, the study and development of improvement projects aimed at enhancing efficiency in GI have been growing rapidly in recent years. This focus on improving efficiency in GI has been spurred by the dramatic rise in the demand for endoscopic procedures as well as the rising number of insured patients requiring GI care coupled at the same time with limited resources in terms of staffing and space in endoscopy centers. This paper will critically review the history of efficiency in endoscopy centers, first by looking at other healthcare industries that have extensively studied and improved efficiency in their fields, examine a number of proposed efficiency metrics and benchmarks in endoscopy centers, and finally discuss opportunities where endoscopy centers could improve their efficiency.

Enhancement opportunities in operating room utilization; with a statistical appendix

BACKGROUND

The purpose of this study was to assess the direct and indirect relationships between first-case tardiness (or "late start"), turnover time, underused operating room (OR) time, and raw utilization, as well as to determine which indicator had the most negative impact on OR utilization to identify improvement potential. Furthermore, we studied the indirect relationships of the three indicators of "nonoperative" time on OR utilization, to recognize possible "trickle down" effects during the day.

MATERIALS AND METHODS

(Multiple) linear regression analysis and mediation effect analysis were applied to a data set from all eight University Medical Centers in the Netherlands. This data set consisted of 190,071 OR days (on which 623,871 surgical cases were performed).

RESULTS

Underused OR time at the end of the day had the strongest influence on raw utilization, followed by late start and turnover time. The relationships between the three "nonoperative" time indicators were negligible. The impact of the partial indirect effects of "nonoperative" time indicators on raw utilization were statistically significant, but relatively small. The "trickle down" effect that late start can cause resulting in an increased delay as the day progresses, was not supported by our results.

CONCLUSIONS

The study findings clearly suggest that OR utilization can be improved by focusing on the reduction of underused OR time at the end of the day. Improving the prediction of total procedure time, improving OR scheduling by, for example, altering the sequencing of operations, changing patient cancellation policies, and flexible staffing of ORs adjusted to patient needs, are means to reduce "nonoperative" time.

Optimizing efficiency and operations at a California safety-net endoscopy center: a modeling and simulation approach

BACKGROUND

Improvements in endoscopy center efficiency are needed, but scant data are available.

OBJECTIVE

To identify opportunities to improve patient throughput while balancing resource use and patient wait times in a safety-net endoscopy center.

SETTING

Safety-net endoscopy center.

PATIENTS

Outpatients undergoing endoscopy.

INTERVENTION

A time and motion study was performed and a discrete event simulation model constructed to evaluate multiple scenarios aimed at improving endoscopy center efficiency.

MAIN OUTCOME MEASUREMENTS

Procedure volume and patient wait time.

RESULTS

Data were collected on 278 patients. Time and motion study revealed that 53.8 procedures were performed per week, with patients spending 2.3 hours at the endoscopy center. By using discrete event simulation modeling, a number of proposed changes to the endoscopy center were assessed. Decreasing scheduled endoscopy appointment times from 60 to 45 minutes led to a 26.4% increase in the number of procedures performed per week, but also increased patient wait time. Increasing the number of endoscopists by 1 each half day resulted in increased procedure volume, but there was a concomitant increase in patient wait time and nurse utilization exceeding capacity. By combining several proposed scenarios together in the simulation model, the greatest improvement in performance metrics was created by moving patient endoscopy appointments from the afternoon to the morning. In this simulation at 45- and 40-minute appointment times, procedure volume increased by 30.5% and 52.0% and patient time spent in the endoscopy center decreased by 17.4% and 13.0%, respectively. The predictions of the simulation model were found to be accurate when compared with actual changes implemented in the endoscopy center.

LIMITATIONS

Findings may not be generalizable to non-safety-net endoscopy centers.

CONCLUSIONS

The combination of minor, cost-effective changes such as reducing appointment times, minimizing and standardizing recovery time, and making small increases in preprocedure ancillary staff maximized endoscopy center efficiency across a number of performance metrics.

Allocating operating room block time using historical caseload variability

Operating room (OR) allocation and planning is one of the most important strategic decisions that OR managers face. The number of ORs that a hospital opens depends on the number of blocks that are allocated to the surgical groups, services, or individual surgeons, combined with the amount of open posting time (i.e., first come, first serve posting) that the hospital wants to provide. By allocating too few ORs, a hospital may turn away surgery demand whereas opening too many ORs could prove to be a costly decision. The traditional method of determining block frequency and size considers the average historical surgery demand for each group. However, given that there are penalties to the system for having too much or too little OR time allocated to a group, demand variability should play a role in determining the real OR requirement. In this paper we present an algorithm that allocates block time based on this demand variability, specifically accounting for both over-utilized time (time used beyond the block) and under-utilized time (time unused within the block). This algorithm provides a solution to the situation in which total caseload demand can be accommodated by the total OR resource set, in other words not in a capacity-constrained situation. We have found this scenario to be common among several regional healthcare providers with large OR suites and excess capacity. This algorithm could be used to adjust existing blocks or to assign new blocks to surgeons that did not previously have a block. We also have studied the effect of turnover time on the number of ORs that needs to be allocated. Numerical experiments based on real data from a large health-care provider indicate the opportunity to achieve over 2,900 hours of OR time savings through improved block allocations.

The surgical scorecard: a tool to close the loop on OR utilization

OBJECTIVE

Operating room (OR) suites are among the highest cost- and highest revenue-generating areas in most hospitals. A scorecard containing utilization and performance metrics for each surgical service and surgeon was designed by the OR leadership with results sent monthly to each surgical chief. Recent trends reveal an increased focus on optimizing utilization of OR resources as part of institutional cost-analysis efforts. Protected block time into which elective surgical and procedural cases can be booked must be used appropriately and booked fully to offset the fixed costs of staffing and running the OR.

DESIGN AND SETTING

The intent of the scorecard tool was to provide detailed information on utilization of protected block time for performance-improvement planning. First-case on-time start was also measured and reported so that block time at the start of the day was fully utilized. With the granular information on time-use performance of each surgeon, the surgical chiefs were able to make workflow changes to improve utilization of staffed prime-time block hours. The scorecard tool is used ultimately for communication, not calculation, of utilization metrics. MEASUREMENTS AND CONCLUSIONS: Block-time utilization was measured both before and after the implementation of the scorecard. The analysis of the period before and after implementation of the scorecard revealed an improvement in block-time utilization in all but 1 surgical service.

Estimating the cost of no-shows and evaluating the effects of mitigation strategies

OBJECTIVE

To measure the cost of nonattendance ("no-shows") and benefit of overbooking and interventions to reduce no-shows for an outpatient endoscopy suite.

METHODS

We used a discrete-event simulation model to determine improved overbooking scheduling policies and examine the effect of no-shows on procedure utilization and expected net gain, defined as the difference in expected revenue based on Centers for Medicare & Medicaid Services reimbursement rates and variable costs based on the sum of patient waiting time and provider and staff overtime. No-show rates were estimated from historical attendance (18% on average, with a sensitivity range of 12%-24%). We then evaluated the effectiveness of scheduling additional patients and the effect of no-show reduction interventions on the expected net gain.

RESULTS

The base schedule booked 24 patients per day. The daily expected net gain with perfect attendance is $4433.32. The daily loss attributed to the base case no-show rate of 18% is $725.42 (16.4% of net gain), ranging from $472.14 to $1019.29 (10.7%-23.0% of net gain). Implementing no-show interventions reduced net loss by $166.61 to $463.09 (3.8%-10.5% of net gain). The overbooking policy of 9 additional patients per day resulted in no loss in expected net gain when compared with the reference scenario.

CONCLUSIONS

No-shows can significantly decrease the expected net gain of outpatient procedure centers. Overbooking can help mitigate the impact of no-shows on a suite's expected net gain and has a lower expected cost of implementation to the provider than intervention strategies.

RFID-initiated workflow control to facilitate patient safety and utilization efficiency in operation theater

OBJECTIVE

To control the workflow for surgical patients, we in-cooperate radio-frequency identification (RFID) technology to develop a Patient Advancement Monitoring System (PAMS) in operation theater.

METHODS

The web-based PAMS is designed to monitor the whole workflow for the handling of surgical patients. The system integrates multiple data entry ports Across the multi-functional surgical teams. Data are entered into the system through RFID, bar code, palm digital assistance (PDA), ultra-mobile personal computer (UMPC), or traditional keyboard at designated checkpoints. Active radio-frequency identification (RFID) tag can initiate data demonstration on the computer screens upon a patient's arrival at any particular checkpoint along the advancement pathway.

RESULTS

The PAMS can manage the progress of operations, patient localization, identity verification, and peri-operative care. The workflow monitoring provides caregivers' instant information sharing to enhance management efficiency.

CONCLUSION

RFID-initiate surgical workflow control is valuable to meet the safety, quality, efficiency requirements in operation theater.

How to juggle priorities? An interactive tool to provide quantitative support for strategic patient-mix decisions: an ophthalmology case

An interactive tool was developed for the ophthalmology department of the Academic Medical Center to quantitatively support management with strategic patient-mix decisions. The tool enables management to alter the number of patients in various patient groups and to see the consequences in terms of key performance indicators. In our case study, we focused on the bottleneck: the operating room. First, we performed a literature review to identify all factors that influence an operating room's utilization rate. Next, we decided which factors were relevant to our study. For these relevant factors, two quantitative methods were applied to quantify the impact of an individual factor: regression analysis and computer simulation. Finally, the average duration of an operation, the number of cancellations due to overrun of previous surgeries, and the waiting time target for elective patients all turned out to have significant impact. Accordingly, for the case study, the interactive tool was shown to offer management quantitative decision support to act proactively to expected alterations in patient-mix. Hence, management can anticipate the future situation, and either alter the expected patient-mix or expand capacity to ensure that the key performance indicators will be met in the future.

Planning and scheduling of semi-urgent surgeries

This paper investigates the trade-off between cancellations of elective surgeries due to semi-urgent surgeries, and unused operating room (OR) time due to excessive reservation of OR time for semi-urgent surgeries. Semi-urgent surgeries, to be performed soon but not necessarily today, pose an uncertain demand on available hospital resources, and interfere with the planning of elective patients. For a highly utilized OR, reservation of OR time for semi-urgent surgeries avoids excessive cancellations of elective surgeries, but may also result in unused OR time, since arrivals of semi-urgent patients are unpredictable. First, using a queuing theory framework, we evaluate the OR capacity needed to accommodate every incoming semi-urgent surgery. Second, we introduce another queuing model that enables a trade-off between the cancelation rate of elective surgeries and unused OR time. Third, based on Markov decision theory, we develop a decision support tool that assists the scheduling process of elective and semi-urgent surgeries. We demonstrate our results with actual data obtained from a department of neurosurgery.

Elective surgical case cancellation in the Veterans Health Administration system: identifying areas for improvement

BACKGROUND

This study evaluated elective surgical case cancellation (CC) rates, reasons for these cancellations, and identified areas for improvement within the Veterans Health Administration (VA) system.

METHODS

CC data for 2006 were collected from the scheduling software for 123 VA facilities. Surveys were distributed to 40 facilities (10 highest and 10 lowest CC rates for high- and low-volume facilities). CC reasons were standardized and piloted at 5 facilities.

RESULTS

Of 329,784 cases scheduled by 9 surgical specialties, 40,988 (12.4%) were cancelled. CC reasons (9,528) were placed into 6 broad categories: patient (35%), work-up/medical condition change (28%), facility (20%), surgeon (8%), anesthesia (1%), and miscellaneous (8%). Survey results show areas for improvement at the facility level and a standardized list of 28 CC reasons was comprehensive.

CONCLUSIONS

Interventions that decrease cancellations caused by patient factors, inadequate work-up, and facility factors are needed to reduce overall elective surgical case cancellations.

A discrete event simulation model to evaluate operational performance of a colonoscopy suite

BACKGROUND AND AIMS

Colorectal cancer, a leading cause of cancer death, is preventable with colonoscopic screening. Colonoscopy cost is high, and optimizing resource utilization for colonoscopy is important. This study's aim is to evaluate resource allocation for optimal use of facilities for colonoscopy screening.

METHOD

The authors used data from a computerized colonoscopy database to develop a discrete event simulation model of a colonoscopy suite. Operational configurations were compared by varying the number of endoscopists, procedure rooms, the patient arrival times, and procedure room turnaround time. Performance measures included the number of patients served during the clinic day and utilization of key resources. Further analysis included considering patient waiting time tradeoffs as well as the sensitivity of the system to procedure room turnaround time.

RESULTS

The maximum number of patients served is linearly related to the number of procedure rooms in the colonoscopy suite, with a fixed room to endoscopist ratio. Utilization of intake and recovery resources becomes more efficient as the number of procedure rooms increases, indicating the potential benefits of large colonoscopy suites. Procedure room turnaround time has a significant influence on patient throughput, procedure room utilization, and endoscopist utilization for varying ratios between 1:1 and 2:1 rooms per endoscopist. Finally, changes in the patient arrival schedule can reduce patient waiting time while not requiring a longer clinic day.

CONCLUSIONS

Suite managers should keep a procedure room to endoscopist ratio between 1:1 and 2:1 while considering the utilization of related key resources as a decision factor as well. The sensitivity of the system to processes such as turnaround time should be evaluated before improvement efforts are made.