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

Plan-do-study-act (PDSA) interventions to improve real-world endoscopy unit productivity

Background and study aims The Plan-Do-Study Act (PDSA) ramp is a framework that uses initial small changes to build consensus and momentum for subsequent, iterative process improvement. Our aim was to study its impact on endoscopy unit efficiency and throughput. Methods Following a granular time-and-motion analysis to evaluate baseline performance (phase 1) we instituted successive interventions and measured their impact on core efficiency metrics including procedure volume and turnover time (phases 2-3). Results We identified that inefficiency in turnover of anesthesia-supported endoscopy was the most crucial issue. Implementation of a pre-procedure anesthesia visit in phase 2 reduced turnover time by 15.5 minutes (95% confidence interval 3.9-27.1 minutes). Subsequent changes (phase 3) including front-loaded procedure scheduling and parallel in-room preparation resulted in an 18% increase in procedure volume. Conclusions The PDSA ramp model is an effective means of assessing operational processes, developing novel interventions, and building consensus to improve the real-world productivity in a resource-conscious manner.

Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources

A healthcare resource allocation generally plays a vital role in the number of patients treated (p_nt) and the patient waiting time (w_t) in healthcare institutions. This study aimed to estimate p_nt and w_t as output variables by considering the number of healthcare resources employed and analyze the cost of health resources to the hospital depending on the cost coefficient (δ_i) in an emergency department (ED). The integration of the discrete-event simulation (DES) model and machine learning (ML) algorithms, namely random forest (RF), gradient boosting (GB), and AdaBoost (AB), was used to calculate the estimation of the output variables depending on the δ_i of resources cost. The AB algorithm performed best in almost all scenarios based on the results of the analysis. According to the AB algorithm based on the δ_0.0, δ_0.1, δ_0.2, and δ_0.3, the accuracy data were calculated as 0.9838, 0.9843, 0.9838, and 0.9846 for p_nt; 0.9514, 0.9517, 0.9514, and 0.9514 for w_t, respectively in the training stage. The GB algorithm had the best performance value, except for the results of the δ_0.2 (AB had a better accuracy at 0.8709 based on the value of δ_0.2 for p_nt) in the test stage. According to the AB algorithm based on the δ_0.0, δ_0.1, δ_0.2, and δ_0.3, the accuracy data were calculated as 0.7956, 0.9298, 0.8288, and 0.7394 for p_nt; 0.8820, 0.8821, 0.8819, and 0.8818 for w_t in the training phase, respectively. All scenarios created by the δ_i coefficient should be preferred for ED since the income provided by the p_nt value to the hospital was more than the cost of healthcare resources. On the contrary, the w_t estimation results of ML algorithms based on the δ_i coefficient differed. Although w_t values in all ML algorithms with δ_0.0 and δ_0.1 coefficients reduced the cost of the hospital, w_t values based on δ_0.2 and δ_0.3 increased the cost of the hospital.

A critical review of implementing lean and simulation to improve resource utilization and patient experience in outpatient clinics

Purpose

The purpose of this paper is to explore and critically review the existing literature on applications of Lean Methodology (LM) and Discrete-Event Simulation (DES) to improve resource utilization and patient experience in outpatient clinics. In doing, it is aimed to identify how to implement LM in outpatient clinics and discuss the advantages of integrating both lean and simulation tools towards achieving the desired outpatient clinics outcomes.

Design/methodology/approach

A theoretical background of LM and DES to define a proper implementation approach is developed. The search strategy of available literature on LM and DES used to improve outpatient clinic operations is discussed. Bibliometric analysis to identify patterns in the literature including trends, associated frameworks, DES software used, and objective and solutions implemented are presented. Next, an analysis of the identified work offering critical insights to improve the implementation of LM and DES in outpatient clinics is presented.

Findings

Critical analysis of the literature on LM and DES reveals three main obstacles hindering the successful implementation of LM and DES. To address the obstacles, a framework that integrates DES with LM has been recommended and proposed. The paper provides an example of such a framework and identifies the role of LM and DES towards improving the performance of their implementation in outpatient clinics.

Originality/value

This study provides a critical review and analysis of the existing implementation of LM and DES. The current roadblocks hindering LM and DES from achieving their expected potential has been identified. In addition, this study demonstrates how LM with DES combined to achieve the desired outpatient clinic objectives.

Impact of the SARS-CoV-2 pandemic on turnover time and revenue in the endoscopy unit: single-center experience

Background and study aims  The SARS-CoV-2 pandemic heavily impacted the New York metro area causing most institutions to either reduce case volume or fully close remaining open units incorporated specific guidelines for procedures lockdown potentially leading to a greater turn-over time. We analyzed the quantitative and financial impact of this lost time on our tertiary care center's endoscopy unit. Patients and methods  This single-center, retrospective study included demographics, procedure details and turn-over times (TOT) from all endoscopic procedures between December 1, 2019 to June 30, 2020. Cases were categorized as pre-COVID-19 group from December 1, 2019 to March 15, 2020 and during COVID-19 (lockdown) group from March 16, 2020 to June 30, 2020. The financial impact was assessed using national averages for reimbursement of outpatient endoscopic procedures provided by Center for Medicare and Medicaid Services. Results  A total of 3622 procedures were performed during the study period: 2297 in the pre-COVID-19 period, 1325 in the COVID-19 period, representing a 42.32 % decrease. In the COVID-19 lockdown group, there was a significant increase in TOT in both the general endoscopy cases (18.11 minutes, P  = 0.000) and advanced endoscopy cases (17.7 minutes, P  = 0.000). The 42.3 % decrease in volume equated to at least $1.6 million USD in lost revenue during the lockdown. Conclusions  COVID-19 pandemic led to an increase in TOT with overall reduced procedure volume and a negative effect on revenue. Providing continued endoscopic management during a pandemic avoids delays in reopening the endoscopy unit and hampers the post-pandemic surge of delayed cases and its financial impact.

A discrete-event simulation model for analysing and improving operations in a blood donation centre

Background and objectives

Healthcare systems require effective and efficient blood donation supply chains to provide an adequate amount of whole blood and blood components to hospitals and transfusion centres. However, some crucial steps of the chain, for example blood collection, are not adequately studied in the literature. This work analyses the operations in a blood collection centre with the twofold aim of analysing different configurations and evaluating the effectiveness and feasibility of schedules defined at higher planning levels.

Materials and methods

The analyses are performed through a discrete event simulation (DES) model that describes a customizable collection centre. Moreover, a feedback loop from the DES to the higher planning level allows to adjust scheduling decisions if they determine criticalities or infeasibilities at the operational level.

Results

Numerical tests have been conducted considering a real Italian provider. An experimental plan has been designed to compare different configurations for the blood collection centre and evaluate the best ones in terms of cost and service quality for the three main actors involved (donors, workers and managers). The best configurations have been also used to test the feedback loop.

Conclusions

Results confirm the appropriateness of the proposed DES model, which can be considered a useful decision support tool for dimensioning and managing a blood collection centre, either as a standalone tool or in conjunction with a scheduler.

Identifying Areas for Operational Improvement and Growth in IR Workflow Using Workflow Modeling, Simulation, and Optimization Techniques

Identifying areas for workflow improvement and growth is essential for an interventional radiology (IR) department to stay competitive. Deployment of traditional methods such as Lean and Six Sigma helped in reducing the waste in workflows at a strategic level. However, achieving efficient workflow needs both strategic and tactical approaches. Uncertainties about patient arrivals, staff availability, and variability in procedure durations pose hindrances to efficient workflow and lead to delayed patient care and staff overtime. We present an alternative approach to address both tactical and strategic needs using discrete event simulation (DES) and simulation based optimization methods. A comprehensive digital model of the patient workflow in a hospital-based IR department was modeled based on expert interviews with the incumbent personnel and analysis of 192 days' worth of electronic medical record (EMR) data. Patient arrival patterns and process times were derived from 4393 individual patient appointments. Exactly 196 unique procedures were modeled, each with its own process time distribution and rule-based procedure-room mapping. Dynamic staff schedules for interventional radiologists, technologists, and nurses were incorporated in the model. Stochastic model simulation runs revealed the resource "computed tomography (CT) suite" as the major workflow bottleneck during the morning hours. This insight compelled the radiology department leadership to re-assign time blocks on a diagnostic CT scanner to the IR group. Moreover, this approach helped identify opportunities for additional appointments at times of lower diagnostic scanner utilization. Demand for interventional service from Outpatients during late hours of the day required the facility to extend hours of operations. Simulation-based optimization methods were used to model a new staff schedule, stretching the existing pool of resources to support the additional 2.5 h of daily operation. In conclusion, this study illustrates that the combination of workflow modeling, stochastic simulations, and optimization techniques is a viable and effective approach for identifying workflow inefficiencies and discovering and validating improvement options through what-if scenario testing.

Impact of the COVID-19 pandemic on the workflow of an ambulatory endoscopy center: an assessment by discrete event simulation

BACKGROUND AND AIMS

As community-based ambulatory endoscopy centers (AECs) across the nation are trying to reopen and safely resume outpatient endoscopic procedures after the unprecedented lockdown related to the coronavirus disease 2019 (COVID-19) pandemic, guidelines recommend pretesting and screening for COVID-19 along with other mitigation measures for the safety of patients and staff. The impact of such changes in the workflow of AECs on throughput and other performance indicators is largely unknown, although a significant reduction in revenue stream is expected.

METHODS

A discrete event simulation-based model was developed in the setting of a small to medium community-based single-specialty AEC to quantify the impact of COVID-19-related workflow changes on performance indicators and cost per case compared with the pre-COVID-19 baseline.

RESULTS

In the simulation model, post-COVID-19 recommended workflow changes significantly impacted the operational and productivity metrics and, in turn, adversely affected financial metrics. Overall, there was a significant decrease in staff utilization and consequent increase in total facility time, waiting time for patients, and cost per case because of a bottleneck at the time of preprocedure COVID-19 screening and testing while practicing social distancing. Strategies to minimize this adverse impact on productivity were assessed.

CONCLUSIONS

Pretesting and screening for COVID-19 as recommended by current guidelines will significantly impact the productivity and revenue stream of AECs. Urgent measures by payors are needed to adjust the facility reimbursement of endoscopy centers to ensure successful reopening and ramping up outpatient endoscopy services in these facilities already hit hard by the pandemic.

A Simulation-Based Planning Methodology for Decreasing Patient Waiting Times in Pure Walk-In Clinics

This article presents a simulation-based planning methodology that aims to improve patient service quality in pure walk-in clinics. Capacity planning is one of the major challenges in walk-in clinics because of the uncertainty in both patient demand and arrival times. This work presents a discrete-event simulation model for walk-in clinics that takes into consideration patient behavior in terms of arrival times for capacity planning at the clinic level. The goal of the model is to provide a tool that will allow clinics to develop protocols that will reduce patient waiting times by scheduling doctor and medical assistants considering demand uncertainties. A case study is presented to illustrate the benefits of the methodology. The results of the computational study show that by allocating the right number of resources at particular times of the day, walk-in clinics can achieve operational steady state while providing services to patients with minimum waiting times. The tool can be adapted and used to support any walk-in clinic.

Simulation modeling validity and utility in colorectal cancer screening delivery: A systematic review

OBJECTIVE

This study sought to assess the impact and validity of simulation modeling in informing decision making in a complex area of healthcare delivery: colorectal cancer (CRC) screening.

MATERIALS AND METHODS

We searched 10 electronic databases for English-language articles published between January 1, 2008, and March 1, 2019, that described the development of a simulation model with a focus on average-risk CRC screening delivery. Included articles were reviewed for evidence that the model was validated, and provided real or potential contribution to informed decision making using the GRADE EtD (Grading of Recommendations Assessment, Development, and Evaluation Evidence to Decision) framework.

RESULTS

A total of 43 studies met criteria. The majority used Markov modeling (n = 31 [72%]) and sought to determine cost-effectiveness, compare screening modalities, or assess effectiveness of screening. No study reported full model validation and only (58%) reported conducting any validation. Majority of models were developed to address a specific health systems or policy question; few articles report the model's impact on this decision (n = 39 [91%] vs. n = 5 [12%]). Overall, models provided evidence relevant to every element important to decision makers as outlined in the GRADE EtD framework.

DISCUSSION AND CONCLUSION

Simulation modeling contributes evidence that is considered valuable to decision making in CRC screening delivery, particularly in assessing cost-effectiveness and comparing screening modalities. However, the actual impact on decisions and validity of models is lacking in the literature. Greater validity testing, impact assessment, and standardized reporting of both is needed to understand and demonstrate the reliability and utility of simulation modeling.

USING DISCRETE-EVENT SIMULATION TO FIND WAYS TO REDUCE PATIENT WAIT TIME IN A GLAUCOMA CLINIC

Long wait times is one of the most common complaints from patients visiting the glaucoma clinic at the Kellogg Eye Center at the University of Michigan (UM). Long wait times have also been reported as a barrier to glaucoma care in other clinics as well. To address this issue, we develop a discrete-event simulation model to identify bottlenecks in the clinic that cause the majority of patient wait time. Different policies in terms of resource supplementation, i.e. adding staff and the corresponding equipment and exam rooms, are then accordingly proposed. We evaluate each of them using our simulation model through a series of what-if experiments. The most beneficial policy, considering the trade-off between patient wait time and resource supplementation expense, is proposed to the clinic to carry out in practice.

The use of modelling studies to inform planning of health services: case study of rapidly increasing endoscopy services in Australia

Background

Demand for gastrointestinal endoscopy in Australia is increasing as a result of the expanding national bowel cancer screening program and a growing, ageing population. More services are required to meet demand and ensure patients are seen within clinically recommended timeframes.

Methods

A discrete event simulation model was developed to project endoscopy waiting list outcomes for two large metropolitan health services encompassing 8 public hospitals in Australia. The model applied routinely collected health service data to forecast the impacts of future endoscopic demand over 5 years and to identify the level of service activity required to address patient waiting times and meet key policy targets. The approach incorporated evidence from the literature to produce estimates of cost-effectiveness by showing longer term costs and Quality Adjusted Life Years (QALYs) associated with service expansion.

Results

The modelling revealed that doing nothing would lead to the number of patients waiting longer than clinically recommended doubling across each health service within 5 years. A 38% overall increase in the number of monthly procedures available was required to meet and maintain a target of 95–98% of patients being seen within clinically recommended timeframes to the year 2021. This was projected to cost the funder approximately $140 million in additional activity over a 5 year period. Due to improved patient outcomes associated with timely intervention, it was estimated that the increased activity would generate over 22,000 additional QALYs across the two health services. This translated to an incremental cost-effectiveness ratio of $6467 and $5974 per QALY for each health service respectively.

Conclusions

Discrete event simulation modelling provided a rational, data based approach that allowed decision makers to quantify the future demand for endoscopy services and identify cost-effective strategies to meet community needs.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4438-x) contains supplementary material, which is available to authorized users.

Designing and scheduling a multi-disciplinary integrated practice unit for patient-centred care

This paper presents the design and analysis of a newly proposed form of care delivery called an integrated practice unit (IPU) in which a multi-disciplinary team of providers and staff work together to cover the full care cycle for a given condition. In an IPU, the different providers circulate among the patients, according to the need for their expertise, while patients remain in a single location once they check-in. From the patient's perspective, the benefits of such an arrangement should be self-evident. For payers and providers there will also be benefits as the fee-for-service market gives way to structured payments for each episode of care. Before setting up an IPU, it is necessary to gain an understanding of how available resources will limit patient flow and system performance. Treating resources such as providers, imaging equipment, and rooms parametrically, the primary goal of our work is to determine the number of patients that can be seen per day in an IPU while trying to constrain overtime, length of stay, and waiting time to best practice targets. Discrete-event simulation serves as our analytic tool. While we are involved in the design of a comprehensive suite of musculoskeletal IPUs, we illustrate our approach with an extensive computational study of one: a Lower Extremity Joint Pain IPU. Using the simulation methodology, we are not only able to determine the number of patients that can be scheduled for an in-clinic visit each day, but also the daily number of follow-up patients that can be served virtually through telemedicine with no additional resources and minimal impact on IPU performance. These results assisted the Department of Surgery at the Dell Medical School at The University of Texas in the optimal design of its first IPU, which opened in the fall of 2017.

Improving Clinic Operational Efficiency and Utilization with RTLS

New sources of operational data are leading to novel healthcare delivery system design and opportunities to support operational planning and decision-making. Technologies such as real time locating systems (RTLS) provide a unique view and understanding of how healthcare delivery settings behave and respond to operational design changes. In this paper RTLS data from an outpatient clinical setting is leveraged to identify the appropriate number of scheduled providers in order to improve the utilization of the clinical space while balancing the negative effects of clinic congestion. The approaches presented pair historical utilization rates for the clinical space with scheduled provider and patient volumes to support scheduling decisions in an operationally flexible clinic design. These historical data are augmented with clinic staff observation logs to identify target utilization rates as well as high congestion levels. Results are presented for two approaches: one where utilization of clinical space is a key performance metric and another where the decision-maker may be risk averse toward the use of provider time and use a probabilistic approach to determine provider staffing levels.

Application of discrete event simulation in health care: a systematic review

BACKGROUND

The objective was to explore the current advances and extent of DES (Discrete Event Simulation) applied to assisting with health decision making, as well as to categorize the wide spectrum of health-related topics where DES was applied.

METHODS

A systematic review was conducted of the literature published over the last two decades. Original research articles were included and reviewed if they concentrated on the topic of DES technique applied to health care management with model frameworks explicitly demonstrated. No restriction regarding the settings of DES application was applied.

RESULTS

A total of 211 papers met the predefined inclusion criteria. The number of publications included increased significantly especially after 2010.101 papers (48%) stated explicitly disease areas targeted, the most frequently modeled of which are related to circulatory system, nervous system and Neoplasm. The DES applications were distributed unevenly into 4 major classes: health and care systems operation (HCSO) (65%), disease progression modeling (DPM) (28%), screening modeling (SM) (5%) and health behavior modeling (HBM) (2%). More than 68% of HCSO by DES were focused on specific problems in individual units. However, more attempts at modeling highly integrated health service systems as well as some new trends were identified.

CONCLUSIONS

DES technique has been an effective tool to approach a wide variety of health care issues. Among all DES applications in health care, health system operations research occupied the most considerable proportion and increased most significantly. Health Economic Evaluation (HEE) was the second most common topic for DES in health care, but with stable rather than increasing numbers of publications.

A Discrete Event Simulation Model of Patient Flow in a General Hospital Incorporating Infection Control Policy for Methicillin-Resistant Staphylococcus Aureus (MRSA) and Vancomycin-Resistant Enterococcus (VRE)

BACKGROUND

Hospitalized patients are assigned to available staffed beds based on patient acuity and services required. In hospitals with double-occupancy rooms, patients must be additionally matched by gender. Patients with methicillin-resistant Staphylococcus aureus (MRSA) or vancomycin-resistant Enterococcus (VRE) must be bedded in single-occupancy rooms or cohorted with other patients with similar MRSA/VRE flags.

METHODS

We developed a discrete event simulation (DES) model of patient flow through an acute care hospital. Patients are matched to beds based on acuity, service, gender, and known MRSA/VRE colonization. Outcomes included time to bed arrival, length of stay, patient-bed acuity mismatches, occupancy, idle beds, acuity-related transfers, rooms with discordant MRSA/VRE colonization, and transmission due to discordant colonization.

RESULTS

Observed outcomes were well-approximated by model-generated outcomes for time-to-bed arrival (6.7 v. 6.2 to 6.5 h) and length of stay (3.3 v. 2.9 to 3.0 days), with overlapping 90% coverage intervals. Patient-bed acuity mismatches, where patient acuity exceeded bed acuity and where patient acuity was lower than bed acuity, ranged from 0.6 to 0.9 and 8.6 to 11.1 mismatches per h, respectively. Values for observed occupancy, total idle beds, and acuity-related transfers compared favorably to model-predicted values (91% v. 86% to 87% occupancy, 15.1 v. 14.3 to 15.7 total idle beds, and 27.2 v. 22.6 to 23.7 transfers). Rooms with discordant colonization status and transmission due to discordance were modeled without an observed value for comparison. One-way and multi-way sensitivity analyses were performed for idle beds and rooms with discordant colonization.

CONCLUSIONS

We developed and validated a DES model of patient flow incorporating MRSA/VRE flags. The model allowed for quantification of the substantial impact of MRSA/VRE flags on hospital efficiency and potentially avoidable nosocomial transmission.

Operational research techniques applied throughout cancer care services: a review

Cancer is a disease affecting increasing numbers of people. In the UK, the proportion of people affected by cancer is projected to increase from 1 in 3 in 1992, to nearly 1 in 2 by 2020. Health services to tackle cancer can be grouped broadly into prevention, diagnosis, staging, and treatment. We review examples of Operational Research (OR) papers addressing decisions encountered in each of these areas. In conclusion, we find many examples of OR research on screening strategies, as well as on treatment planning and scheduling. On the other hand, our search strategy uncovered comparatively few examples of OR models applied to reducing cancer risks, optimising diagnostic procedures, and staging. Improvements to cancer care services have been made as a result of successful OR modelling. There is potential for closer working with clinicians to enable the impact of other OR studies to be of greater benefit to cancer sufferers.

Decreasing patient length of stay via new flexible exam room allocation policies in ambulatory care clinics

To address prolonged lengths of stay (LOS) in ambulatory care clinics, we analyze the impact of implementing flexible and dynamic policies for assigning exam rooms to providers. In contrast to the traditional approaches of assigning specific rooms to each provider or pooling rooms among all practitioners, we characterize the impact of alternate compromise policies that have not been explored in previous studies. Since ambulatory care patients may encounter multiple different providers in a single visit, room allocation can be determined separately for each encounter accordingly. For the first phase of the visit, conducted by the medical assistant, we define a dynamic room allocation policy that adjusts room assignments based on the current state of the clinic. For the second phase of the visit, conducted by physicians, we define a series of room sharing policies which vary based on two dimensions, the number of shared rooms and the number of physicians sharing each room. Using a discrete event simulation model of an outpatient cardiovascular clinic, we analyze the benefits and costs associated with the proposed room allocation policies. Our findings show that it is not necessary to fully share rooms among providers in order to reduce patient LOS and physician idle time. Instead, most of the benefit of pooling can be achieved by implementation of a compromise room allocation approach, limiting the need for significant organizational changes within the clinic. Also, in order to achieve most of the benefits of room allocation policies, it is necessary to increase flexibility in the two dimensions simultaneously. These findings are shown to be consistent in settings with alternate patient scheduling and distinctions between physicians.

Efficiency of endoscopy units can be improved with use of discrete event simulation modeling

Background and study aims: The projected increased demand for health services obligates healthcare organizations to operate efficiently. Discrete event simulation (DES) is a modeling method that allows for optimization of systems through virtual testing of different configurations before implementation. The objective of this study was to identify strategies to improve the daily efficiencies of an endoscopy center with the use of DES. Methods: We built a DES model of a five procedure room endoscopy unit at a tertiary-care university medical center. After validating the baseline model, we tested alternate configurations to run the endoscopy suite and evaluated outcomes associated with each change. The main outcome measures included adequate number of preparation and recovery rooms, blocked inflow, delay times, blocked outflows, and patient cycle time. Results: Based on a sensitivity analysis, the adequate number of preparation rooms is eight and recovery rooms is nine for a five procedure room unit (total 3.4 preparation and recovery rooms per procedure room). Simple changes to procedure scheduling and patient arrival times led to a modest improvement in efficiency. Increasing the preparation/recovery rooms based on the sensitivity analysis led to significant improvements in efficiency. Conclusions: By applying tools such as DES, we can model changes in an environment with complex interactions and find ways to improve the medical care we provide. DES is applicable to any endoscopy unit and would be particularly valuable to those who are trying to improve on the efficiency of care and patient experience.

Evaluation of interventional endoscopy unit efficiency metrics at a tertiary academic medical center

BACKGROUND AND STUDY AIMS

There is an increasing demand for interventional endoscopic services and the need to develop efficient endoscopic units. The aim of this study was to analyze performance data and define metrics to improve efficiency in a single academic interventional endoscopy center. ]

PATIENTS AND METHODS

The prospective operations performance data (6-month period) of our interventional endoscopy unit (EU) was analyzed. First-case start time (FIRST) delay was defined as any time the first patient of the day entered the endoscopy room after the scheduled time. Non-endoscopy time (NET) and total time (TT) were defined as non-procedural and total time elapsed in the EU, respectively. Time-interval between successive patients (TISP) was defined as the time from one patient departure from the room until the time of arrival of the next patient in the room.

RESULTS

A total of 1421 patients underwent 1635 endoscopic procedures. FIRST was delayed (54.2 % cases) by 13.6 min (range 1 - 53), but started within 15 min of the scheduled time in 85 % of the cases. NET accounted for 9.1 hours (67.2 %) of 13.5 hours TT/day. TISP (37.1 min, range 5 - 125) comprised 54.2 % of the NET, and was delayed (> 30 min) in 49.8 % of cases. "Patient flow" processes (registration, admission, transportation, scheduling) accounted for 50.1 % of TISP delays.

CONCLUSIONS

Delays in NET, specifically TISP, rather than FIRST, were identified as a cause for decreased efficiency. "Patient flow" processes were the main reasons for delays in TISP. This study identifies potential process measures that can be used as benchmarks to improve efficiency in the EU.

A Patient Flow Analysis: Identification of Process Inefficiencies and Workflow Metrics at an Ambulatory Endoscopy Unit

Background. The increasing demand for endoscopic procedures coincides with the paradigm shift in health care delivery that emphasizes efficient use of existing resources. However, there is limited literature on the range of endoscopy unit efficiencies. Methods. A time and motion analysis of patient flow through the Hotel-Dieu Hospital (Kingston, Ontario) endoscopy unit was followed by qualitative interviews. Procedures were directly observed in three segments: individual endoscopy room use, preprocedure/recovery room, and overall endoscopy unit utilization. Results. Data were collected for 137 procedures in the endoscopy room, 139 procedures in the preprocedure room, and 143 procedures for overall room utilization. The mean duration spent in the endoscopy room was 31.47 min for an esophagogastroduodenoscopy, 52.93 min for a colonoscopy, 30.47 min for a flexible sigmoidoscopy, and 66.88 min for a double procedure. The procedure itself accounted for 8.11 min, 34.24 min, 9.02 min, and 39.13 min for the above procedures, respectively. The focused interviews identified the scheduling template as a major area of operational inefficiency. Conclusions. Despite reasonable procedure times for all except colonoscopies, the endoscopy room durations exceed the allocated times, reflecting the impact of non-procedure-related factors and the need for a revised scheduling template. Endoscopy units have unique operational characteristics and identification of process inefficiencies can lead to targeted quality improvement initiatives.

Computer modeling of lung cancer diagnosis-to-treatment process

We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed.

Declining liver graft quality threatens the future of liver transplantation in the United States

National liver transplantation (LT) volume has declined since 2006, in part because of worsening donor organ quality. Trends that degrade organ quality are expected to continue over the next 2 decades. We used the United Network for Organ Sharing (UNOS) database to inform a 20-year discrete event simulation estimating LT volume from 2010 to 2030. Data to inform the model were obtained from deceased organ donors between 2000 and 2009. If donor liver utilization practices remain constant, utilization will fall from 78% to 44% by 2030, resulting in 2230 fewer LTs. If transplant centers increase their risk tolerance for marginal grafts, utilization would decrease to 48%. The institution of "opt-out" organ donation policies to increase the donor pool would still result in 1380 to 1866 fewer transplants. Ex vivo perfusion techniques that increase the use of marginal donor livers may stabilize LT volume. Otherwise, the number of LTs in the United States will decrease substantially over the next 15 years. In conclusion, the transplant community will need to accept inferior grafts and potentially worse posttransplant outcomes and/or develop new strategies for increasing organ donation and utilization in order to maintain the number of LTs at the current level.

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.

Improving operating room productivity via parallel anesthesia processing

PURPOSE

Parallel processing of regional anesthesia may improve operating room (OR) efficiency in patients undergoes upper extremity surgical procedures. The purpose of this paper is to evaluate whether performing regional anesthesia outside the OR in parallel increases total cases per day, improve efficiency and productivity.

DESIGN/METHODOLOGY/APPROACH

Data from all adult patients who underwent regional anesthesia as their primary anesthetic for upper extremity surgery over a one-year period were used to develop a simulation model. The model evaluated pure operating modes of regional anesthesia performed within and outside the OR in a parallel manner. The scenarios were used to evaluate how many surgeries could be completed in a standard work day (555 minutes) and assuming a standard three cases per day, what was the predicted end-of-day time overtime.

FINDINGS

Modeling results show that parallel processing of regional anesthesia increases the average cases per day for all surgeons included in the study. The average increase was 0.42 surgeries per day. Where it was assumed that three cases per day would be performed by all surgeons, the days going to overtime was reduced by 43 percent with parallel block. The overtime with parallel anesthesia was also projected to be 40 minutes less per day per surgeon.

RESEARCH LIMITATIONS/IMPLICATIONS

Key limitations include the assumption that all cases used regional anesthesia in the comparisons. Many days may have both regional and general anesthesia. Also, as a case study, single-center research may limit generalizability.

PRACTICAL IMPLICATIONS

Perioperative care providers should consider parallel administration of regional anesthesia where there is a desire to increase daily upper extremity surgical case capacity. Where there are sufficient resources to do parallel anesthesia processing, efficiency and productivity can be significantly improved.

ORIGINALITY/VALUE

Simulation modeling can be an effective tool to show practice change effects at a system-wide level.

The influence of waiting times on cost-effectiveness: a case study of colorectal cancer mass screening

When a cost-effectiveness analysis is implemented, the health-care system is usually assumed to adjust smoothly to the proposed new strategy. However, technological innovations in health care may often induce friction in the organization of care supply, implying the congestion of services and subsequent waiting times. Our objective here is to measure how these short run rigidities can challenge cost-effectiveness recommendations favorable to an innovative mass screening test for colorectal cancer. Using Markov modeling, we compare the standard Guaiac fecal occult blood test (gFOBT) with an innovative screening test for colorectal cancer, namely the immunological fecal occult blood test (iFOBT). Waiting time can occur between a positive screening test and the subsequent confirmation colonoscopy. Five scenarios are considered for iFOBT: no further waiting time compared with gFOBT, twice as much waiting time for a period of 5 or 10 years, and twice as much waiting time for a period of 5 or 10 years combined with a 25% decrease in participation to confirmation colonoscopies. According to our modeling, compared with gFOBT, iFOBT would approximately double colonoscopy demand. Probabilistic sensitivity analysis enables concluding that the waiting time significantly increases the uncertainty surrounding recommendations favorable to iFOBT if it induces a decrease in the adherence rate for confirmation colonoscopy.

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.

Improving patient flow at a family health clinic

This paper presents an analysis of a residency primary care clinic whose majority of patients are underserved. The clinic is operated by the health system for Bexar County and staffed primarily with physicians in a three-year Family Medicine residency program at The University of Texas School of Medicine in San Antonio. The objective of the study was to obtain a better understanding of patient flow through the clinic and to investigate changes to current scheduling rules and operating procedures. Discrete event simulation was used to establish a baseline and to evaluate a variety of scenarios associated with appointment scheduling and managing early and late arrivals. The first steps in developing the model were to map the administrative and diagnostic processes and to collect time-stamped data and fit probability distributions to each. In conjunction with the initialization and validation steps, various regressions were performed to determine if any relationships existed between individual providers and patient types, length of stay, and the difference between discharge time and appointment time. The latter two statistics along with resource utilization and closing time were the primary metrics used to evaluate system performance.The results showed that up to an 8.5 % reduction in patient length of stay is achievable without noticeably affecting the other metrics by carefully adjusting appointment times. Reducing the no-show rate from its current value of 21.8 % or overbooking, however, is likely to overwhelm the system's resources and lead to excessive congestion and overtime. Another major finding was that the providers are the limiting factor in improving patient flow. With an average utilization rate above 90 % there is little prospect in shortening the total patient time in the clinic without reducing the providers' average assessment time. Finally, several suggestions are offered to ensure fairness when dealing with out-of-order arrivals.

Use of discrete event simulation to improve a mental health clinic

OBJECTIVE

To improve clinic design, trial-and-error is commonly used to discover strategies that lead to improvement. Our goal was to predict the effects of various changes before undertaking them.

METHOD

Systems engineers collaborated with staff at an integrated primary care-mental health care clinic to create a computer simulation that mirrored how the clinic currently operates. We then simulated hypothetical changes to the staffing to understand their effects on percentage of patients seen outside scheduled clinic hours and service completion time.

RESULTS

We found that, out of the change options being considered by the clinic, extending daily clinic hours by two and including an additional psychiatrist are likely to result in the greatest incremental decreases in patients seen outside clinic hours and in service time.

CONCLUSION

Simulation in partnership with engineers can be an attractive tool for improving mental health clinics, particularly when changes are costly and thus trial-and-error is not desirable.

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.

Achieving efficiency in crowded emergency departments: a research agenda

In 2011, Academic Emergency Medicine convened a consensus conference entitled "Interventions to Assure Quality in the Crowded Emergency Department." This article, a product of the breakout session on "interventions to safeguard efficiency of care," explores various elements of the research agenda on efficiency and quality in crowded emergency departments (EDs). The authors discuss four areas identified as critical to achieving progress in the research agenda for improving ED efficiency: 1) What measures can be used to understand and improve the efficiency and quality of interventions in the ED? 2) Which factors outside of the ED's control affect ED efficiency? 3) How do workforce factors affect ED efficiency? 4) How do ED design, patient flow structures, and use of technology affect efficiency? Filling these knowledge gaps is vital to identifying interventions that improve the delivery of emergency care in all EDs.

Modeling and analysis of work flow and staffing level in a computed tomography division of University of Wisconsin Medical Foundation

In this paper, a Markov chain model is developed to model the work flow in a computed tomography (CT) imaging department at University of Wisconsin Medical Foundation. Using this model, we estimate the patient length of stay and investigate different configurations of radiology specialists for potential efficiency improvement to reduce flow time and cost. What-if analysis is carried out to investigate the impact of various staffing levels and sensitivity study is used to identify the bottleneck operation, i.e., the most impeding one whose improvement can lead to the highest productivity increase.

Mapping turnaround times (TAT) to a generic timeline: a systematic review of TAT definitions in clinical domains

BACKGROUND

Assessing turnaround times can help to analyse workflows in hospital information systems. This paper presents a systematic review of literature concerning different turnaround time definitions. Our objectives were to collect relevant literature with respect to this kind of process times in hospitals and their respective domains. We then analysed the existing definitions and summarised them in an appropriate format.

METHODS

Our search strategy was based on Pubmed queries and manual reviews of the bibliographies of retrieved articles. Studies were included if precise definitions of turnaround times were available. A generic timeline was designed through a consensus process to provide an overview of these definitions.

RESULTS

More than 1000 articles were analysed and resulted in 122 papers. Of those, 162 turnaround time definitions in different clinical domains were identified. Starting and end points vary between these domains. To illustrate those turnaround time definitions, a generic timeline was constructed using preferred terms derived from the identified definitions. The consensus process resulted in the following 15 terms: admission, order, biopsy/examination, receipt of specimen in laboratory, procedure completion, interpretation, dictation, transcription, verification, report available, delivery, physician views report, treatment, discharge and discharge letter sent. Based on this analysis, several standard terms for turnaround time definitions are proposed.

CONCLUSION

Using turnaround times to benchmark clinical workflows is still difficult, because even within the same clinical domain many different definitions exist. Mapping of turnaround time definitions to a generic timeline is feasible.