An emergency department patient flow model based on queueing theory principles

Obstetric operating room staffing and operating efficiency using queueing theory

INTRODUCTION

Strategies to achieve efficiency in non-operating room locations have been described, but emergencies and competing priorities in a birth unit can make setting optimal staffing and operation benchmarks challenging. This study used Queuing Theory Analysis (QTA) to identify optimal birth center operating room (OR) and staffing resources using real-world data.

METHODS

Data from a Level 4 Maternity Center (9,626 births/year, cesarean delivery (CD) rate 32%) were abstracted for all labor and delivery operating room activity from July 2019-June 2020. QTA has two variables: Mean Arrival Rate, λ and Mean Service Rate µ. QTA formulas computed probabilities: P0 = 1-(λ/ µ) and Pn = P0 (λ/µ)n where n = number of patients. P0…n is the probability there are zero patients in the queue at a given time. Multiphase multichannel analysis was used to gain insights on optimal staff and space utilization assuming a priori safety parameters (i.e., 30 min decision to incision in unscheduled CD; ≤ 5 min for emergent CD; no greater than 8 h for nil per os time). To achieve these safety targets, a < 0.5% probability that a patient would need to wait was assumed.

RESULTS

There were 4,017 total activities in the operating room and 3,092 CD in the study period. Arrival rate λ was 0.45 (patients per hour) at peak hours 07:00-19:00 while λ was 0.34 over all 24 h. The service rate per OR team (µ) was 0.87 (patients per hour) regardless of peak or overall hours. The number of server teams (s) dedicated to OR activity was varied between two and five. Over 24 h, the probability of no patients in the system was P0 = 0.61, while the probability of 1 patient in the system was P1 = 0.23, and the probability of 2 or more patients in the system was P≥2 = 0.05 (P3 = 0.006). However, between peak hours 07:00-19:00, λ was 0.45, µ was 0.87, s was 3, P0 was 0.48; P1 was 0.25; and P≥2 was 0.07 (P3 = 0.01, P4 = 0.002, P5 = 0.0003).

CONCLUSION

QTA is a useful tool to inform birth center OR efficiency while upholding assumed safety standards and factoring peaks and troughs of daily activity. Our findings suggest QTA is feasible to guide staffing for maternity centers of all volumes through varying model parameters. QTA can inform individual hospital-level decisions in setting staffing and space requirements to achieve safe and efficient maternity perioperative care.

Analyzing the queuing theory at the emergency department at King Hussein cancer center

OBJECTIVES

This study was conducted in 2022 at King Hussein Cancer Center (KHCC) to analyze the queuing theory approach at the Emergency Department (ED) to estimate patients' wait times and predict the accuracy of the queuing theory approach.

METHODS

According to the statistics, the peak months were July and August, with peak hours from 10 a.m. until 6 p.m. The study sample was a week in July 2022, during the peak days and hours. This study measured patients' wait times at these three stations: the health informatics desk, triage room, and emergency bed area.

RESULTS

The average number of patients in line at the health informatics desk was not more than 3, and the waiting time was between 1 and 4 min. Since patients were receiving the service immediately in the triage room, there was no waiting time or line because the nurse's role ended after taking the vital signs and rating the patient's disease acuity. Using equations of queuing theory and other relativistic equations in the emergency bed area gave different results. The queuing theory approach showed that the average residence time in the system was between 4 and 10 min.

CONCLUSIONS

Conversely, relativistic equations (ratios of served patients and departed patients and other related variables) demonstrated that the average residence time was between 21 and 36 min.

The association between day of arrival, time of arrival, daily volume and the rate of patients that "left without being seen"

INTRODUCTION

Patients' left without being seen (LWBS) rate is used as an emergency department (ED) quality indicator. Prior research has investigated characteristics of these patients, but there are minimal studies assessing the impact of departmental variables. We evaluate the LWBS rate at a granular level, looking at its relationship to day of week, hour of arrival and total patient volume.

METHODS

Retrospective cohort analysis of 109,983 cases from a single academic center. We captured patient disposition, day of week and hour of day of arrival, and total daily volume. Chi-squared test was performed to determine the difference in LWBS rates based on arrival variables. We ran a polynomial regression for LWBS rates by decile of daily patient volume.

RESULTS

The overall LWBS rate was 1.82% over 2 years. This varied significantly by day of week and hour of day (p < 0.001). Day of week rates ranged from 0.73% on Sunday to 2.45% on Wednesday. Hour of day rates ranged from 0.26% between 8 AM-9 AM, to 3.71% between 10 PM-11 PM. As total daily patient volume increased, LWBS rates gradually increased until the 70th percentile, followed by significant exponential growth afterwards.

DISCUSSION

LWBS rates are not static measurements, and vary greatly depending on ED circumstances. Weekdays and evenings have significantly higher rates. Additionally, LWBS rates climb above 2% as daily registrations reach the 70th percentile, increasing exponentially at each subsequent decile. Understanding these effects will allow for more effective, targeted interventions to minimize this rate and improve throughput.

Self-reporting and screening: Data with right-censored, left-censored, and complete observations

We consider survival data that combine three types of observations: uncensored, right-censored, and left-censored. Such data arises from screening a medical condition, in situations where self-detection arises naturally. Our goal is to estimate the failure-time distribution, based on these three observation types. We propose a novel methodology for distribution estimation using both semiparametric and nonparametric techniques. We then evaluate the performance of these estimators via simulated data. Finally, as a case study, we estimate the patience of patients who arrive at an emergency department and wait for treatment. Three categories of patients are observed: those who leave the system and announce it, and thus their patience time is observed; those who get service and thus their patience time is right-censored by the waiting time; and those who leave the system without announcing it. For this third category, the patients' absence is revealed only when they are called to service, which is after they have already left; formally, their patience time is left-censored. Other applications of our proposed methodology are discussed.

Graph Network Techniques to Model and Analyze Emergency Department Patient Flow

This article moves beyond analysis methods related to a traditional relational database or network analysis and offers a novel graph network technique to yield insights from a hospital’s emergency department work model. The modeled data were saved in a Neo4j graphing database as a time-varying graph (TVG), and related metrics, including degree centrality and shortest paths, were calculated and used to obtain time-related insights from the overall system. This study demonstrated the value of using a TVG method to model patient flows during emergency department stays. It illustrated dynamic relationships among hospital and consulting units that could not be shown with traditional analyses. The TVG approach augments traditional network analysis with temporal-related outcomes including time-related patient flows, temporal congestion points details, and periodic resource constraints. The TVG approach is crucial in health analytics to understand both general factors and unique influences that define relationships between time-influenced events. The resulting insights are useful to administrators for making decisions related to resource allocation and offer promise for understanding impacts of physicians and nurses engaged in specific patient emergency department experiences. We also analyzed customer ratings and reviews to better understand overall patient satisfaction during their journey through the emergency department.

Queueing Problems in Emergency Departments: A Review of Practical Approaches and Research Methodologies

Problems related to patient scheduling and queueing in emergency departments are gaining increasing attention in theory, in the fields of operations research and emergency and healthcare services, and in practice. This paper aims to provide an extensive review of studies addressing queueing-related problems explicitly related to emergency departments. We have reviewed 229 articles and books spanning seven decades and have sought to organize the information they contain in a manner that is accessible and useful to researchers seeking to gain knowledge on specific aspects of such problems. We begin by presenting a historical overview of applications of queueing theory to healthcare-related problems. We subsequently elaborate on managerial approaches used to enhance efficiency in emergency departments. These approaches include bed management, fast-track, dynamic resource allocation, grouping/prioritization of patients, and triage approaches. Finally, we discuss scientific methodologies used to analyze and optimize these approaches: algorithms, priority models, queueing models, simulation, and statistical approaches.

Redesign of US Medical Schools: A Shift from Health Service to Population Health Management

The integration of medical schools and clinical partners is effectively established through the formation of academic medical centers (AMCs). The tripartite mission of AMCs emphasizes the importance of providing critical clinical services, medical innovation through research, and the education of future health care leaders. Although AMCs represent only 5% of all hospitals, they contribute substantially to serving disadvantaged populations of patients, including an estimated 37% of all charity care and 26% of all Medicaid hospitalizations. Currently, most AMCs use a business model centered upon revenue generated from hospital services and/or practice plans. In the last decade, mounting financial demands have placed significant pressure on AMC finances because of the rising costs associated with complex clinical care and operating diverse graduate medical education programs. A shift toward population health-centric health care management strategies will profoundly influence the predominant forms of health care delivery in the United States in the foreseeable future. Health systems are increasingly pursuing new strategies to manage financial risk, such as forming Accountable Care Organizations and provider-sponsored plans to provide value-based care. Refocusing research and operational capacity toward population health management fosters collaboration and enables reintegration with hospital and clinical partners across care networks, and can potentially create new revenue streams for AMCs. Despite the benefits of population health integration, current literature lacks a blueprint to guide AMCs in the transformation toward sustainable population health management models. The purpose of this paper is to propose a modern conceptual framework that can be operationalized by AMCs in order to achieve a sustainable future.

Optimization of Patient Flow in Urgent Care Centers Using a Digital Tool for Recording Patient Symptoms and History: Simulation Study

Background

Crowding can negatively affect patient and staff experience, and consequently the performance of health care facilities. Crowding can potentially be eased through streamlining and the reduction of duplication in patient history-taking through the use of a digital symptom-taking app.

Objective

We simulated the introduction of a digital symptom-taking app on patient flow. We hypothesized that waiting times and crowding in an urgent care center (UCC) could be reduced, and that this would be more efficient than simply adding more staff.

Methods

A discrete-event approach was used to simulate patient flow in a UCC during a 4-hour time frame. The baseline scenario was a small UCC with 2 triage nurses, 2 doctors, 1 treatment/examination nurse, and 1 discharge administrator in service. We simulated 33 scenarios with different staff numbers or different potential time savings through the app. We explored average queue length, waiting time, idle time, and staff utilization for each scenario.

Results

Discrete-event simulation showed that even a few minutes saved through patient app-based self-history recording during triage could result in significantly increased efficiency. A modest estimated time saving per patient of 2.5 minutes decreased the average patient wait time for triage by 26.17%, whereas a time saving of 5 minutes led to a 54.88% reduction in patient wait times. Alternatively, adding an additional triage nurse was less efficient, as the additional staff were only required at the busiest times.

Conclusions

Small time savings in the history-taking process have potential to result in substantial reductions in total patient waiting time for triage nurses, with likely effects of reduced patient anxiety, staff anxiety, and improved patient care. Patient self-history recording could be carried out at home or in the waiting room via a check-in kiosk or a portable tablet computer. This formative simulation study has potential to impact service provision and approaches to digitalization at scale.

Design and comparison of scheduling strategy for teleconsultation

BACKGROUND

Telemedicine is playing an increasingly more important role in disease diagnosis and treatment. The market of telemedicine application is continuously promoted, thus bringing some issues on telemedicine operations management.

OBJECTIVE

We aimed to compare the teleconsultation scheduling performance of newly designed proactive strategy and existing static strategy and explore the decision-making under different conditions.

METHODS

We developed a discrete-event simulation model based on practical investigation to describe the existing static scheduling strategy of teleconsultation. The static strategy model was verified by comparing it with the historical data. Then a new proactive strategy was proposed, whose average waiting time, variance of waiting time and completed numbers were compared with the static strategy.

RESULTS

The analysis indicated that the proactive strategy performed better than static under the current resource allocation. Furthermore, we explored the impact on the system of both strategies varying arrival rate and experts' shift time.

CONCLUSIONS

Under different shift times and arrival rates, the managers of telemedicine center should select different strategy. The experts' shift time had a significant impact on all system performance indicators. Therefore, if managers wanted to improve the system performance to a greater extent, they needed to reduce the shift time as much as possible.

Using Queue Theory and Load-Leveling Principles to Identify a Simple Metric for Resource Planning in a Pediatric Emergency Department

Increased waiting time in pediatric emergency departments is a well-recognized and complex problem in a resource-limited US health care system. Efforts to reduce emergency department wait times include modeling arrival rates, acuity, process flow, and human resource requirements. The aim of this study was to investigate queue theory and load-leveling principles to model arrival rates and to identify a simple metric for assisting with determination of optimal physical space and human resource requirements. We discovered that pediatric emergency department arrival rates vary based on time of day, day of the week, and month of the year in a predictable pattern and that the hourly change in pediatric emergency department waiting room census may be useful as a simple metric to identify target times for shifting resources to better match supply and demand at no additional cost.

Quantifying Dynamic Flow of Emergency Department (ED) Patient Managements: A Multistate Model Approach

Background

Emergency department (ED) crowding and prolonged lengths of stay continue to be important medical issues. It is difficult to apply traditional methods to analyze multiple streams of the ED patient management process simultaneously. The aim of this study was to develop a statistical model to delineate the dynamic patient flow within the ED and to analyze the effects of relevant factors on different patient movement rates.

Methods

This study used a retrospective cohort available with electronic medical data. Important time points and relevant covariates of all patients between January and December 2013 were collected. A new five-state Markov model was constructed by an expert panel, including three intermediate states: triage, physician management, and observation room and two final states: admission and discharge. A day was further divided into four six-hour periods to evaluate dynamics of patient movement over time.

Results

A total of 149,468 patient records were analyzed with a median total length of stay being 2.12 (interquartile range = 6.51) hours. The patient movement rates between states were estimated, and the effects of the age group and triage level on these movements were also measured. Patients with lower acuity go home more quickly (relative rate (RR): 1.891, 95% CI: 1.881–1.900) but have to wait longer for physicians (RR: 0.962, 95% CI: 0.956–0.967) and admission beds (RR: 0.673, 95% CI: 0.666–0.679). While older patients were seen more quickly by physicians (RR: 1.134, 95% CI: 1.131–1.139), they spent more time waiting for the final state (for admission RR: 0.830, 95% CI: 0.821–0.839; for discharge RR: 0.773, 95% CI: 0.769–0.776). Comparing the differences in patient movement rates over a 24-hour day revealed that patients wait longer before seen by physicians during the evening and that they usually move from the ED to admission afternoon. Predictive dynamic illustrations show that six hours after the patients' entry, the probability of still in the ED system ranges from 28% in the evening to 38% in the morning.

Conclusions

The five-state model well described the dynamic ED patient flow and analyzed the effects of relevant influential factors at different states. The model can be used in similar medical settings or incorporate different important covariates to develop individually tailored approaches for the improvement of efficiency within the health professions.

Association of Insurance With Use of Emergency Medical Services Among Children

OBJECTIVE

The use of emergency medical services (EMS) can be lifesaving for critically ill children and should be defined by the child's clinical need. Our objective was to determine whether nonclinical demographic factors and insurance status are associated with EMS use among children presenting to the emergency department (ED).

METHODS

In this cross-sectional study using the National Hospital Ambulatory Medical Care Survey, we included children presenting to EDs from 2009 to 2014. We evaluated the association between EMS use and patients' insurance status using multivariable logistic regressions, adjusting for demographic, socioeconomic, and clinical factors such as illness severity as measured by a modified and recalibrated version of the Revised Pediatric Emergency Assessment Tool (mRePEAT) and the presence of comorbidities or chronic conditions. A propensity score analysis was performed to validate our findings.

RESULTS

Of the estimated 191,299,454 children presenting to EDs, 11,178,576 (5.8%) arrived by EMS and 171,145,895 (89.5%) arrived by other means. Children arriving by EMS were more ill [mRePEAT score, 1.13; 95% confidence interval (CI), 1.12-1.14 vs mRePEAT score, 1.01; 95% CI: 1.01-1.02] and more likely to have a comorbidity or chronic condition (OR: 3.17, 95% CI: 2.80-3.59). In the adjusted analyses, the odds of EMS use were higher for uninsured children and lower for children with public insurance compared with children with private insurance [OR (95% CI): uninsured, 1.41 (1.12-1.78); public, 0.77 (0.65-0.90)]. The propensity score analysis showed similar results.

CONCLUSIONS

In contrast to adult patients, children with public insurance are less likely to use EMS than children with private insurance, even after adjustment for illness severity and other confounders.

Patient Characteristics and Clinical Process Predictors of Patients Leaving Without Being Seen from the Emergency Department

INTRODUCTION

Delays in patient flow in the emergency department (ED) result in patients leaving without being seen (LWBS). This compromises patient experience and quality of care. Our primary goal was to develop a predictive model by evaluating associations between patients LWBS and ED process measures and patient characteristics.

METHODS

This was a cross-sectional study in a 95,000 annual visit adult ED comparing patients LWBS, with controls. Data were drawn from four seasonally adjusted four-week periods (30,679 total visits). Process measures included 1) arrivals per hour; 2) "door-to-provider" time; and the numbers of 3) patients in the waiting room; 4) boarding ED patients waiting for an inpatient bed; 5) providers and nurses (RN); and 6) patients per RN. Patient characteristics collected included 1) age; 2) gender; 3) race/ethnicity; 4) arrival mode (walk-in or via emergency medical services [EMS]); and 5) acuity based on Emergency Severity Index (ESI). Univariable analyses included t-tests and Pearson's chi-square tests. We split the data randomly into derivation and validation cohorts. We used backward selection to develop the final derivation model, and factors with a p-value ≤ 0.05 were retained. Estimates were applied to the validation cohort and measures of discrimination (receiver operating characteristic) and model fit were assessed.

RESULTS

In the final model, the odds of LWBS increased with the number of patients in the waiting room (odds ratio [OR] 1.05; 95% confidence interval [CI], 1.03 to 1.06); number of boarding patients (OR 1.02; 95% CI, 1.01 to 1.03); arrival rate (OR 1.04; 95% CI, 1.02 to 1.05) and longer "door-to-provider" times (test of linear trend in the adjusted OR was p = 0.002). Patient characteristics associated with LWBS included younger age (OR 0.98; 95% CI, 0.98 to 0.99), and lower acuity (higher ESI category) (OR 2.01; 95% CI, 1.84 to 2.20). Arrival by EMS was inversely associated with LWBS (OR 0.29; 0.23 to 0.36). The area under the curve for the final model in the validation cohort was 0.85 (95% CI, 0.84 to 0.86). There was good agreement between the observed and predicted risk.

CONCLUSION

Arrival rate, "door-to-provider time," and the numbers of patients in the waiting room and ED boarders are all associated with patients LWBS.

Queuing Theory and Modeling Emergency Department Resource Utilization

Queueing theory is a discipline of applied mathematics that studies the behavior of lines. Queueing theory has successfully modeled throughput in a variety of industries, including within the emergency department (ED). Queueing equations model the demand for different processes within the ED, and help to factor in effects of variability on delays and service times. Utilization is a measure of the throughput of a process relative to demand, and provides a quick means of comparing the demand for certain resources. Although there have been some significant successes in applying queueing theory to EDs, the field remains underused within ED operations.

Challenges, consequences, and lessons for way-outs to emergencies at hospitals: a systematic review study

Background

Emergency Department (ED) overcrowding adversely affects patients’ health, accessibility, and quality of healthcare systems for communities. Several studies have addressed this issue. This study aimed to conduct a systematic review study concerning challenges, lessons and way outs of clinical emergencies at hospitals.

Methods

Original research articles on crowding of emergencies at hospitals published from 1st January 2007, and 1st August 2018 were utilized. Relevant studies from the PubMed and EMBASE databases were assessed using suitable keywords. Two reviewers independently screened the titles, abstracts and the methodological validity of the records using data extraction format before their inclusion in the final review. Discussions with the senior faculty member were used to resolve any disagreements among the reviewers during the assessment phase.

Results

Out of the total 117 articles in the final record, we excluded 11 of them because of poor quality. Thus, this systematic review synthesized the reports of 106 original articles. Overall 14, 55 and 29 of the reviewed refer to causes, effects, and solutions of ED crowding, respectively. The review also included four articles on both causes and effects and another four on causes and solutions. Multiple individual patients and healthcare system related challenges, experiences and responses to crowding and its consequences are comprehensively synthesized.

Conclusion

ED overcrowding is a multi-facet issue which affects by patient-related factors and emergency service delivery. Crowding of the EDs adversely affected individual patients, healthcare delivery systems and communities. The identified issues concern organizational managers, leadership, and operational level actions to reduce crowding and improve emergency healthcare outcomes efficiently.

Simulation of the Emergency Department Care Process for Pediatric Traumatic Brain Injury

The treatment of patients in the emergency department (ED) with severe pediatric traumatic brain injury (TBI) is challenging, and treatment process strategies that facilitate good outcomes are not well documented. The overall objective of this study was to identify factors that can affect the care process associated with pediatric TBI. This objective was achieved using a discrete-event simulation model of patients with TBI as they progress through the ED treatment process of a Level I trauma center. This model was used to identify areas where the ED length of stay can be reduced. The number of patients arriving at any given time was also varied in the simulation model to observe the impact to bed allocation policies and changes in staff and equipment. The findings showed that implementing changes in the ED (i.e., availability of two computerized tomography scanners, formation of resuscitation teams that included eight staff personnel, and modifying the bed allocation policy) could result in a 17% reduction in the mean ED length of stay. The study outcomes would be of interest to those (e.g., health administrators, health managers, and physicians) who can make decisions related to the treatment process in an ED.

Patient classification based on volume and case-mix in the emergency department and their association with performance

Predicting daily patient volume is necessary for emergency department (ED) strategic and operational decisions, such as resource planning and workforce scheduling. For these purposes, forecast accuracy requires understanding the heterogeneity among patients with respect to their characteristics and reasons for visits. To capture the heterogeneity among ED patients (case-mix), we present a patient coding and classification scheme (PCCS) based on patient demographics and diagnostic information. The proposed PCCS allows us to mathematically formalize the arrival patterns of the patient population as well as each class of patients. We can then examine the volume and case-mix of patients presenting to an ED and investigate their relationship to the ED's quality and time-based performance metrics. We use data from five hospitals in February, July and November for the years of 2007, 2012, and 2017 in the city of Calgary, Alberta, Canada. We find meaningful arrival time patterns of the patient population as well as classes of patients in EDs. The regression results suggest that patient volume is the main predictor of time-based ED performance measures. Case-mix is, however, the key predictor of quality of care in EDs. We conclude that considering both patient volume and the mix of patients are necessary for more accurate strategic and operational planning in EDs.

Could we employ the queueing theory to improve efficiency during future mass causality incidents?

Background

Preparation for a disaster or accident-related mass casualty events is often based on experience. The objective measures or tools for evaluating decision-making and effectiveness during such events are underdeveloped. Queueing theory has been suggested to evaluate the effectiveness of mass causality incidents (MCI) plans.

Objective

Using different types of real MCI, we aimed to determine if a queueing network model could be used as a tool to assist in preparing plans to address mass causality incidents.

Methods

We collected information from two types of mass casualty events: a motor vehicle accident and a dust explosion. Patient characteristics, time intervals of every working station, numbers of physicians and nurses attending, and time required by physicians and nurses during these two MCIs were collected and used for calculation in a queueing network model. Balanced efficiency was determined by calculating the numbers of server, i.e., nurses and physicians, in the two MCIs.

Results

Efficient patient flows were found in both MCIs. However, excessive medical manpower supply was revealed when the queueing network model was applied to assess the MCIs. The best fitting result, i.e., the most efficient man power utilization, can be calculated by the queueing network models. Furthermore, balanced efficiency may be a more suitable condition than the highest efficiency man power utilization when faced with MCIs.

Conclusion

The queueing network model is a flexible tool that could be used in different types of MCIs to observe the degree of efficiency when handling MCIs.

Electronic supplementary material

The online version of this article (10.1186/s13049-019-0620-8) contains supplementary material, which is available to authorized users.

An exhaustive review and analysis on applications of statistical forecasting in hospital emergency departments

Emergency departments (EDs) provide medical treatment for a broad spectrum of illnesses and injuries to patients who arrive at all hours of the day. The quality and efficient delivery of health care in EDs are associated with a number of factors, such as patient overall length of stay (LOS) and admission, prompt ambulance diversion, quick and accurate triage, nurse and physician assessment, diagnostic and laboratory services, consultations and treatment. One of the most important ways to plan the healthcare delivery efficiently is to make forecasts of ED processes. The aim this study is thus to provide an exhaustive review for ED stakeholders interested in applying forecasting methods to their ED processes. A categorisation, analysis and interpretation of 102 papers is performed for review. This exhaustive review provides an insight for researchers and practitioners about forecasting in EDs in terms of showing current state and potential areas for future attempts.

Optimizing Endoscope Reprocessing Resources Via Process Flow Queuing Analysis

The Massachusetts General Hospital (MGH) is merging its older endoscope processing facilities into a single new facility that will enable high-level disinfection of endoscopes for both the ORs and Endoscopy Suite, leveraging economies of scale for improved patient care and optimal use of resources. Finalized resource planning was necessary for the merging of facilities to optimize staffing and make final equipment selections to support the nearly 33,000 annual endoscopy cases. To accomplish this, we employed operations management methodologies, analyzing the physical process flow of scopes throughout the existing Endoscopy Suite and ORs and mapping the future state capacity of the new reprocessing facility. Further, our analysis required the incorporation of historical case and reprocessing volumes in a multi-server queuing model to identify any potential wait times as a result of the new reprocessing cycle. We also performed sensitivity analysis to understand the impact of future case volume growth. We found that our future-state reprocessing facility, given planned capital expenditures for automated endoscope reprocessors (AERs) and pre-processing sinks, could easily accommodate current scope volume well within the necessary pre-cleaning-to-sink reprocessing time limit recommended by manufacturers. Further, in its current planned state, our model suggested that the future endoscope reprocessing suite at MGH could support an increase in volume of at least 90% over the next several years. Our work suggests that with simple mathematical analysis of historic case data, significant changes to a complex perioperative environment can be made with ease while keeping patient safety as the top priority.

Simulating the behavior of patients who leave a public hospital emergency department without being seen by a physician: a cellular automaton and agent-based framework

The objective of this study was to develop an agent based modeling (ABM) framework to simulate the behavior of patients who leave a public hospital emergency department (ED) without being seen (LWBS). In doing so, the study complements computer modeling and cellular automata (CA) techniques to simulate the behavior of patients in an ED. After verifying and validating the model by comparing it with data from a real case study, the significance of four preventive policies including increasing number of triage nurses, fast-track treatment, increasing the waiting room capacity and reducing treatment time were investigated by utilizing ordinary least squares regression. After applying the preventing policies in ED, an average of 42.14% reduction in the number of patients who leave without being seen and 6.05% reduction in the average length of stay (LOS) of patients was reported. This study is the first to apply CA in an ED simulation. Comparing the average LOS before and after applying CA with actual times from emergency department information system showed an 11% improvement. The simulation results indicated that the most effective approach to reduce the rate of LWBS is applying fast-track treatment. The ABM approach represents a flexible tool that can be constructed to reflect any given environment. It is also a support system for decision-makers to assess the relative impact of control strategies.

First-Aid Treatment for Friction Blisters: "Walking Into the Right Direction?"

OBJECTIVE

Blisters are common foot injuries during and after prolonged walking. However, the best treatment remains unclear. The aim of the study was to compare the effect of 2 different friction blister treatment regimens, wide area fixation dressing versus adhesive tape.

DESIGN

A prospective observational cohort study.

SETTING

The 2015 Nijmegen Four Days Marches in the Netherlands.

PARTICIPANTS

A total of 2907 participants (45 ± 16 years, 52% men) were included and received 4131 blister treatments.

INTERVENTIONS

Blisters were treated with either a wide area fixation dressing or adhesive tape.

MAIN OUTCOME MEASURES

Time of treatment application was our primary outcome. In addition, effectiveness and satisfaction were evaluated in a subgroup (n = 254). During a 1-month follow-up period, blister healing, infection and the need for additional medical treatment were assessed in the subgroup.

RESULTS

Time of treatment application was lower (41.5 minutes; SD = 21.6 minutes) in the wide area fixation dressing group compared with the adhesive tape group (43.4 minutes; SD = 25.5 minutes; P = 0.02). Furthermore, the wide area fixation dressing group demonstrated a significantly higher drop-out rate (11.7% vs 4.0%, P = 0.048), delayed blister healing (51.9% vs 35.3%, P = 0.02), and a trend toward lower satisfaction (P = 0.054) when compared with the adhesive tape group.

CONCLUSIONS

Wide area fixation dressing decreased time of treatment application by 2 minutes (4.5%) when compared with adhesive tape. However, because of lower effectiveness and a trend toward lower satisfaction, we do not recommend the use of wide area fixation dressing over adhesive tape in routine first-aid treatment for friction blisters.

Decreasing Emergency Department Walkout Rate and Boarding Hours by Improving Inpatient Length of Stay

Introduction

Patient progress, the movement of patients through a hospital system from admission to discharge, is a foundational component of operational effectiveness in healthcare institutions. Optimal patient progress is a key to delivering safe, high-quality and high-value clinical care. The Baystate Patient Progress Initiative (BPPI), a cross-disciplinary, multifaceted quality and process improvement project, was launched on March 1, 2014, with the primary goal of optimizing patient progress for adult patients.

Methods

The BPPI was implemented at our system’s tertiary care, academic medical center, a high-volume, high-acuity hospital that serves as a regional referral center for western Massachusetts. The BPPI was structured as a 24-month initiative with an oversight group that ensured collaborative goal alignment and communication of operational teams. It was organized to address critical aspects of a patient’s progress through his hospital stay and to create additional inpatient capacity. The specific goal of the BPPI was to decrease length of stay (LOS) on the inpatient adult Hospital Medicine service by optimizing an interdisciplinary plan of care and promoting earlier departure of discharged patients. Concurrently, we measured the effects on emergency department (ED) boarding hours per patient and walkout rates.

Results

The BPPI engaged over 300 employed clinicians and non-clinicians in the work. We created increased inpatient capacity by implementing daily interdisciplinary bedside rounds to proactively address patient progress; during the 24 months, this resulted in a sustained rate of discharge orders written before noon of more than 50% and a decrease in inpatient LOS of 0.30 days (coefficient: −0.014, 95% CI [−0.023, −0.005] P< 0.005). Despite the increase in ED patient volumes and severity of illness over the same time period, ED boarding hours per patient decreased by approximately 2.1 hours (coefficient: −0.09; 95% CI [−0.15, −0.02] P = 0.007). Concurrently, ED walkout rates decreased by nearly 32% to a monthly mean of 0.4 patients (coefficient: 0.4; 95% CI [−0.7, −0.1] P= 0.01).

Conclusion

The BPPI realized significant gains in patient progress for adult patients by promoting earlier discharges before noon and decreasing overall inpatient LOS. Concurrently, ED boarding hours per patient and walkout rates decreased.

A Full-Capacity Protocol Allows for Increased Emergency Patient Volume and Hospital Admissions

PROBLEM

Our hospital was encountering problems with ED crowding. We sought to determine the impact of implementing a full-capacity protocol to respond to anticipated or actual crowding conditions. Our full-capacity protocol is based on collaboration among multiple hospital units.

METHODS

We completed a quality improvement initiative using a pre/post analysis of all ED patient encounters after implementing a full-capacity protocol with a corresponding period from the prior year. The principal outcomes measured were patient volume, admission rate, patient left without being seen (LWBS) rate, length of stay, and ambulance diversion hours.

RESULTS

In the post-full-capacity protocol period, a 7.4% increase in emergency patient encounters (P < .001) and an 11.9% increase in admissions (P < .001) were noted compared with the corresponding period in 2013. Also noted in the study period were a 10.2% decrease in LWBS rate (P = .29), an increase in length of stay of 34 minutes (P < .001), and a 92% decrease in ambulance diversion hours (111 fewer hours, P < .001).

IMPLICATIONS FOR PRACTICE

The collaborative full-capacity protocol was effective in reducing LWBS and ambulance diversion, while accommodating a significant increase in ED volume and increased hospital admission rates at our institution.

A Queue-Based Monte Carlo Analysis to Support Decision Making for Implementation of an Emergency Department Fast Track

Emergency departments (EDs) are seeking ways to utilize existing resources more efficiently as they face rising numbers of patient visits. This study explored the impact on patient wait times and nursing resource demand from the addition of a fast track, or separate unit for low-acuity patients, in the ED using a queue-based Monte Carlo simulation in MATLAB. The model integrated principles of queueing theory and expanded the discrete event simulation to account for time-based arrival rates. Additionally, the ED occupancy and nursing resource demand were modeled and analyzed using the Emergency Severity Index (ESI) levels of patients, rather than the number of beds in the department. Simulation results indicated that the addition of a separate fast track with an additional nurse reduced overall median wait times by 35.8 ± 2.2 percent and reduced average nursing resource demand in the main ED during hours of operation. This novel modeling approach may be easily disseminated and informs hospital decision-makers of the impact of implementing a fast track or similar system on both patient wait times and acuity-based nursing resource demand.

Impact of a Hospitalwide Quality Improvement Initiative on Emergency Department Throughput and Crowding Measures

BACKGROUND

This pre- and postintervention analysis evaluates the impact of a systemwide, comprehensive, executively supported quality improvement (QI) project on emergency department (ED) throughput measures and crowding in a large nonacademic community hospital.

METHODS

The two primary endpoints used to assess the impact of the project were (1) the percentage of all patients who were door-in to door-out in less than three hours and (2) the percentage of patients who left without being seen (LWBS). Secondary endpoints for throughput were mean door-in to door-out, door-in to physician, physician to disposition, and disposition to door-out times for all patients. Secondary endpoints for crowding were median disposition to door-out time of admitted patients and the percentage of admitted patients with a disposition to door-out time of ≥ one, two, and six hours.

RESULTS

A total of 666,640 patient visits were included in the primary endpoint analyses, with no patients excluded. The percentage of patients meeting the three-hour door-in to door-out goal after the QI project was 81.4%, versus 46.5% in the pre-QI group (difference, 34.9 percentage points; 95% confidence interval [CI] = 34.7-35.1; p < 0.0001). The postintervention LWBS rate was 0.49%, versus 4.00% in the pre-QI group (difference, 3.51 percentage points; 95% CI = 3.43-3.58; p < 0.0001). A total of 417,673 patient visits were screened for inclusion for the secondary endpoint analyses. The pre-QI and post-QI groups were also compared for secondary endpoints, and significant improvement was noted in all analyses.

CONCLUSION

This study suggests that a comprehensive systemwide and executively supported QI project can make sustained multiyear improvements in ED throughput and LWBS. Further research is needed to determine if this standardized set of changes can be generalized to other hospital systems.

Population health and the academic medical center: the time is right

Optimizing the health of populations, whether defined as persons receiving care from a health care delivery system or more broadly as persons in a region, is emerging as a core focus in the era of health care reform. To achieve this goal requires an approach in which preventive care is valued and "nonmedical" determinants of patients' health are engaged. For large, multimission systems such as academic medical centers, navigating the evolution to a population-oriented paradigm across the domains of patient care, education, and research poses real challenges but also offers tremendous opportunities, as important objectives across each mission begin to align with external trends and incentives. In clinical care, opportunities exist to improve capacity for assuming risk, optimize community benefit, and make innovative use of advances in health information technology. Education must equip the next generation of leaders to understand and address population-level goals in addition to patient-level needs. And the prospects for research to define strategies for measuring and optimizing the health of populations have never been stronger. A remarkable convergence of trends has created compelling opportunities for academic medical centers to advance their core goals by endorsing and committing to advancing the health of populations.

Using queuing theory and simulation model to optimize hospital pharmacy performance

BACKGROUND

Hospital pharmacy is responsible for controlling and monitoring the medication use process and ensures the timely access to safe, effective and economical use of drugs and medicines for patients and hospital staff.

OBJECTIVES

This study aimed to optimize the management of studied outpatient pharmacy by developing suitable queuing theory and simulation technique.

PATIENTS AND METHODS

A descriptive-analytical study conducted in a military hospital in Iran, Tehran in 2013. A sample of 220 patients referred to the outpatient pharmacy of the hospital in two shifts, morning and evening, was selected to collect the necessary data to determine the arrival rate, service rate, and other data needed to calculate the patients flow and queuing network performance variables. After the initial analysis of collected data using the software SPSS 18, the pharmacy queuing network performance indicators were calculated for both shifts. Then, based on collected data and to provide appropriate solutions, the queuing system of current situation for both shifts was modeled and simulated using the software ARENA 12 and 4 scenarios were explored.

RESULTS

Results showed that the queue characteristics of the studied pharmacy during the situation analysis were very undesirable in both morning and evening shifts. The average numbers of patients in the pharmacy were 19.21 and 14.66 in the morning and evening, respectively. The average times spent in the system by clients were 39 minutes in the morning and 35 minutes in the evening. The system utilization in the morning and evening were, respectively, 25% and 21%. The simulation results showed that reducing the staff in the morning from 2 to 1 in the receiving prescriptions stage didn't change the queue performance indicators. Increasing one staff in filling prescription drugs could cause a decrease of 10 persons in the average queue length and 18 minutes and 14 seconds in the average waiting time. On the other hand, simulation results showed that in the evening, decreasing the staff from 2 to 1 in the delivery of prescription drugs, changed the queue performance indicators very little. Increasing a staff to fill prescription drugs could cause a decrease of 5 persons in the average queue length and 8 minutes and 44 seconds in the average waiting time.

CONCLUSIONS

The patients' waiting times and the number of patients waiting to receive services in both shifts could be reduced by using multitasking persons and reallocating them to the time-consuming stage of filling prescriptions, using queuing theory and simulation techniques.