In this study, we have aimed to optimize patient flow in emergency departments while minimizing associated costs. In order to be able to compare the effect of any changes, we developed a simulation model for an emergency department using Queuing theory, and regarding the optimization we utilized Genetic Algorithm to find the best change. Basically, we have designed a Discrete Event based, multi-class, multi-server queuing network as we have considered the emergency department a set of stages associated with a queue of patients waiting to be served. Each stage has multiple service providers such as Nurses, Doctors or other staff. We also classified patients passing through the stages, according to their acuity level and personal characteristics. Then, we defined a function as a measure of the ED performance in respect to the calculated wait times and the cost. Finally, we developed a customized genetic algorithm to find the best performance which reflects the best allocation of service providers into multiple stages of the emergency department.

We analyze the patient flow of three community health clinics from the Seton group in Austin, Texas, using simulation tools. Our goal is to help the clinics find solutions to cope with increasing patient demand. Several scenarios for increasing efficiency are explored using an ARENA-based patient flow model. Multiple bottlenecks are identified and solutions are found to help the clinics minimize overall patient cycle time and to distribute the workload more evenly across the staff. This study demonstrates that healthcare service facilities may benefit from quantitative analysis, especially simulation tools, to improve their efficiency. / text

Emergency Departments have begun implementing new patient flow processes aimed at improving ED throughput and limiting ED crowding. The purpose of this study was to evaluate the effectiveness of two flow processes. <br>This was a retrospective quasi-experimental study designed to evaluate the impact of a Rapid Medical Assessment process versus Fast Track process on improving ED throughput. Data analysis included descriptive statistics and two-factor analyses of covariance (ANCOVA). ANCOVA statistics were calculated using “arrival to first provider contact time” and “arrival to departure time” as the dependent variables and RMA process versus FT process as well as ESI levels as the independent variables. There was a significant difference in the mean arrival to first provider contact times for all patients during all hours, F (1, 5744) = 9.5, p = .002. There was also a significant difference in the mean arrival to first provider contact time for low-acuity patients during all hours, F (1, 3131) = 14.6, p = < .001 <br>There was a significant difference in the mean arrival to departure times for all patients during all hours, F (1, 6079) = 5.8, p = .016. There was no significant difference in the mean arrival to departure times for low-acuity patients during all hours, F (1, 3306) = 0.774, p = .379, or for all patients during FT hours, F (1, 2647) = 1.1, p = .295. The results of the study support the belief that rapid evaluation and disposition of low-acuity patients improve ED efficiency and reduce ED crowding. / School of Nursing; / Nursing / PhD; / Dissertation;

No description available.

Yes / In the last six years, hospitals in developed countries have been trialling the use of command centres for improving organisational efficiency and patient care. However, the impact of these Command Centres has not been systematically studied in the past. Methods: It is a retrospective population based study. Participants were patients who visited Bradford Royal Infirmary Hospital, accident and emergency (A&E) department, between Jan 01, 2018 and August 31, 2021. Outcomes were patient flow (measured as A&E waiting time, length of stay and clinician seen time)and data quality (measured by the proportion of missing treatment and assessment dates and valid transition between A&E care stages).Interrupted time-series segmented regression and process mining were used for analysis. Results: A&E transition time from patient arrival to assessment by a clinician marginally improved during the intervention period; there was a decrease of 0.9 minutes (95% CI: 0.35 to 1.4), 3 minutes (95% CI: 2.4 to 3.5), 9.7 minutes (95% CI: 8.4 to 11.0) and 3.1 minutes (95% CI: 2.7 to 3.5) during ‘patient flow program’, ‘command centre display roll-in’, ‘command centre activation’ and ‘hospital wide training program’, respectively. However, the transition time from patient treatment until conclusion of consultation showed an increase of 11.5 minutes (95% CI: 9.2 to 13.9), 12.3 minutes (95% CI: 8.7 to 15.9), 53.4 minutes (95% CI: 48.1 to 58.7) and 50.2 minutes (95% CI: 47.5 to 52.9) for the respective four post-intervention periods. Further, length of stay was not significantly impacted; the change was -8.8hrs (95% CI: -17.6 to 0.08), -8.9hrs (95% CI: -18.6 to 0.65), -1.67hrs (95% CI: -10.3 to 6.9) and -0.54hrs (95% CI: -13.9 to 12.8) during the four respective post intervention periods. It was a similar pattern for the waiting and clinician seen times. Data quality as measured by the proportion of missing dates of records was generally poor (treatment date=42.7% and clinician seen date=23.4%) and did not significantly improve during the intervention periods. Conclusion: The findings of the study suggest that a command centre package that includes process change and software technology does not appear to have consistent positive impact on patient safety and data quality based on the indicators and data we used. Therefore, hospitals considering introducing a Command Centre should not assume there will be benefits in patient flow and data quality. / This project is funded by the National Institute for Health Research Health Service and Delivery Research Programme (NIHR129483).

No description available.

The hospital emergency department (ED) is a critical source for health care amid a complex healthcare system in the United States. It is the gateway to care for a broad range of people, arriving from a variety of locations. With this wide reaching net and a decreasing trend in hospital beds, EDs throughout the United States are experiencing overcrowding. ED crowding has various tactical and strategic facility management impacts ranging from facility occupancy issues to adverse health outcomes. Among other factors, recent research has cited the sharp increase in ED visits over the years and ED patient boarding as key contributors to crowding. Home hospital care is a model in which health care is delivered at an individual’s home as a substitute for hospital-level inpatient short-term acute care. Clinical research has shown home hospital to be an effective care model for select illnesses presenting frequently to EDs, such as congestive heart failure, community acquired pneumonia, chronic obstructive pulmonary disease, and cellulitis. While there exist distinct clinical and social criteria for which delineate eligible individuals, home hospital care models have been linked with the potential to free inpatient beds. The overarching objective of this study is to investigate the relationship between home hospital care and ED crowding. To achieve this objective, the study examined the relationship between home hospital care and ED crowding, specific to ED boarding performance at a large, urban, teaching hospital facility. A methodology for identification of potential home hospital patients was used through clinical and social criteria, and a scale for the range of clinical eligibility rates was established for the five suitable illnesses. The study modeled patient flow and bed demand, and utilized computer simulation modeling to assess the impact of home hospital care on ED boarding performance. Various models were simulated to represent different home hospital intervention types. The models incorporated home hospital through an ED Referral program, Inpatient-Transfer Referral program, Community Referral program, and a fully integrated home hospital program. Three scenarios were run for each model to assess practical possibilities for the utilization of the freed bed hours from a home hospital program. This research contributes insight and understanding of home hospital’s impacts on ED crowding. The insight from this study quantifies the effects of a home hospital program on ED boarding and inpatient bed demand. The modeling study is contributes an analytical understanding of the impacts that home hospital could potentially have on crowding, which could prove useful in the struggle against ED congestion. This understanding helps to provide a more thorough understanding of home hospital, and could aid in an organization’s decision-making process of whether to implement a program. The presented modeling methodology for analyzing home hospital and ED crowding can also be used as a model format for researchers and practitioners for analytical purposes in future studies.

Alternate Level of Care (ALC) patients are patients that stay in the acute care setting while waiting to be transferred to an ALC facility. They are not receiving the appropriate type of care and are occupying acute care resources. ALC patients occupy 5,200 patient beds everyday in Canada, and 12 percent of these ALC patients die during their waiting period. This study evaluates Toronto General Hospital's (TGH) discharge policy in the General Surgery and General Internal Medicine (GIM) departments using a discrete-event simulation. For long-term care ALC patients, it was found that applying to one extra application or maximizing the number of short waiting list facilities in their total number of applications significantly reduces the number of ALC days and the number of died in hospital patients. Knowing if discharge policies can decrease ALC days is not only significant to TGH but also to other health care institutions.

Alternate Level of Care (ALC) patients are patients that stay in the acute care setting while waiting to be transferred to an ALC facility. They are not receiving the appropriate type of care and are occupying acute care resources. ALC patients occupy 5,200 patient beds everyday in Canada, and 12 percent of these ALC patients die during their waiting period. This study evaluates Toronto General Hospital's (TGH) discharge policy in the General Surgery and General Internal Medicine (GIM) departments using a discrete-event simulation. For long-term care ALC patients, it was found that applying to one extra application or maximizing the number of short waiting list facilities in their total number of applications significantly reduces the number of ALC days and the number of died in hospital patients. Knowing if discharge policies can decrease ALC days is not only significant to TGH but also to other health care institutions.

The increase in patients presenting to the emergency department (ED) for primary care poses a serious safety issue in the care that can be provided. In a care area that is overcrowded, physicians, nurses, ancillary department staff, and other care team members may have a difficult time delivering care. Poorly managed flow in the ED leads to overcrowding, and patients with life-threatening illnesses are less likely to be transitioned to designated specialized areas in a safe and efficient manner. The practice-focused question was whether processes to improve the flow of patients entering the ED decreased the number of patients leaving without being seen, decreased time from the time entering the ED to hospital admission, improved the average length of stay, and increased patient satisfaction. The plan-do-check-act methodology was used to address this quality improvement project. Results of the project demonstrated a decrease in the number of patients leaving without being seen, a decrease in the time entering the ED to hospital admission, a decrease in average length of stay, and an increase in patient satisfaction. This project provided positive social change to the patients, families, organization, and community by improving the ED processes to ensure patient needs were addressed as rapidly as possible.