Scenarios to Improve the Patient Experience Time in a Tertiary Academic Hospital Using Simulation

Background Shortening the patient experience time (PET) in the emergency department (ED) improves patient quality and satisfaction and reduces mortality and morbidity. Worldwide, the PET target in the ED is ≤ 6 hours; however, the PET awaiting admission to inpatient Medicine at Hamad General Hospital (HGH) in the Qatar State, through ED is currently 15.3±6.4 (mean ± SD) hours. Aim Identify solutions to reduce the PET duration at HGH-ED to the international target. Method A cohort study was done using the Discrete-event simulation (DES) model, utilizing a commercial simulation software package (Process Model Inc., Utah, version 5.2.0). One-year data, January 1, 2019 - December 30, 2019, was analyzed and found to follow seven subprocesses. The duration of each subprocess was recorded, and the average time was calculated. A computer simulation scheme was developed for all the subprocesses of the actual PET duration. The simulated PET was validated, and scenarios were proposed and assessed for each subprocess separately and in combination, A constructed simulatory design using an iterative process involving a construction model. This model starts with the logical organization of submitted tasks based on their cycle times. A subject-matter expert interview was conducted to determine the appropriateness and frequency of actions. The duration of each activity in the considered process was defined using a triangular distribution. Results The actual PET duration for the Medical Department was 15.3±6.4 (mean + SD) hours. The three most prolonged PET subprocess durations were in the referral to internal medicine, the decision to admit, and finding a free bed; these represent 17.9%, 53.8%, and 16.7% of the PET, respectively. Adding two physicians to each shift, which shortens the subprocess of the decision to admit, reduced the PET duration by 27.5%. Moreover, creating a new admitting team (unit) that takes care of new patients admitted to the ED reduced PET duration by another 12.5%. Combining these two scenarios reduced the average PET duration to only 10.2±0.5 hours. In addition to these scenarios, the PET can be further decreased to six hours by increasing the number of inpatient beds. Conclusions The simulated scenarios indicated that restructuring the medical teams, adding two physicians to each shift, and creating an admissions team dedicated to the ED would reduce the total PET duration to 10.2 hours, Furthermore, PET's further reduction to six hours is predictable by increasing the bed number.


Introduction
Waiting times for elective care [1], and emergency interventions are considered major problems (such as overcrowding and long waiting time for medical care) in the healthcare system, and patient wellness, impaired care access, and patient dissatisfaction [2,3]. One reason for the delay in providing the best flow of health services is the administrative boundaries. The time elapsed between the patient presenting to the emergency department (ED) and reaching the allocated bed are system efficiency measure. 1 2 1 1 For over two centuries, policymakers have progressively tackled delay problems (such as bed availability, inside hospital transportation, nurse availability, and bed management system) before patients reach their assigned beds [4]. Supply and demand policies have been launched to address challenges caused by excessive waiting times [5]. Initiatives have been proposed based on urgency and management requirements for medium-or high-risk patients [4], or patient choice programs [6,7]. For instance, in 2001, Norway abstained from rules impeding provider referrals to a single district, hoping to create a competitive referral system for elective admissions and care [8].
Although there are varying crosstalk and optimism regarding the potential benefits of patient choice, multiple recent reviews have reported minor and insignificant evidence of the tremendous opportunities for such effects [6,9]. A UK single-hospital study investigating the impact of patient choice for the facility on patient waiting times noted that more options were significantly linked with shorter waiting times, and the quantitative effect was moderately improved [10]. However, the London Patient Choice Project data reported that waiting time was significantly reduced in all patients, including those enrolled in the project [7].
An assessment of the geographical transfer of patients between hospitals across administrative boundaries was illustrated in a Canadian study on patients waiting for elective surgery [11]. A 2020 study reported that implementing a public wait-time website reduced the number of patients who attended crowded hospitals and moved them to other less overcrowded hospitals, leading to a significant reduction in waiting time. However, it adversely affects waiting times in other hospitals [12].
Prolonged patient experience time (PET) causes significant ED congestion, consequent decrease in the quality of care, increased morbidity and mortality, a low threshold of patient satisfaction, extended hospitalization, increased bed occupancy, and increased pressure on ED bed availability [13,14,15]. Therefore, this study aimed to identify the subprocesses with the longest time in the current admission process and test the simulation changes to identify improvements that can be conducted at the lowest possible cost.

Simulation
Simulation has several types, including continuous, agent-based, and discrete events [16]. Over the past two decades, modeling and simulation have been used remarkably well in the global healthcare industry, which depends on finding and developing solutions to increase the efficiency of this vital sector. This sector's modeling and simulation start by addressing the flow of healthcare beneficiaries, emergency rooms, and wards to review and simulate diseases and epidemics for populations within a range. This platform is used to develop models for measuring, evaluating, analyzing, and predicting the performance of healthcare systems [17]. The data sources and calculations required to implement and operate the simulation model were used for implantation in real situations.

Current patient care and admission process
Patients admitted to the Hamad General Hospital (HGH) Emergency Department (ED) had multiple subprocesses. The sub-processes are registration and assessment by the ED nurse and ED doctor, who usually order preliminary investigations. A specialty physician was contacted to assess and decide whether to admit the patient after the assessment subprocess. However, the latter may contain additional queries.
Step three involved coordinating bed management, setting the bed, and transferring the patient to their designated space. Any delay in these steps lengthens the patient's stay in the ED.
Discrete-event simulation (DES) is a system operation that separates a sequence of events in time. Every event at a selected point first marks a modification of the system state [12]. Among successive events, no change in the system is expected to appear; therefore, the simulation time will jump directly to the incidence time of the subsequent event, which is termed the next-event time progression. Thus, DES is a method for building models to simulate systems that depend on the distribution of imitation time as a function of future events at different times ( Figure 1).

FIGURE 1: Proposed simulation model: Discrete-event simulation (DES) is the modeling of systems in which the state variable changes only at a discrete set of points in time
This cohort project aims to use DES to explore the best scenarios that reduce PET and examine their impact on the different subprocesses of patient admission. DES is a technique used to represent real-world systems that may be divided into a series of conceptually distinct processes that independently advance through time. Each event in a particular process is given a logical time. This event's results may be passed on to one or more additional procedures. The outcome's content may result in the development of additional events to be handled at a future logical time. Therefore, due to the appropriateness of this system to the aim of the study, DES was used. Hence, we collected one-year data on the different components of the period spent in the ED for patients in whom a decision was made for admission to the medical department. Data were collected from the electronic record system (Cerner), physicians, nurses, bed managers, and administrative officers in the ED, between January 2019 and December 2019, and the relevant subprocesses were identified ( Figure 2) and modeled. The electronic system (Cerner) that is used in the hospital is a closed system and the information can not be entered except by an official employee, and not possible to change the information saved. Furthermore, during the data collection, a significant number of interviews were conducted with patients, physicians, nurses, bed managers, other supportive teams (workers transferring patients), and administration employees about their thoughts to redirect the PET duration. The actual and model data were compared based on the number of physicians on the admission team and the number of patients waiting for transfer.

Statistical analysis
The patient transfer model from the ED to inpatient wards was implemented using a commercial simulation software package (Process Model Inc., Utah, version 5.2.0). Historical data were used to estimate the probability distributions of the model input data analysis using Easy Fit 5.6 Professional (Math Wave Technologies, 2015). Qualitative variables were described as frequencies and percentages.

Actual admission model & durations
PET, defined as the period from the patient's entrance to HGH ED reception until the patient is delivered to the assigned bed, includes seven subprocesses ( Table 1 and Figure 2): registration, triage, assessment, referral to medicine, the decision to admit, allocation of bed, and physical transfer of the patient to the assigned bed. The human power capacity, ED bed capacity, and performance metrics are presented in Table  2.

Subprocess Resource
Actual Data Simulation pvalue* Average Duration (minutes, means ± SD) Percentage of total PET Duration deduced from the simulated model.   visualize and quantitatively analyze its performance. The process model is the most effective tool for performing quantitative 'what-if' analysis and plays different scenarios of the process behavior as its conditions and variables change with time. This simulation capability allows experiments to be performed on a computer display and test solutions to evaluate and determine their effectiveness before real implantation. Typical applications include staff and capacity planning, cycle time; throughput capability; and resource utilization.

Experimental (simulation) model
Formalized elements and mechanisms were identified as relevant by parameters and variables, using data observed over time. Historical data were created for patients' admission processes ( Table 3 and Figure 2). A simulatory design was constructed using an iterative process involving model construction, which began with the logical organization of submitted tasks based on their cycle times, representing resource utilization. A subject-matter expert interview was conducted to determine the appropriateness and frequency of actions and to collect information on the professional rules restricting these actions. The interview involved the physician, nurses, bed managers, and administration officers. The duration of each activity in the considered process was defined using a triangular distribution, and the parameters used are listed in Table 3.  Different scenarios were proposed and tested to determine the best strategy for reducing patient admissions. Scenarios designed for a particular intervention added one or more changes to the subprocesses, and the influence of the intervention on the entire admission PET scan was assessed. The research team members proposed the scenarios based on the collected data and their personal views, experience in the facility, and knowledge about the common problems they faced. Furthermore, the simulation program used according to the given data suggests different practical solutions to reduce the PET duration. On these bases, nine different scenarios were proposed and simulated ( Table 3). Scenarios were developed by adding one or more staff members to each subprocess to detect changes in the operational duration of the subprocesses and the duration of the entire process. After testing each scenario's effect on the PET duration, the combination of two or three scenarios that affected the PET significantly was tested to detect the best combination that improved the PET duration. The developed simulation model was validated, verified to be representative, and used to determine the best scenario for reducing PET in patients. Accordingly, visual tracking of each group of blocks was verified to ensure that the care path was correct. Furthermore, senior managers validated the conceptual and generic simulation models.

Scenario description
A simulation model was created for approximately 3,700 simulated patients over the course of three months. Accordingly, the simulation model was driven by real-time and actual patient data. The major sources of data were electronic records (Cerner), firsthand observations, and staff interviews (nurses, physicians, bed managers, and administration officers). The simulation model was routinely calibrated and executed for 180 days. To decrease system variation caused by chance, ten replications were carried out. The tested model accurately represented the system, resulting in a PET duration for admission that was comparable to the observed data. The simulation is as realistic as feasible due to the average operational parameter values recorded throughout the experiment. The simulation model was constructed using operational parameters ( Table 2).

Results
In 2019, 386,889 patients visited the ED, where 30,185 patients were referred to the medical teams for admission to internal medicine. The decision to admit 19,058 patients was made, corresponding to an average of 52±28 admissions per day (range 28-79 patients). The monthly admission ranged between 707-1331 patients (median of 955 per month) (Figure 3). The observed PET duration was 15.36.4 (mean ± SD) hours, corresponding to a median of 17 hours and an IQR of 9.4-20.5 hours. The most extended PET subprocess durations were noted in the referral to internal medicine and the decision to admit and find a free bed, representing 17.9%, 53.8%, and 16.7% of PET, respectively ( Table 1).
The impact of changing the numbers of doctors, nurses, and beds was examined in nine scenarios ( Table 3). Each scenario resulted in PET improvement; however, combining scenarios six and nine (new scenario 10 in Table 4) further reduced the PET with minimal extra cost ( Table 4 and Figure 4). Scenarios six and nine were chosen because scenario six improved the PET by 27.5% and scenario nine reduced the PET by 12.5%. Therefore, their combination (scenario nine) improved the PET by 40%. The minimal cost increase is possible only if two new physicians are hired, but if we used the already working physicians, there will be no significant extra cost.  Furthermore, adding the number of beds will improve the PETas illustrated in Figure 4.

Discussion
The total number of 248 inpatient beds allocated to medical patients included 51 beds (20%) for the acute medical assessment unit (AMAU), 24 beds (10%) for the acute assessment unit (AAU), and 173 beds (70 %) as inpatient wards. The AMAU is used to admit a high-turnover patient with a predicted length of stay of fewer than 72 hours and is located within the main hospital tower. AAU is used to rapidly assess patients likely to be admitted for a shorter period for observation, quick investigations, or planned procedures. The admission percentage distributions among the three internal medicine sections were 45%, 35 %, and 20% of the main internal medicine ward beds, AAU beds, and AMAU beds. The average duration of the admission process shows a PET of 17 hours ( Table 1). The time study data analysis showed a normal parametric distribution (used to model the service times). Only 7.9% of the admitted patients had a total admission duration within the six-hour target ( Figure 5).

Conflicts of interest:
In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.