Introduction
Background
According to Jin et al. (2013), waiting time for GP services in hospitals has been a major difficulty to the delivery of quality healthcare in the United Kingdom. The government policies focused on reducing the waiting time in the previous decades, yet it has remained the long routing for hospital procedures. One of the major objectives of the UK government became implementing policy reform to reduce GP waiting time (p. 2191).
As the population increases, the public outpatient services face a challenge of delivering services to patients in time. There is an increasing demand for General Practice services, both primary care and GPs since the baby generation are moving to develop at a high rate. On the other hand, medical resources like medical facilities and doctors are not expanding at the required rate to accommodate the increase in demand. The result is an overflow of hospitals with patients in needs of services. It follows that healthcare providers and hospitals must use the available General practitioners but rely on an improved flow control as well as a better capacity apportionment to reduce the adverse effect of patient long waiting time in GP (Jin et al., 2013, p. 2193).
This simulation project develops the current situation in the Carters Green Medical Centre GP in the United Kingdom regarding the arrival time and the time of completion of services. The GP in this facility are facing delays in waiting time. After the modeling, there should be a reduction in the time spent as waiting and service time. Many other variables in the healthcare management system affect the patient waiting time, which includes the availability of resources at the GP center. This simulation chooses factors that are controllable and implementing the control system to register a reduced waiting time in GP. Variables like the growth of resources are a difficulty of corporate planning, which the company has to try to beat the problem without relying on resource increase (Mandahawi et al., 2010, p. 94) The conclusions drawn from the simulation are meant to reduce the problem of waiting time in GP.
Aim and Objectives
Aim
The aim of this simulation project is to identify the main factors that trigger long waiting time for General Practice by making an analysis of the effects of the selected factors that are controllable then use the data to redesign the process to ensure a reduction in the waiting time. The project aims at realizing a process for General Practice that will reduce the overall time that patients spend. This simulation is moved by the vision to solve the problem of long waiting time for patients in GP, which would be the beginning of improving the efficiency of the whole process of medical practice (McGahon, 2013, p. 37).
Objectives
The specific objectives of the simulation project are
- To improve the process of first appointments to incorporate a new patient assessment session that would increase productivity and efficiency. The new patient assessment will be managed at more than consultation room to reduce patient waiting time on the line and referrals.
- To improve the follow-up treatment process to avoid the possibility of treating one patient more than once. It would be achieved through evidence-based group therapy; individual follow-up treatment and apportionment of instructors to specific patients.
- To ensure the conditions of patients is solved within a few contact sessions hence achieving less time at GP. Carrying out treatment within a short time makes it possible for patients to heal faster than if they took a longer time.
Proposed model
Description
A discrete event simulation of the General Practice service will be developed using Simul8 software. As indicated earlier, GP services are consultation based. The simulation will help the management to realize ways of reducing patient waiting time. Fulfilling this model requires process before the consultation room, consultation, and services after consultation like payment, financial counseling, biometric station, clerking station and treatment, waiting time before and within be also included.
Determining the arrival process will be based on a probability distribution of the concentration of arrival is not uniform throughout the day and the week. Most arrivals before 8 am occur within the last 30 minutes. A single distribution is used for the period between 7 am and 1:30 pm. Then arrivals after 4:30 pm are rare. The probability of general patient arrival comes at 0.65. The patient is then assigned to a specific doctor randomly. The patient is served by the next available doctor and based on their past characteristics like HVR, VA, and Refraction. The dilation process is checked within 30 minutes before the patient goes for consultation. Returning patients will have their dilation checked before consultation while first-time patients’ dilation will be checked after the consultation is done. After dilation, the patient goes for a second consultation. When patients are in the waiting room, the next general patient is called in by the first available general practice doctor. A subspecialty patient is served by a prespecified doctor in the subspecialty pool. The patient with the earliest appointment is served first based on a first-come-first-served basis.
Routing
The Simul8 model will be used to this simulation. The process begins with the arrival of the patient at the premises. Then the patient will either undergo Retraction, VA dilation, or HVF before proceeding to consultation. Here, the patient goes through dilation then back to consultation. The patient can then either proceed to the payment of financial counseling before they exit.
Data Collection and Related Issues
Items on Data to be collected
The simulation analyses the impact of various variables on patient waiting time. Items on data collected the arrival pattern of patients, the appointment schedule, and the process flow. Arrival patterns are used to simulate arrival of the patient on a typical day. Appointment schedules determine merging the system while the process flow of duration between registration and exit is assessed (Knight et al., 2005, p. 101).
Time Dependency Behavior
The time dependency, in this case, is based on randomised time. Patients arrive at the random time, and the pattern is also random. The consultation period is restricted to below 10 minutes. The nature of the patient as either first-time or returning determines the time they take.
Routing and Other Issues
The simulation is based on the assumption that at any given time, resources are either occupied or available for patients. It assumes that external factors will not at any point affect resource availability. Also, registration resources are unlimited.
Key performance indicators
Performance Measures
The performance measure for this simulation is mainly based on daily waiting time for GP patients. The waiting time is defined as the time between arrival and last consultation. This process will involve checking the average waiting time, the median and the 95th percentile waiting time.
Experimentation issues
This experiment is to be done in a hospital scenario, which has random trends. Thus the unpredictability of events may adjust the results of the simulation. The model will be validated by comparing the output of the simulation and the actual outcome of processes in a GP (Cimellaro et al., 2016, p. 20). Considering all the highlighted issues will reduce waiting time in a GP.
Bibliography
- Cimellaro, G.P., Malavisi, M. and Mahin, S., 2016. Using Discrete Event Simulation Models to Evaluate Resilience of an Emergency Department. Journal of Earthquake Engineering, pp.1-24.
- Jin, X., Sivakumar, A.I. and Lim, S.Y., 2013, December. A simulation based analysis on reducing patient waiting time for consultation in an outpatient eye clinic. In Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World (pp. 2192-2203). IEEE Press.
- Knight, A.W., Padgett, J., George, B. and Datoo, M.R., 2005. Reduced waiting times for the GP: two examples of” advanced access” in Australia. Medical Journal of Australia, 183(2), p.101.
- Mandahawi, N., Al-Shihabi, S., Abdallah, A.A. and Alfarah, Y.M., 2010. Reducing waiting time at an emergency department using the design for Six Sigma and discrete event simulation. International Journal of Six Sigma and Competitive Advantage, 6(1-2), pp.91-104.
- McGahon, H., 2013. Service improvement: Reducing physiotherapy outpatient waiting times. Cumbria Partnership Journal of Research Practice and Learning, 3(1), 37-41
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