As Part Of A Specific Disease Prevention Program, A Clinic

As Part Of A Specific Disease Prevention Program A Clinic I

As part of a specific disease prevention program, a clinic is considering setting up a screening service in a town. They are considering the following design: after waiting in a single line, each walk-in patient is served by one of three available nurses. Each nurse has their own booth, where the patient is first asked some medical health questions, then the patient’s vitals are taken, and finally, a glucose test is performed to check for the disease. In this design, each nurse performs the tasks in sequence for the patient. If the glucose test indicates a chance of the disease, the patient is sent to a separate clerk to schedule an appointment for a follow-up at the clinic. If the test is not positive, then the patient departs. Patients are eager to get tested but are reluctant to wait for the screening operations. To clarify, if the number of people waiting for nurses is larger than 4, they prefer to go to a park nearby the screening booths and spend 15 minutes there. After this period, they return to check if the number of people is acceptable or not. Noting that, there is no limit on the number of times for park visiting; patients can be screened eventually. Patients arrive according to an exponential distribution with a mean of 4 minutes. There is a 5% chance that the glucose test will be positive. The time to complete paperwork, take vitals, and perform glucose testing are all lognormally distributed with means of 6.5, 6.0, and 5.5 minutes, respectively, each with a standard deviation of approximately 0.5 minutes. The scheduling of a follow-up appointment follows a Weibull distribution with parameters 2.6 and 7.3 (in minutes). Travel time between the park and screening booths is uniformly distributed between 1 and 2 minutes. Nurses work based on a schedule with staggered breaks: one nurse begins a break, and immediately upon return, the next nurse takes a break, until all nurses have had their breaks. Nurses wait until they complete their current patient before taking breaks. The screening begins at 8:00. The first set of 10-minute breaks starts at 10:00; after 4 hours of work (at 12:00), there is a 30-minute lunch break; and at 15:00, a 10-minute break occurs. The booths close at 17:00. For this simulation, collect statistics over 30 replications for queue length, queue time, and resource utilization. Also, determine the average number of positive and negative patients screened, as well as the average cycle times for both groups.

Paper For Above instruction

This study presents a detailed simulation analysis of a disease screening process at a community clinic, focusing on the efficiency and resource utilization of a multi-stage, nurse-led screening model integrated with patient behavior dynamics. The simulation aims to assess how operational procedures, patient preferences, and staffing schedules influence key performance metrics such as queue lengths, waiting times, screening outcomes, and resource utilization rates over a typical workday.

The clinic's screening process involves a sequential task flow performed by three nurses. Patients arrive following a Poisson process, with an average inter-arrival time of 4 minutes, reflecting real-world variability in patient arrivals. Upon arrival, patients join a single queue and are directed to one of the available nurses. Each nurse conducts three tasks sequentially: asking health questions, measuring vitals, and performing a glucose test—a process modeled with lognormal distributions with specified means and standard deviations, capturing variability in task durations.

A critical component of the simulation considers patient reluctance to wait beyond four waiting patients. When the queue length exceeds this threshold, patients divert to a nearby park, spending approximately 15 minutes there before rechecking queue conditions. This behavior introduces a stochastic delay modeled by a uniform distribution for travel time and affects overall throughput and wait times. Patients may revisit the park multiple times until the queue size drops below the threshold, eventually completing screening.

The glucose test results determine subsequent actions: a positive result (5% probability) leads patients to a scheduling clerk via a separate process governed by a Weibull distribution, capturing variability in scheduling time. Conversely, negative tests result in patients departing after completing the screening process. These dynamics influence the final counts of positive and negative diagnoses.

Additional realism is incorporated through staffing schedules with staggered nurse breaks, to mirror operational constraints. Breaks are scheduled starting from 10:00, with all nurses sequentially taking their respective breaks to maintain continuous service. The entire process spans from 8:00 to 17:00, with specific intervals allocated for lunch and short breaks, affecting staffing levels and resource availability.

The simulation collects detailed data over 30 replications, including queue length, queue waiting times, nurse utilization, and count of positive versus negative screenings. This data provides insights into operational bottlenecks and efficiency. Outputs include average cycle times for positive and negative patients, elucidating the overall patient experience and throughput efficiency.

Analyzing this data enables stakeholders to optimize scheduling, improve patient flow, and allocate resources efficiently to enhance disease screening capacity without compromising quality or patient satisfaction. The results also inform policy decisions on staffing levels, break times, and patient management protocols for similar community health initiatives.

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