This article describes a field study undertaken at a hospital clinic. The study used computer simulation to attempt to assess the efficiency of the outpatient consultation process in the rheumatology, allergy, and immunology (RAI) clinic in terms of patient wait times and utilization of nurses involved in the process. Based on the simulation results, we recommend the optimal numbers of nurses at which patient wait times could be reduced and nurses could be most efficiently utilized.
Background
Computer simulation uses modeling processes to study how a system reacts to conditions that are not easily or conveniently applied in real-world situations and to examine how the working of a system can be altered by changing individual parts of the system. The power of simulation is realized when it is used to study scenarios such as healthcare systems that have complex interactions among various components and processes. The applications of computer simulation used in healthcare can be classified into two groups: 1) applications to healthcare systems at various levels of communities, regions, or the nation; and 2) applications to specific operations, processes, or services in healthcare. The first group includes applications intended to study the provisions of mental health, public health, health reform or healthcare workforce, often with policy implications.(1) The second group, which applies to the focus of this article, includes applications intended to improve facility design, staffing and scheduling, as well as the reduction of patient wait times and operating costs.(2) The case study described later in this article attempts to extend this line of study by considering patient wait times and utilization of nurses in the outpatient consultation process at a hospital clinic.
Methods
The RAI clinic treats outpatients in the areas of arthropathies; connective tissue diseases; soft tissue rheumatism; rheumatic diseases; drug, food and insect venom allergies; anaphylaxis; urticaria and angioedema; allergic rhinitis and asthma; atopic eczema; and investigations.
The simulation model for the RAI clinic involves the following entities, resources, and locations. An entity refers to an object or person that a simulation model processes. There is one type of entity—patients—in the simulation model for the RAI clinic. A location refers to a fixed place where entities are routed for processing or some other activity or decision. The simulation model for the RAI clinic has four types of locations: registration counters; consultation rooms; diagnostic laboratories; and payment counters. A resource is a person, piece of equipment or some other device used for one or more of the following functions: treating and moving entities; assisting in performing tasks for entities at locations; and performing maintenance on locations or other resources. There is one type of resource—nurse—in this simulation model for the RAI clinic.
The outpatient consultation process in the RAI clinic consists of four stages in a sequence:
Registration;
Consultation by doctors;
Diagnostic (blood and urine) testing; and
Payment.
For the simulation in this study, we used historical data collected from the hospital for one month. Interarrival times of patients in the RAI clinic were found to be exponentially distributed, with a mean value of 2.31 minutes. On average, the RAI clinic treated 123 patients daily. More patients were treated on Monday, Wednesday, and Friday than on the other days, and more patients were treated in the morning than in the afternoon, except on Thursday. Each day had three off-peak periods—8 AM to 08:59 AM, 11 AM to 1:59 PM, and 3:59 PM to 5:30 PM—and two distinct peak periods—9 AM to 10:59 AM and 2 PM to 3:59 PM.
During off-peak hours, nurses were distributed among the four stages as follows:
Registration stage: one nurse;
Consultation stage: four nurses;
Diagnostic test stage: two nurses; and
Payment stage: one nurse
During the peak hours, the distribution was different:
Registration stage: two nurses;
Consultation stage: four nurses;
Diagnostic test stage: two nurses; and
Payment stage: two nurses.
We constructed the simulation model of this study using Arena simulation software. The simulation model was run for five independent replications. The simulation results are based on the average results of the five independent replications. In validating the simulation model, we calculated the confidence intervals of the simulation outputs at 95% (α = .05) confidence level and compared them with the actual values obtained from the RAI clinic. In addition, we verified the architecture of the simulation model with the clinic staff before the simulation runs and showed the simulation results to the clinic staff after the simulation runs to ensure that the simulation results are reliable.
Findings
We ran the simulation model first with the current nurse staffing and then with a varying number of nurses at each stage in the process to find the optimal number of nurses at which patient wait times could be reduced and nurses could be more efficiently utilized. The simulation results with the current nurse staffing show that the patient wait time is 132.67 minutes during off-peak hours and 118.97 minutes during peak hours. The simulation results using a varying number of nurses at each stage show that the shortest possible patient wait time is 100.37 minutes, which happens when two nurses are at the registration stage, five nurses are at the consultation stage, three nurses are at the diagnostic test stage, and one nurse is at the payment stage. The second shortest patient wait time is 122.13 minutes, when one nurse is at the registration stage, five nurses are at the consultation stage, three nurses are at the procedure stage, and one nurse is at the payment stage.
The simulation results show that the RAI clinic can reduce the patient’s wait time significantly by adding one nurse to the registration stage, adding one nurse to the diagnostic test stage, and reallocating one nurse at the consultation stage. The simulation results in this scenario show that the utilization of nurses can reach 80% percent, which is considered acceptable in the field. In the context of the process in the RAI clinic, nurses are considered as being utilized while they are present at any location in the process; otherwise, they are considered as being idle.
Conclusion
The simulation results show that there are opportunities for improvement in the patient wait times in the RAI clinic. Based on these results, we recommended the optimal numbers of nurses needed at the RAI clinic at which the patient wait times and the utilization of nurses would be balanced. The results of this study demonstrate that computer simulation can be an effective tool supporting decisions on staffing for various processes in hospital clinics. The results would be helpful to those who are considering using computer simulation to improve healthcare and similar processes.
Acknowledgment: Support for travel expenses pertaining to this study were provided by the Institute for International Business of the Stillman School of Business at Seton Hall University for Sung J. Shim.
References
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