ArticleBMC health services research2015
Optimizing appointment template and number of staff of an OB/GYN clinic--micro and macro simulation analyses.
Article in BMC health services research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 21 citations in OpenAlex.
- Research on outpatient capacity planning combining lean thinking and integer linear programming.BMC medical informatics and decision making · 2023Article
- Study to assess the utility of discrete event simulation software in projection & optimization of resources in the out-patient department at an apex cancer institute in India.Health science reports · 2022Article
- Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System.Healthcare (Basel, Switzerland) · 2022Article
- Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review.International journal of environmental research and public health · 2021Review
- FASStR: a framework for ensuring high-quality operational metrics in health care.The American journal of managed care · 2020Review
- Capacity and patient flow planning in post-term pregnancy outpatient clinics: a computer simulation modelling study.BMC health services research · 2020Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Department of Obstetrics and Gynecology (OB/GYN) at the University of Arkansas for Medical Sciences (UAMS) tested various, new system-restructuring ideas such as varying number of different types of nurses to reduce patient wait times for its outpatient clinic, often with little or no effect on waiting time. Witnessing little progress despite these time-intensive interventions, we sought an alternative way to intervene the clinic without affecting the normal clinic operations.
aimThe aim is to identify the optimal (1) time duration between appointments and (2) number of nurses to reduce wait time of patients in the clinic.
methodsWe developed a discrete-event computer simulation model for the OB/GYN clinic. By using the patient tracker (PT) data, appropriate probability distributions of service times of staff were fitted to model different variability in staff service times. These distributions were used to fine-tune the simulation model. We then validated the model by comparing the simulated wait times with the actual wait times calculated from the PT data. The validated model was then used to carry out "what-if" analyses.
resultsThe best scenario yielded 16 min between morning appointments, 19 min between afternoon appointments, and addition of one medical assistant. Besides removing all peak wait times and bottlenecks around noon and late in the afternoon, the best scenario yielded 39.84 % (p<.001), 30.31 % (p<.001), and 15.12 % (p<.001) improvement in patients' average wait times for providers in the exam rooms, average total wait time at various locations and average total spent time in the clinic, respectively. This is achieved without any compromise in the utilization of the staff and in serving all patients by 5 pm.
conclusionsA discrete-event simulation model is developed, validated, and used to carry out "what-if" scenarios to identify the optimal time between appointments and number of nurses. Using the model, we achieved a significant improvement in wait time of patients in the clinic, which the clinic management initially had difficulty achieving through manual interventions. The model provides a tool for the clinic management to test new ideas to improve the performance of other UAMS OB/GYN clinics.
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