ArticleBMC health services research2020
Capacity and patient flow planning in post-term pregnancy outpatient clinics: a computer simulation modelling study.
Article in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Leveraging quality improvement initiatives to support development of decision support tools in healthcare.Health systems (Basingstoke, England) · 2025Article
- 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
- 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.
Funding
Abstract
backgroundThe demand for a large Norwegian hospital's post-term pregnancy outpatient clinic has increased substantially over the last 10 years due to changes in the hospital's catchment area and to clinical guidelines. Planning the clinic is further complicated due to the high did not attend rates as a result of women giving birth. The aim of this study is to determine the maximum number of women specified clinic configurations, combination of specified clinic resources, can feasibly serve within clinic opening times.
methodsA hybrid agent based discrete event simulation model of the clinic was used to evaluate alternative configurations to gain insight into clinic planning and to support decision making. Clinic configurations consisted of six factors: X0: Arrivals. X1: Arrival pattern. X2: Order of midwife and doctor consultations. X3: Number of midwives. X4: Number of doctors. X5: Number of cardiotocography (CTGs) machines. A full factorial experimental design of the six factors generated 608 configurations.
resultsEach configuration was evaluated using the following measures: Y1: Arrivals. Y2: Time last woman checks out. Y3: Women's length of stay (LoS). Y4: Clinic overrun time. Y5: Midwife waiting time (WT). Y6: Doctor WT. Y7: CTG connection WT. Optimisation was used to maximise X0 with respect to the 32 combinations of X1-X5. Configuration 0a, the base case Y1 = 7 women and Y3 = 102.97 [0.21] mins. Changing the arrival pattern (X1) and the order of the midwife and doctor consultations (X2) configuration 0d, where X3, X4, X5 = 0a, Y1 = 8 woman and Y3 86.06 [0.10] mins.
conclusionsThe simulation model identified the availability of CTG machines as a bottleneck in the clinic, indicated by the WT for CTG connection effect on LoS. One additional CTG machine improved clinic performance to the same degree as an extra midwife and an extra doctor. The simulation model demonstrated significant reductions to LoS can be achieved without additional resources, by changing the clinic pathway and scheduling of appointments. A more general finding is that a simulation model can be used to identify bottlenecks, and efficient ways of restructuring an outpatient clinic.
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