Reducing appointment lead-time in an outpatient department of gynecology and obstetrics through discrete-event simulation: A case study

Appointment lead-time is a critical variable in outpatient clinic services. In Gynecology and Obstetrics departments, longer appointment lead times are associated with lower patient satisfaction, the use of more complex healthcare services, development of long-term and severe complications and the increase of fetal, infant and maternal mortality rates. This paper aims to define and evaluate improvement alternatives through the use of Discrete-event simulation (DES). First, input data analysis is performed. Second, the simulation model is created; then, performance metrics are calculated and analyzed. Finally, improvement scenarios are designed and assessed. A case study of a mixed-patient type environment (Perinatology and Gynecobstetrics) in an outpatient department has been explored to verify the effectiveness of the proposed approach. Statistical analysis evidence that appointment lead times could be significantly reduced in both Perinatology and Gynecobstetrics appointments based on the proposed approaches in this paper

Implementation by simulation; strategies for ultrasound screening for hip dysplasia in the Netherlands

Background: Implementation of medical interventions may vary with organization and available capacity. The influence of this source of variability on the cost-effectiveness can be evaluated by computer simulation following a carefully designed experimental design. We used this approach as part of a national implementation study of ultrasonographic infant screening for developmental dysplasia of the hip (DDH). Methods: First, workflow and performance of the current screening program (physical examination) was analyzed. Then, experimental variables, i.e., relevant entities in the workflow of screening, were defined with varying levels to describe alternative implementation models. To determine the relevant levels literature and interviews among professional stakeholders are used. Finally, cost-effectiveness ratios (inclusive of sensitivity analyses) for the range of implementation scenarios were calculated. Results: The four experimental variables for implementation were: 1) location of the consultation, 2) integrated with regular consultation or not, 3) number of ultrasound machines and 4) discipline of the screener. With respective numbers of levels of 3,2,3,4 in total 72 possible scenarios were identified. In our model experimental variables related to the number of available ultrasound machines and the necessity of an extra consultation influenced the cost-effectiveness most. Conclusions: Better information comes available for choosing optimised implementation strategies where organizational and capacity variables are important using the combination of simulation models and an experimental design. Information to determine the levels of experimental variables can be extracted from the literature or directly from experts

Analysing management policies for operating room planning using simulation

Simulation, Optimisation, Healthcare, Management, Operating room planning,