MODELING IMPLEMENTATION STRATEGIES BY SIMULATION
Sabrina Ramwadhdoebe, Godefridus Van Merode, Ralph Sakkers, Magda Boere-Boonekamp and Erik Buskens
International Conference on Health Sciences Simulation (ICHSS 2008)
Crowne Plaza Ottawa Hotel, Ottawa, Canada, April 14-17, 2008
Summary
Purpose In cost effectiveness studies variables associated with logistics such as, organization and available capacity, may affect the overall results and thus the conclusions of the study. The influence of the variation of these variables on the cost effectiveness can be evaluated by means of computer simulation following a carefully designed experimental design. The combination of a simulation model and an experimental design provides a means to examine a full range of possible scenarios. A simulation model will be used to calculate the cost effectiveness, and assess the required capacity in organizations for the implementation of a new screening strategy for detecting developmental dysplasia of the hip (DDH). In the new strategy current physical examination will be replaced by ultrasound (US) examination at child health care centers (chc). We will describe the steps for the development of an experimental design and the simulation model. Methods First the current workflow and performance is analyzed. Thereafter important output factors are identified. Then improvements and alternative scenarios are determined with the use of experimental variables (input factors). Each experimental variable is given levels with different values. For the determination of the levels literature and interviews among child health care professionals are used. With a simulation model we experiment with the scenarios to evaluate the variation on the cost effectiveness. Results Important output factors are the attendance, missed cases, false positives and the costs the youth health care organization incurs. The experimental variables are the location of the consult (3 levels), integrated with regular consult or not (2 levels), the number of US machines (3 levels) and the discipline of the screener (4 levels). With these 4 experimental variables 72 possible scenarios are identified. ‘Possible’ means that they can be evaluated with the simulation model. The 5 most cost effective scenarios had in common that all did not include the level many US machines, specialist as a discipline and external organised in building that have to be rented. The 5 least CE-scenarios had in common they all included the level many US machines, an extra consult and a child health care doctor or radiologist as a discipline. Conclusion The combination of a simulation model and an experimental design greatly enhance cost effectiveness studies where organizational and capacity variables are important. Relevant information to determine the levels of experimental variables can be revealed with literature and directly from experts. Using an experimental design one can a priori explore how input variables affect the outcome and explore different strategies for implementation. In our model the experimental variables related to the number of US machines in combination with an extra consult is influencing the cost effectiveness the most.
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