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Open Access Research article

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

Sabrina Ramwadhdoebe1*, Godefridus G Van Merode2, Magda M Boere-Boonekamp3, Ralph JB Sakkers1 and Erik Buskens45

Author Affiliations

1 Department Orthopaedics, University Medical Center Utrecht, Utrecht, Netherlands

2 Faculty of health, medicine and life sciences, Maastricht University Medical Center, Maastricht, Netherlands

3 Department of Science, Technology, Health, and Policy Studies (SteHPS), School of Management and Governance, University of Twente, Enschede, Netherlands

4 Julius Center for health sciences and primary care, University Medical Center Utrecht, Utrecht, Netherlands

5 Medical Technology Assessment, Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands

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BMC Health Services Research 2010, 10:75  doi:10.1186/1472-6963-10-75

Published: 23 March 2010

Abstract

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.