Research article
Development of a minimization instrument for allocation of a hospital-level performance improvement intervention to reduce waiting times in Ontario emergency departments
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* Corresponding author: Michael J Schull mjs@ices.on.ca
1 Institute for Clinical Evaluative Sciences, 2075 Bayview Ave, Toronto, Canada
2 Department of Paediatrics, University of Toronto, Toronto, Canada
3 Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
4 Centre for Health Services Sciences, Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, Canada
5 Department of Emergency Medicine and School of Public Health, University of Alberta, Edmonton, Canada
6 Rotman School of Management, University of Toronto, Toronto, Canada
7 University Health Network, 90 Elizabeth St, Toronto, Canada
8 Department of Medicine, University of Toronto, Toronto, Canada
9 Mount Sinai Hospital, 600 University Ave, Toronto, Canada
10 Clinical Epidemiology Unit, Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, Canada
Implementation Science 2009, 4:32 doi:10.1186/1748-5908-4-32
Published: 8 June 2009Abstract
Background
Rigorous evaluation of an intervention requires that its allocation be unbiased with respect to confounders; this is especially difficult in complex, system-wide healthcare interventions. We developed a short survey instrument to identify factors for a minimization algorithm for the allocation of a hospital-level intervention to reduce emergency department (ED) waiting times in Ontario, Canada.
Methods
Potential confounders influencing the intervention's success were identified by literature review, and grouped by healthcare setting specific change stages. An international multi-disciplinary (clinical, administrative, decision maker, management) panel evaluated these factors in a two-stage modified-delphi and nominal group process based on four domains: change readiness, evidence base, face validity, and clarity of definition.
Results
An original set of 33 factors were identified from the literature. The panel reduced the list to 12 in the first round survey. In the second survey, experts scored each factor according to the four domains; summary scores and consensus discussion resulted in the final selection and measurement of four hospital-level factors to be used in the minimization algorithm: improved patient flow as a hospital's leadership priority; physicians' receptiveness to organizational change; efficiency of bed management; and physician incentives supporting the change goal.
Conclusion
We developed a simple tool designed to gather data from senior hospital administrators on factors likely to affect the success of a hospital patient flow improvement intervention. A minimization algorithm will ensure balanced allocation of the intervention with respect to these factors in study hospitals.