Abstract
BackgroundRigorous 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.MethodsPotential 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.ResultsAn 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.ConclusionWe 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.
Highlights
Balancing potential confounders in evaluation of hospitallevel interventions Rigorous evaluation of an intervention requires that its allocation be unbiased with respect to confounders
Candidate factors related to the implementation of the emergency department (ED)-PIP and covered a broad spectrum of issues
The top ranking factors across the domains were discussed; the factors with the highest average score in each domain were confirmed in the discussion as the consensus choice to include in the minimization algorithm
Summary
Balancing potential confounders in evaluation of hospitallevel interventions Rigorous evaluation of an intervention requires that its allocation be unbiased with respect to confounders. One way to help circumvent this problem is to stratify or match on key characteristics before randomization In order for this to work, a small but inclusive set of key potential confounders must be identified. The method assigns subjects to a balanced allocation sequence or to treatment groups with respect to marginal frequencies between these selected covariates. This is achieved by an algorithm that allocates the intervention to each subject, in our case, a hospital, that volunteers and is eligible to receive the intervention [6,7,8]. We developed a short survey instrument to identify factors for a minimization algorithm for the allocation of a hospitallevel intervention to reduce emergency department (ED) waiting times in Ontario, Canada
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