A Library of Logic Models to Explain How Interventions to Reduce Diagnostic Errors Work.

Abstract

We aimed to create a library of logic models for interventions to reduce diagnostic error. This library can be used by those developing, implementing, or evaluating an intervention to improve patient care, to understand what needs to happen, and in what order, if the intervention is to be effective. To create the library, we modified an existing method for generating logic models. The following five ordered activities to include in each model were defined: preintervention; implementation of the intervention; postimplementation, but before the immediate outcome can occur; the immediate outcome (usually behavior change); and postimmediate outcome, but before a reduction in diagnostic errors can occur. We also included reasons for lack of progress through the model. Relevant information was extracted about existing evaluations of interventions to reduce diagnostic error, identified by updating a previous systematic review. Data were synthesized to create logic models for four types of intervention, addressing five causes of diagnostic error in seven stages in the diagnostic pathway. In total, 46 interventions from 43 studies were included and 24 different logic models were generated. We used a novel approach to create a freely available library of logic models. The models highlight the importance of attending to what needs to occur before and after intervention delivery if the intervention is to be effective. Our work provides a useful starting point for intervention developers, helps evaluators identify intermediate outcomes, and provides a method to enable others to generate libraries for interventions targeting other errors.