TY - JOUR
T1 - A Library of Logic Models to Explain How Interventions to Reduce Diagnostic Errors Work
AU - Kletter, Maartje
AU - Melendez-Torres , G. J.
AU - Lilford, Richard
AU - Taylor , Celia
PY - 2018/1/24
Y1 - 2018/1/24
N2 - FavoritesPermissionsORIGINAL STUDIESA Library of Logic Models to Explain How Interventions to Reduce Diagnostic Errors WorkKletter, Maartje MSc∗; Melendez-Torres, G.J. PhD†; Lilford, Richard PhD∗; Taylor, Celia PhD∗Author InformationJournal of Patient Safety 17(8):p e1223-e1233, December 2021. | DOI: 10.1097/PTS.0000000000000459SDCMetricsAbstractObjectives 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.Methods 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.Results 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.Conclusions 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.
AB - FavoritesPermissionsORIGINAL STUDIESA Library of Logic Models to Explain How Interventions to Reduce Diagnostic Errors WorkKletter, Maartje MSc∗; Melendez-Torres, G.J. PhD†; Lilford, Richard PhD∗; Taylor, Celia PhD∗Author InformationJournal of Patient Safety 17(8):p e1223-e1233, December 2021. | DOI: 10.1097/PTS.0000000000000459SDCMetricsAbstractObjectives 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.Methods 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.Results 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.Conclusions 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.
KW - Diagnostic error
KW - Logic model
KW - mechanistic theory
KW - Effectiveness
UR - http://dx.doi.org/10.1097/pts.0000000000000459
U2 - 10.1097/pts.0000000000000459
DO - 10.1097/pts.0000000000000459
M3 - Article
SN - 1549-8417
VL - 17
SP - 1223
EP - 1233
JO - Journal of Patient Safety
JF - Journal of Patient Safety
IS - 8
ER -