How to study improvement interventions: a brief overview of possible study types

By M. Portela, Peter J. Pronovost, T. Woodcock, P. Carter, M. Dixon-Woods. BMJ Quality & Safety, 2015

313 citations recorded by the source corpus.

Improvement (defined broadly as purposive efforts to secure positive change) has become an increasingly important activity and field of inquiry within healthcare. This article offers an overview of possible methods for the study of improvement interventions. The choice of available designs is wide, but debates continue about how far improvement efforts can be simultaneously practical (aimed at producing change) and scientific (aimed at producing new knowledge), and whether the distinction between the practical and the scientific is a real and useful one. Quality improvement projects tend to be applied and, in some senses, self-evaluating. They are not necessarily directed at generating new knowledge, but reports of such projects if well conducted and cautious in their inferences may be of considerable value. They can be distinguished heuristically from research studies, which are motivated by and set out explicitly to test a hypothesis, or otherwise generate new knowledge, and from formal evaluations of improvement projects. We discuss variants of trial designs, quasi-experimental designs, systematic reviews, programme evaluations, process evaluations, qualitative studies, and economic evaluations. We note that designs that are better suited to the evaluation of clearly defined and static interventions may be adopted without giving sufficient attention to the challenges associated with the dynamic nature of improvement interventions and their interactions with contextual factors. Reconciling pragmatism and research rigour is highly desirable in the study of improvement. Trade-offs need to be made wisely, taking into account the objectives involved and inferences to be made.

Subfields: Economics and Econometrics, General Health Professions

View Paper: https://www.semanticscholar.org/paper/f06dd289c8d8c93c5b694495274e3a114a16075c

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Citations in the corpus

Ensuring Fairness in Machine Learning to Advance Health Equity (2018, methodology): “If a model is deployed, the performance of the model and outcome measurements should be monitored, possibly through formal trial design (such as stepped-wedge trials [50]).”