The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
How to study improvement interventions: a brief overview of possible study types
Open accessAbstract
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.
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Cited by
Ogrinc, G., Davies, L., Goodman, D., Batalden, P., Davidoff, F., & Stevens, D. (2016) · BMJ Quality & Safety · doi:10.1136/bmjqs-2015-004411 · Cited by 2,996
Ensuring fairness in machine learning to advance health equity.
Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018) · Annals of Internal Medicine · doi:10.7326/m18-1990 · Cited by 1,341
Interventions to reduce unnecessary caesarean sections in healthy women and babies
Betrán, A. P., Temmerman, M., Kingdon, C., Mohiddin, A., Opiyo, N., Torloni, M. R., Zhang, J., Musana, O., Wanyonyi, S. Z., Gülmezoglu, A. M., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31927-5 · Cited by 600
Schwendimann, R., Blatter, C., Dhaini, S., Simon, M., & Ausserhofer, D. (2018) · BMC Health Services Research · doi:10.1186/s12913-018-3335-z · Cited by 423
Ogrinc, G., Davies, L., Goodman, D., Batalden, P., Davidoff, F., & Stevens, D. (2016) · Journal of Surgical Research · doi:10.1016/j.jss.2015.09.015 · Cited by 366
Metadata
- Authors
- Portela, M. C., Pronovost, P. J., Woodcock, T., Carter, P., & Dixon-Woods, M.
- Venue
- BMJ Quality & Safety
- Publisher
- BMJ
- Published
- 2015-03-25
- Volume
- 24
- Issue
- 5
- Pages
- 325-336
- ISSN
- 1475-3898 (ISSN-L)
- 2044-5415
- 2044-5423
- OpenAlex
- W2161116273
- Cited by
- 315
- References
- 29
- FWCI
- 66.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
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