A scoping review of de-implementation frameworks and models

Reduction or elimination of inappropriate, ineffective, or potentially harmful healthcare services and public health programs can help to ensure limited resources are used effectively. Frameworks and models (FM) are valuable tools in conceptualizing and guiding the study of de-implementation. This scoping review sought to identify and characterize FM that can be used to study de-implementation as a phenomenon and identify gaps in the literature to inform future model development and application for research.

We searched nine databases and eleven journals from a broad array of disciplines (e.g., healthcare, public health, public policy) for de-implementation studies published between 1990 and June 2020. Two raters independently screened titles and abstracts, and then a pair of raters screened all full text records. We extracted information related to setting, discipline, study design, methodology, and FM characteristics from included studies.

The final search yielded 1860 records, from which we screened 126 full text records. We extracted data from 27 articles containing 27 unique FM. Most FM (n = 21) were applicable to two or more levels of the Socio-Ecological Framework, and most commonly assessed constructs were at the organization level (n = 18). Most FM (n = 18) depicted a linear relationship between constructs, few depicted a more complex structure, such as a nested or cyclical relationship. Thirteen studies applied FM in empirical investigations of de-implementation, while 14 articles were commentary or review papers that included FM.

De-implementation is a process studied in a broad array of disciplines, yet implementation science has thus far been limited in the integration of learnings from other fields. This review offers an overview of visual representations of FM that implementation researchers and practitioners can use to inform their work. Additional work is needed to test and refine existing FM and to determine the extent to which FM developed in one setting or for a particular topic can be applied to other contexts. Given the extensive availability of FM in implementation science, we suggest researchers build from existing FM rather than recreating novel FM.

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