Applying Machine Learning Techniques to Implementation Science*
Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.
Citation
Huguet N, Chen J, Parikh RB, et al. Applying Machine Learning Techniques to Implementation Science. Online J Public Health Inform. 2024;16:e50201.
Contributors
Nathalie Huguet, PhD
Jinying Chen, PhD; Ravi B. Parikh, MD, MPP; Miguel Marino, PhD; Susan A. Flocke, PhD; Sonja Likumahuwa-Ackman, MID, MPH; Justin Bekelman, MD; Jennifer E. DeVoe, MD, DPhil
Note
* This public good was developed with funding from a CCIS public goods cycle.