2025/03/28 by Suzanne E. Morrissey, Arwen Bunce, Jenna Donovan +5 · 1 voice
Health Professions · Computer Science · Decision Sciences · #Health Sciences Research and Education #Machine Learning in Healthcare #Meta-analysis and systematic reviews
paper · doi:10.1177/16094069251326415
openalex publication_date 2025/03/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
The application of realist-informed approaches to implementation research can produce answers to why, for whom and under what circumstances social determinants of health interventions work. In the context of a study to develop and test EHR-based clinical decision support tools that suggest adjusting care plans in response to patient-reported financial, housing, food, transportation, and utilities insecurity, the authors applied an innovative use of realist principles in a bounded, mid-study task. This paper demonstrates how realist retroduction can be applied in intervention development processes. Retroduction proved useful in identifying the often intangible clinical needs and preferences that affected decision support tool desirability and use, which then guided the revision of five tools prior to a formal trial. This paper illustrates how data from the study development phases were put in service of retroductive steps that, through the identification of tentative program theories, guided revision of the pilot electronic tools to better meet clinic needs in the study trial phase. Applying retroductive thinking to establish what may be more or less effective under real-world conditions before participants are recruited is a productive, pragmatic form of researcher/stakeholder co-design that seeks to achieve results without wasting clinical teams' time.