Characterizing Behaviors That Influence the Implementation of Digital-Based Interventions in Health Care: Systematic Review.

BACKGROUND

Successful implementation of any digital intervention in a health care setting requires adoption by all stakeholders. Appropriate consideration of behavioral change is a key driver that is often overlooked during implementation. The nonadoption, abandonment, scale-up, spread, and systems (NASSS) behavioral framework offers a broad evaluation of success for digital health solutions, and the theoretical domains framework (TDF) focuses particularly on adopters, identifying determinants of behavior and potential reasons for implementation issues.

OBJECTIVE

The aim of this study was to describe and characterize barriers and facilitators to the adoption of digital solutions within health care using behavioral frameworks: the NASSS and TDF.

METHODS

A systematic search was performed in 4 databases (ie, Ovid in MEDLINE, Embase, Health Management Information Consortium, and PsycINFO). Included studies reported a behavioral change by health care professionals following digital interventions or the practicality of delivering such interventions. Barriers and facilitators were identified, extracted, and classified using the NASSS framework and TDF. Risk of bias was assessed using the Mixed Methods Appraisal Tool.

RESULTS

The initial search result included 2704 unique studies, 12 of which met the inclusion criteria and from which data were extracted. All 12 scored ≥3 out of 5 stars on the Mixed Methods Appraisal Tool risk of bias assessment. Out of the 12 studies, 67% (n=8) were conducted in the United States, and 8% (n=1) each in India, Australia, the Netherlands, and Tanzania. The NASSS framework identified facilitators and barriers in 4 domains: the condition or illness, technology, value proposition, and adopter system. The TDF framework identified 8 relevant domains, including knowledge, skills, and beliefs about capabilities. Key facilitators included intuitive technology design aligned with existing workflows, clear communication of value propositions to adopters, adequate provision of training resources tailored to adopters' knowledge levels, and ensuring organizational readiness for technological change. Conversely, significant barriers involved disruptions to clinical workflow, inadequate adopter training or confidence levels, unclear value propositions leading to disengagement, insufficient consideration of cognitive load impacts, such as alert fatigue, and limited organizational preparedness. Notably, psychological factors such as optimism, intentions, and social influences were underreported.

CONCLUSIONS

This study delineated and analyzed various critical behavioral factors impacting the adoption and implementation of digital interventions in health care. Based on these findings, future research must consider the key factors reported and alternative approaches to assess behaviors influencing adoption that are not presented in the current scientific literature.

TRIAL REGISTRATION: PROSPERO CRD42022264937; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022264937.