Diabetes Prevention and Care Capacity at Urban Indian Health Organizations

Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions

BACKGROUND

The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods.

MAIN TEXT

This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly.

CONCLUSIONS

Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.

Applying an equity lens to assess context and implementation in public health and health services research and practice using the PRISM framework

Dissemination and implementation science seeks to enhance the uptake, successful implementation, and sustainment of evidence-based programs and policies. While a focus on health equity is implicit in many efforts to increase access to and coverage of evidence-based programs and policies, most implementation frameworks and models do not explicitly address it. Disparities may in fact be increased by emphasizing high intensity interventions or ease of delivery over meeting need within the population, addressing deep-rooted structural inequities, and adapting to local context and priorities. PRISM (Practical, Robust Implementation and Sustainability Model), the contextual expansion of the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework has several elements that address health equity, but these have not been explicated, integrated, or illustrated in one place. We present guidance for applying PRISM with an equity lens across its four context domains (external environment; multi-level perspectives on the intervention; characteristics of implementers and intended audience; and the implementation and sustainability infrastructure-as well as the five RE-AIM outcome dimensions. We then present an example with health equity considerations and discuss issues of representation and participation, representativeness and the importance of ongoing, iterative assessment of dynamic context and structural drivers of inequity. We also elaborate on the importance of a continuous process that requires addressing community priorities and responding to capacity and infrastructure needs and changes. We conclude with research and practice recommendations for applying PRISM with an increased emphasis on equity.