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Front Public Health ; 10: 959567, 2022.
Article in English | MEDLINE | ID: covidwho-2022984


Responding rapidly to emerging public health crises is vital to reducing their escalation, spread, and impact on population health. These responses, however, are challenging and disparate processes for researchers and practitioners. Researchers often develop new interventions that take significant time and resources, with little exportability. In contrast, community-serving systems are often poorly equipped to properly adopt new interventions or adapt existing ones in a data-driven way during crises' onset and escalation. This results in significant delays in deploying evidence-based interventions (EBIs) with notable public health consequences. This prolonged timeline for EBI development and implementation results in significant morbidity and mortality that is costly and preventable. As public health emergencies have demonstrated (e.g., COVID-19 pandemic), the negative consequences often exacerbate existing health disparities. Implementation science has the potential to bridge the extant gap between research and practice, and enhance equity in rapid public health responses, but is underutilized. For the field to have a greater "real-world" impact, it needs to be more rapid, iterative, participatory, and work within the timeframes of community-serving systems. This paper focuses on rapid adaptation as a developing implementation science area to facilitate system responses during public health crises. We highlight frameworks to guide rapid adaptation for optimizing existing EBIs when responding to urgent public health issues. We also explore the economic implications of rapid adaptation. Resource limitations are frequently a central reason for implementation failure; thus, we consider the economic impacts of rapid adaptation. Finally, we provide examples and propose directions for future research and application.

COVID-19 , Implementation Science , COVID-19/prevention & control , Humans , Pandemics , Public Health
Implement Sci Commun ; 3(1): 89, 2022 Aug 12.
Article in English | MEDLINE | ID: covidwho-1993406


BACKGROUND: Lung ultrasound (LUS) is a clinician-performed evidence-based imaging modality that has multiple advantages in the evaluation of dyspnea caused by multiple disease processes, including COVID-19. Despite these advantages, few hospitalists have been trained to perform LUS. The aim of this study was to increase adoption and implementation of LUS during the 2020 COVID-19 pandemic by using recurrent assessments of RE-AIM outcomes to iteratively revise our implementation strategies. METHODS: In an academic hospital, we implemented guidelines for the use of LUS in patients with COVID-19 in July 2020. Using a novel "RE-AIM dashboard," we used an iterative process of evaluating the high-priority outcomes of Reach, Adoption, and Implementation at twice monthly intervals to inform revisions of our implementation strategies for LUS delivery (i.e., Iterative RE-AIM process). Using a convergent mixed methods design, we integrated quantitative RE-AIM outcomes with qualitative hospitalist interview data to understand the dynamic determinants of LUS Reach, Adoption, and Implementation. RESULTS: Over the 1-year study period, 453 LUSs were performed in 298 of 12,567 eligible inpatients with COVID-19 (Reach = 2%). These 453 LUS were ordered by 43 out of 86 eligible hospitalists (LUS order adoption = 50%). However, the LUSs were performed/supervised by only 8 of these 86 hospitalists, 4 of whom were required to complete LUS credentialing as members of the hospitalist procedure service (proceduralist adoption 75% vs 1.2% non-procedural hospitalists adoption). Qualitative and quantitative data obtained to evaluate this Iterative RE-AIM process led to the deployment of six sequential implementation strategies and 3 key findings including (1) there were COVID-19-specific barriers to LUS adoption, (2) hospitalists were more willing to learn to make clinical decisions using LUS images than obtain the images themselves, and (3) mandating the credentialing of a strategically selected sub-group may be a successful strategy for improving Reach. CONCLUSIONS: Mandating use of a strategically selected subset of clinicians may be an effective strategy for improving Reach of LUS. Additionally, use of Iterative RE-AIM allowed for timely adjustments to implementation strategies, facilitating higher levels of LUS Adoption and Reach. Future studies should explore the replicability of these preliminary findings.