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Purpose Facilitation activities support implementation of evidence-based interventions within healthcare organizations. Few studies have attempted to understand how facilitation activities are performed to promote the uptake of evidence-based interventions in hospitals from resource-poor countries during crises such as pandemics. This paper aims to explore facilitation activities by infection prevention and control (IPC) professionals in 16 hospitals from 9 states in Brazil during the COVID-19 pandemic. Design/methodology/approach Primary and secondary data were collected between March and December 2020. Semi-structured interviews were conducted with 21 IPC professionals in Brazilian hospitals during the COVID-19 pandemic. Public and internal documents were used for data triangulation. The data were analyzed through thematic analysis technique. Findings Building on the change response theory, this study explores the facilitation activities from the cognitive, behavioral and affective aspects. The facilitation activities are grouped in three overarching dimensions: (1) creating and sustaining legitimacy to continuous and rapid changes, (2) fostering capabilities for continuous changes and (3) accelerating individual commitment. Practical implications During crises such as pandemics, facilitation activities by IPC professionals need to embrace all the cognitive, behavioral and affective aspects to stimulate positive attitudes of frontline workers toward continuous and urgent changes. Originality/value This study provides unique and timely empirical evidence on the facilitation activities that support the implementation of evidence-based interventions by IPC professionals during crises in hospitals in a resource-poor country. © Luís Irgang, Magnus Holmén, Fábio Gama and Petra Svedberg. ; BINECO – Business Models for Information-Driven Ecosystems in Healthcare

Background: Significant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, health care professionals still struggle to implement AI in their daily practice. Objective: This paper aims to identify the implementation frameworks used to understand the application of AI in health care practice. Methods: A scoping review was conducted using the Cochrane, Evidence Based Medicine Reviews, Embase, MEDLINE, and PsycINFO databases to identify publications that reported frameworks, models, and theories concerning AI implementation in health care. This review focused on studies published in English and investigating AI implementation in health care since 2000. A total of 2541 unique publications were retrieved from the databases and screened on titles and abstracts by 2 independent reviewers. Selected articles were thematically analyzed against the Nilsen taxonomy of implementation frameworks, and the Greenhalgh framework for the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health care technologies. Results: In total, 7 articles met all eligibility criteria for inclusion in the review, and 2 articles included formal frameworks that directly addressed AI implementation, whereas the other articles provided limited descriptions of elements influencing implementation. Collectively, the 7 articles identified elements that aligned with all the NASSS domains, but no single article comprehensively considered the factors known to influence technology implementation. New domains were identified, including dependency on data input and existing processes, shared decision-making, the role of human oversight, and ethics of population impact and inequality, suggesting that existing frameworks do not fully consider the unique needs of AI implementation. Conclusions: This literature review demonstrates that understanding how to implement AI in health care practice is still in its early stages of development. Our findings suggest that ...