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ObjectivesGiven widespread interest in applying artificial intelligence (AI) to health data to improve patient care and health system efficiency, there is a need to understand the perspectives of the general public regarding the use of health data in AI research.DesignA qualitative study involving six focus groups with members of the public. Participants discussed their views about AI in general, then were asked to share their thoughts about three realistic health AI research scenarios. Data were analysed using qualitative description thematic analysis.SettingsTwo cities in Ontario, Canada: Sudbury (400 km north of Toronto) and Mississauga (part of the Greater Toronto Area).ParticipantsForty-one purposively sampled members of the public (21M:20F, 25–65 years, median age 40).ResultsParticipants had low levels of prior knowledge of AI and mixed, mostly negative, perceptions of AI in general. Most endorsed using data for health AI research when there is strong potential for public benefit, providing that concerns about privacy, commercial motives and other risks were addressed. Inductive thematic analysis identified AI-specific hopes (eg, potential for faster and more accurate analyses, ability to use more data), fears (eg, loss of human touch, skill depreciation from over-reliance on machines) and conditions (eg, human verification of computer-aided decisions, transparency). There were mixed views about whether data subject consent is required for health AI research, with most participants wanting to know if, how and by whom their data were used. Though it was not an objective of the study, realistic health AI scenarios were found to have an educational effect.ConclusionsNotwithstanding concerns and limited knowledge about AI in general, most members of the general public in six focus groups in Ontario, Canada perceived benefits from health AI and conditionally supported the use of health data for AI research.

ObjectivesTo identify ML tools in hospital settings and how they were implemented to inform decision-making for patient care through a scoping review. We investigated the following research questions: What ML interventions have been used to inform decision-making for patient care in hospital settings? What strategies have been used to implement these ML interventions?DesignA scoping review was undertaken. MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) and the Cochrane Database of Systematic Reviews (CDSR) were searched from 2009 until June 2021. Two reviewers screened titles and abstracts, full-text articles, and charted data independently. Conflicts were resolved by another reviewer. Data were summarised descriptively using simple content analysis.SettingHospital setting.ParticipantAny type of clinician caring for any type of patient.InterventionMachine learning tools used by clinicians to inform decision-making for patient care, such as AI-based computerised decision support systems or “‘model-based’” decision support systems.Primary and secondary outcome measuresPatient and study characteristics, as well as intervention characteristics including the type of machine learning tool, implementation strategies, target population. Equity issues were examined with PROGRESS-PLUS criteria.ResultsAfter screening 17 386 citations and 3474 full-text articles, 20 unique studies and 1 companion report were included. The included articles totalled 82 656 patients and 915 clinicians. Seven studies reported gender and four studies reported PROGRESS-PLUS criteria (race, health insurance, rural/urban). Common implementation strategies for the tools were clinician reminders that integrated ML predictions (44.4%), facilitated relay of clinical information (17.8%) and staff education (15.6%). Common barriers to successful implementation of ML tools were time (11.1%) and reliability (11.1%), and common facilitators were time/efficiency (13.6%) and perceived usefulness (13.6%).ConclusionsWe found limited evidence related to the implementation of ML tools to assist clinicians with patient healthcare decisions in hospital settings. Future research should examine other approaches to integrating ML into hospital clinician decisions related to patient care, and report on PROGRESS-PLUS items.FundingCanadian Institutes of Health Research (CIHR) Foundation grant awarded to SES and the CIHR Strategy for Patient Oriented-Research Initiative (GSR-154442).Scoping review registrationhttps://osf.io/e2mna.

Background There is a widely recognized need for more pragmatic trials that evaluate interventions in real-world settings to inform decision-making by patients, providers, and health system leaders. Increasing availability of electronic health records, centralized research ethics review, and novel trial designs, combined with support and resources from governments worldwide for patient-centered research, have created an unprecedented opportunity to advance the conduct of pragmatic trials, which can ultimately improve patient health and health system outcomes. Such trials raise ethical issues that have not yet been fully addressed, with existing literature concentrating on regulations in specific jurisdictions rather than arguments grounded in ethical principles. Proposed solutions (e.g. using different regulations in “learning healthcare systems”) are speculative with no guarantee of improvement over existing oversight procedures. Most importantly, the literature does not reflect a broad vision of protecting the core liberty and welfare interests of research participants. Novel ethical guidance is required. We have assembled a team of ethicists, trialists, methodologists, social scientists, knowledge users, and community members with the goal of developing guidance for the ethical design and conduct of pragmatic trials. Methods Our project will combine empirical and conceptual work and a consensus development process. Empirical work will: (1) identify a comprehensive list of ethical issues through interviews with a small group of key informants (e.g. trialists, ethicists, chairs of research ethics committees); (2) document current practices by reviewing a random sample of pragmatic trials and surveying authors; (3) elicit views of chairs of research ethics committees through surveys in Canada, UK, USA, France, and Australia; and (4) elicit views and experiences of community members and health system leaders through focus groups and surveys. Conceptual work will consist of an ethical analysis of identified issues and the development of new ethical solutions, outlining principles, policy options, and rationales. The consensus development process will involve an independent expert panel to develop a final guidance document. Discussion Planned output includes manuscripts, educational materials, and tailored guidance documents to inform and support researchers, research ethics committees, journal editors, regulators, and funders in the ethical design and conduct of pragmatic trials.

The aim of this paper is to review the literature on barriers to conducting replication research and strategies to increase its use and promotion by researchers, editors, and funders. This review was part of a larger meta-narrative review aimed at conducting a concept analysis of replication and developing a replication research framework. A combination of systematic and snowball search strategies was used to identify relevant literature in multiple research fields. Data were coded and analyzed using the Theoretical Domains Framework for barriers to replication and the behavior change wheel for solutions. In total, 153 papers were included in this narrative review. Multiple barriers limit the use of replication research by researchers, editors, and funders. Many of the barriers were related to knowledge and skills of all these actors. Social influences and the research environmental context were also described as not supportive. Multiple strategies were proposed to create positive outcomes expectations, reinforcement, and structural changes in the physical and social context of research. A social change involving advisory groups, research organizations, and institutions is required to establish new norms that will value, promote, support, and reward replication research. •Multiple barriers deeply rooted in the culture of research limit the conduct of replication research.•Social influences, environmental context, reinforcement, knowledge, and skills are all inter-related factors influencing researchers’ behavior about replication research.•Editors and funders can promote and provide opportunities and conditions to increase use of replication.•Strategies found in the literature suggest that a social and community change is required to address these multilevel barriers.

The aim of this study is to clarify the concept of replication research to improve its appropriate use by researchers, editors, research funders, and decision makers. We combined concept analysis and metanarrative review methods to synthetize knowledge on replication research from various scientific fields. We used multiple search strategies to identify the relevant literature published before April 2018. We summarized the data by seeking commonalities and differences in underlying conceptual and theoretical assumptions in the literature. A total of 153 articles from various disciplines were included. The analysis led to the identification of three major definitions of replication: the repetition of a previous study, the extension of a previous study, and the road-testing of a theory. Attributes, conditions required to conduct replication studies, concerns related to the interpretation of replication studies, and diverse replication research typologies were synthesized, combined, and analyzed. Based on this metanarrative review, a comprehensive theoretical definition of replication research was formulated. This study can support the adoption of a shared understanding and recognition of the indispensable nature of replication research for the sound development of knowledge in all research fields. •There is a lack of consensus in the scientific literature on a definition of replication research.•Replication studies can be conducted for different purposes and characterized in accordance with different attributes such as the similarity between research questions and the level of compliance with the methods.•When a replication study confirms the results of an index study or fails to confirm these results, it can lead to better understanding of previous research findings.•Replication studies should be planned and thoroughly designed in accordance with their specific purposes whether this is to improve the reliability, validity, and generalizability of existing knowledge.

Objectives: There is widespread commitment—at least in principle—to “evidence-informed” clinical practice and policy development in health care. The intention is that only “appropriate” care ought to be delivered at public expense. Although the rationale for an appropriateness agenda is widely endorsed, and methods have been proposed for addressing it, few published studies exist of contemporary policy initiatives which have actually led to successful disinvestment. Our objective was to explore whether the direct involvement of policy stakeholders could advance appropriateness and disinvestment. Methods: Several collaborative engagements with policy stakeholders were undertaken to adapt and combine conceptual and empirical material related to appropriateness and disinvestment from the literature to create tools and processes for use in Canada and the province of Ontario in particular. Results: By combining inputs from the literature with colloquial evidence from policy stakeholders, a definition of appropriateness was developed and, importantly, endorsed by all the provincial and territorial ministers of health in Canada. Second, a reassessment framework was successfully implemented for identifying priorities for selective disinvestment. Conclusions: When scientific evidence was combined with colloquial evidence from policy stakeholders, progress was made on the design and successful implementation of policies for appropriateness and disinvestment.

Academic research projects receive hundreds of billions of dollars of government investment each year. They complement business research projects by focusing on the generation of new foundational knowledge and addressing societal challenges. Despite the importance of academic research, the management of it is often undisciplined and ad hoc. It has been postulated that the inherent uncertainty and complexity of academic research projects make them challenging to manage. However, based on this study's analysis of input and voting from more than 500 academic research team members in facilitated risk management sessions, the most important perceived risks are general, as opposed to being research specific. Overall participants' top risks related to funding, team instability, unreliable partners, study participant recruitment, and data access. Many of these risks would require system- or organization-level responses that are beyond the scope of individual academic research teams.

Academic research projects receive hundreds of billions of dollars of government investment each year. They complement business research projects by focusing on the generation of new foundational knowledge and addressing societal challenges. Despite the importance of academic research, the management of it is often undisciplined and ad hoc. It has been postulated that the inherent uncertainty and complexity of academic research projects make them challenging to manage. However, based on this study's analysis of input and voting from more than 500 academic research team members in facilitated risk management sessions, the most important perceived risks are general, as opposed to being research specific. Overall participants' top risks related to funding, team instability, unreliable partners, study participant recruitment, and data access. Many of these risks would require system- or organization-level responses that are beyond the scope of individual academic research teams.