Priorities for Artificial Intelligence Applications in Primary Care: A Canadian Deliberative Dialogue with Patients, Providers, and Health System Leaders.

BACKGROUND: Artificial intelligence (AI) implementation in primary care is limited. Those set to be most impacted by AI technology in this setting should guide it's application. We organized a national deliberative dialogue with primary care stakeholders from across Canada to explore how they thought AI should be applied in primary care. METHODS: We conducted 12 virtual deliberative dialogues with participants from 8 Canadian provinces to identify shared priorities for applying AI in primary care. Dialogue data were thematically analyzed using interpretive description approaches. RESULTS: Participants thought that AI should first be applied to documentation, practice operations, and triage tasks, in hopes of improving efficiency while maintaining person-centered delivery, relationships, and access. They viewed complex AI-driven clinical decision support and proactive care tools as impactful but recognized potential risks. Appropriate training and implementation support were the most important external enablers of safe, effective, and patient-centered use of AI in primary care settings. INTERPRETATION: Our findings offer an agenda for the future application of AI in primary care grounded in the shared values of patients and providers. We propose that, from conception, AI developers work with primary care stakeholders as codesign partners, developing tools that respond to shared priorities.

Implementing artificial intelligence in Canadian primary care: Barriers and strategies identified through a national deliberative dialogue.

BACKGROUND: With large volumes of longitudinal data in electronic medical records from diverse patients, primary care is primed for disruption by artificial intelligence (AI) technology. With AI applications in primary care still at an early stage in Canada and most countries, there is a unique opportunity to engage key stakeholders in exploring how AI would be used and what implementation would look like. OBJECTIVE: To identify the barriers that patients, providers, and health leaders perceive in relation to implementing AI in primary care and strategies to overcome them. DESIGN: 12 virtual deliberative dialogues. Dialogue data were thematically analyzed using a combination of rapid ethnographic assessment and interpretive description techniques. SETTING: Virtual sessions. PARTICIPANTS: Participants from eight provinces in Canada, including 22 primary care service users, 21 interprofessional providers, and 5 health system leaders. RESULTS: The barriers that emerged from the deliberative dialogue sessions were grouped into four themes: (1) system and data readiness, (2) the potential for bias and inequity, (3) the regulation of AI and big data, and (4) the importance of people as technology enablers. Strategies to overcome the barriers in each of these themes were highlighted, where participatory co-design and iterative implementation were voiced most strongly by participants. LIMITATIONS: Only five health system leaders were included in the study and no self-identifying Indigenous people. This is a limitation as both groups may have provided unique perspectives to the study objective. CONCLUSIONS: These findings provide insight into the barriers and facilitators associated with implementing AI in primary care settings from different perspectives. This will be vital as decisions regarding the future of AI in this space is shaped.

Snakes and ladders: A qualitative study understanding the active ingredients of social interaction around the use of audit and feedback.

Explore characteristics of the facilitator, group, and interaction that influence whether a group discussion about data leads to the identification of a clearly specified action plan. Peer-facilitated group discussions among primary care physicians were carried out and recorded. A follow-up focus group was conducted with peer facilitators to explore which aspects of the discussion promoted action planning. Qualitative data was analyzed using an inductive-deductive thematic analysis approach using the conceptual model developed by Cooke et al. Group discussions were coded case-specifically and then analyzed to identify which themes influenced action planning as it relates to performance improvement. Physicians were more likely to interact with practice-level data and explore actions for performance improvement when the group facilitator focused the discussion on action planning. Only one of the three sites (Site C) converged on an action plan following the peer-facilitated group discussion. At Site A, physicians shared skepticism of the data, were defensive about performance, and explained performance as a product of factors beyond their control. Site B identified several potential actions but had trouble focusing on a single indicator or deciding between physician- and group-level actions. None of the groups discussed variation in physician-level performance indicators, or how physician actions might contribute to the reported outcomes. Peer facilitators can support data interpretation and practice change; however their success depends on their personal beliefs about the data and their ability to identify and leverage change cues that arise in conversation. Further research is needed to understand how to create a psychologically safe environment that welcomes open discussion of physician variation.

Family doctors have access to a lot of data on their practice. However, doctors report difficulties in thinking of ways to use this data to improve their practice. Group discussions among doctors may be one way to support practice improvements. This study analyzed discussions among three groups of doctors to see which aspects of the discussions helped the doctors come up with new ways to improve their practices. The ability of the person leading the discussion to continually re-focus the conversation on the goal of making a change was key to whether the group made any progress. The first group was skeptical of the data and felt that its findings were beyond their control; the second group had trouble focusing on a single outcome; and the third group successfully identified an action. None of the groups discussed how their actions might contribute to the outcomes.

Social determinants of COVID-19 incidence and outcomes: A rapid review.

Early reports indicate that the social determinants of health are implicated in COVID-19 incidence and outcomes. To inform the ongoing response to the pandemic, we conducted a rapid review of peer-reviewed studies to examine the social determinants of COVID-19. We searched Ovid MEDLINE, Embase, PsycINFO, CINAHL and Cochrane Central Register of Controlled Trials from December 1, 2019 to April 27, 2020. We also searched the bibliographies of included studies, COVID-19 evidence repositories and living evidence maps, and consulted with expert colleagues internationally. We included studies identified through these supplementary sources up to June 25, 2020. We included English-language peer-reviewed quantitative studies that used primary data to describe the social determinants of COVID-19 incidence, clinical presentation, health service use and outcomes in adults with a confirmed or presumptive diagnosis of COVID-19. Two reviewers extracted data and conducted quality assessment, confirmed by a third reviewer. Forty-two studies met inclusion criteria. The strongest evidence was from three large observational studies that found associations between race or ethnicity and socioeconomic deprivation and increased likelihood of COVID-19 incidence and subsequent hospitalization. Limited evidence was available on other key determinants, including occupation, educational attainment, housing status and food security. Assessing associations between sociodemographic factors and COVID-19 was limited by small samples, descriptive study designs, and the timeframe of our search. Systematic reviews of literature published subsequently are required to fully understand the magnitude of any effects and predictive utility of sociodemographic factors related to COVID-19 incidence and outcomes. PROSPERO: CRD4202017813.

Reducing Inequities During the COVID-19 Pandemic: A Rapid Review and Synthesis of Public Health Recommendations.

Objectives: Efforts to contain the COVID-19 pandemic should take into account worsening health inequities. While many public health experts have commented on inequities, no analysis has yet synthesized recommendations into a guideline for practitioners. The objective of this rapid review was to identify the areas of greatest concern and synthesize recommendations. Methods: We conducted a rapid systematic review (PROSPERO: CRD42020178131). We searched Ovid MEDLINE, Embase, PsycINFO, CINAHL and Cochrane Central Register of Controlled Trials databases from December 1, 2019 to April 27, 2020. We included English language peer-reviewed commentaries, editorials, and opinion pieces that addressed the social determinants of health in the context of COVID-19. Results: 338 articles met our criteria. Authors represented 81 countries. Income, housing, mental health, age and occupation were the most discussed social determinants of health. We categorized recommendations into primordial, primary, secondary and tertiary prevention that spoke to the social determinants of COVID-19 and equity. Conclusion: These recommendations can assist efforts to contain COVID-19 and reduce health inequities during the pandemic. Using these recommendations, public health practitioners could support a more equitable pandemic response. Systematic Review Registration: PROSPERO, CRD42020178131.