The relationship between pragmatism, timing, and study size on impact of randomized trials: a qualitative, hypothesis generating study of trials of systemic corticosteroids for COVID-19.

OBJECTIVE: To explore qualitatively the relationship between selected trial design choices and proxies for a scientific and clinical uptake in a cohort of published randomized controlled trials (RCTs) of corticosteroids for COVID-19, to identify design characteristics that may result in trials with potential to eliminate equipoise, achieve uptake, and help reduce research waste. STUDY DESIGN AND SETTING: A systematic literature search and qualitative, narrative review of published RCTs (up to April 13, 2021) evaluating the effectiveness of systemic corticosteroids in treatment of COVID-19. We extracted information on sample size, number of centers, single-country or multi-country conduct, dates of initiation and of publication, risk of bias and pragmatism scores, and also on an impact measured by citation in scientific literature and in clinical guidelines. We qualitatively compared design features of the highest impact vs. other trials. RESULTS: Randomised Evaluation of COVID-19 Therapy (RECOVERY) was by the most impactful of the seven eligible RCTs as it was 10 times more frequently cited in peer-reviewed literature and influenced all the selected COVID-19 treatment guidelines. All trials started recruiting from similar dates. RECOVERY was a single-country, multicentre platform trial at low risk of bias, features which also fail to distinguish it from the other trials. RECOVERY was distinguished by more strongly pragmatic design features, more centers, and more rapid recruitment resulting in a larger sample size and early publication. CONCLUSION: Higher pragmatism scores may contribute to recruiting more centers and more rapid recruitment of patients at each center, leading to larger size, earlier publication, and greater scientific and guideline uptake. By eliminating equipoise, RECOVERY rendered other simultaneous trials redundant. Further work is needed to confirm these findings in a larger quantitative study and to identify the individual contribution of each characteristic of pragmatism to conduct and impact of trials and their interaction in different national contexts. Until then, research waste might be reduced by designing trials with as many of the characteristics of RECOVERY as is feasible.

How pragmatic are randomized trials of remdesivir and favipiravir for in-hospital treatment of COVID-19: a descriptive methodological review of trial design using the PRECIS-2 framework.

OBJECTIVES: To review the pragmatism of published randomized trials of remdesivir and favipiravir based on the Pragmatic-Explanatory Continuum Indicator Summary (PRECIS-2) framework. STUDY DESIGN AND SETTING: Ten eligible trials were identified from an existing comprehensive living review and were evaluated across the nine PRECIS-2 domains by two independent reviewers. RESULTS: All 10 trials had mostly pragmatic design characteristics. Four of the domains (i.e., recruitment, setting, organization, and primary analysis) were found to be pragmatic with most trials scoring four or five across the two interventions. In comparison scores for four other design domains (i.e., eligibility, follow-up, flexibility of delivery, and primary outcome) varied across the trials with some design choices being more explanatory. CONCLUSION: In our descriptive review of randomized controlled trails for two drugs for patients infected with COVID-19 early in the pandemic, we found that most trials had more pragmatic than explanatory characteristics. Some design choices for some of the trials, however, were not consistent with the urgent goal of informing clinical decision making in an epidemic. PRECIS-2 should be used as a guide by trialists, to help them match their trial design choices to the intended purpose of their trial.

Is primary health care ready for artificial intelligence? What do primary health care stakeholders say?

BACKGROUND: Effective deployment of AI tools in primary health care requires the engagement of practitioners in the development and testing of these tools, and a match between the resulting AI tools and clinical/system needs in primary health care. To set the stage for these developments, we must gain a more in-depth understanding of the views of practitioners and decision-makers about the use of AI in primary health care. The objective of this study was to identify key issues regarding the use of AI tools in primary health care by exploring the views of primary health care and digital health stakeholders. METHODS: This study utilized a descriptive qualitative approach, including thematic data analysis. Fourteen in-depth interviews were conducted with primary health care and digital health stakeholders in Ontario. NVivo software was utilized in the coding of the interviews. RESULTS: Five main interconnected themes emerged: (1) Mismatch Between Envisioned Uses and Current Reality-denoting the importance of potential applications of AI in primary health care practice, with a recognition of the current reality characterized by a lack of available tools; (2) Mechanics of AI Don't Matter: Just Another Tool in the Toolbox- reflecting an interest in what value AI tools could bring to practice, rather than concern with the mechanics of the AI tools themselves; (3) AI in Practice: A Double-Edged Sword-the possible benefits of AI use in primary health care contrasted with fundamental concern about the possible threats posed by AI in terms of clinical skills and capacity, mistakes, and loss of control; (4) The Non-Starters: A Guarded Stance Regarding AI Adoption in Primary Health Care-broader concerns centred on the ethical, legal, and social implications of AI use in primary health care; and (5) Necessary Elements: Facilitators of AI in Primary Health Care-elements required to support the uptake of AI tools, including co-creation, availability and use of high quality data, and the need for evaluation. CONCLUSION: The use of AI in primary health care may have a positive impact, but many factors need to be considered regarding its implementation. This study may help to inform the development and deployment of AI tools in primary health care.

Artificial Intelligence and Primary Care Research: A Scoping Review.

PURPOSE: Rapid increases in technology and data motivate the application of artificial intelligence (AI) to primary care, but no comprehensive review exists to guide these efforts. Our objective was to assess the nature and extent of the body of research on AI for primary care. METHODS: We performed a scoping review, searching 11 published or gray literature databases with terms pertaining to AI (eg, machine learning, bayes* network) and primary care (eg, general pract*, nurse). We performed title and abstract and then full-text screening using Covidence. Studies had to involve research, include both AI and primary care, and be published in Eng-lish. We extracted data and summarized studies by 7 attributes: purpose(s); author appointment(s); primary care function(s); intended end user(s); health condition(s); geographic location of data source; and AI subfield(s). RESULTS: Of 5,515 unique documents, 405 met eligibility criteria. The body of research focused on developing or modifying AI methods (66.7%) to support physician diagnostic or treatment recommendations (36.5% and 13.8%), for chronic conditions, using data from higher-income countries. Few studies (14.1%) had even a single author with a primary care appointment. The predominant AI subfields were supervised machine learning (40.0%) and expert systems (22.2%). CONCLUSIONS: Research on AI for primary care is at an early stage of maturity. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed.