Randomized Trials Using Provincial Health Numbers for Group Assignment.

Purpose: Using data from Ontario, Canada, this report shows how provincial government-assigned health card numbers can be used for individual-level randomization in large pragmatic trials. We describe how health card numbers are assigned and analyze the distribution of health card digits in a trial setting. We then provide an example of how they can be used for randomization and discuss the methodological and practical considerations of the approach.

The hockey fans in training intervention for men with overweight or obesity: a pragmatic cluster randomised trial.

Background: Obesity disproportionately impacts men's health yet fewer men engage in preventive healthcare. We examined the effectiveness of Hockey Fans in Training (Hockey FIT), a gender-sensitised lifestyle intervention that engages men with overweight/obesity through their passion as fans of a local sports team, on weight change and other health indicators.

Methods: Pragmatic, cluster randomised trial (aged 35-65 years, body mass index ≥ 27 kg/m) within 42 community-based sites in Canada and the United States, randomly assigned (1:1) to intervention (Hockey FIT) or control (wait-list) and stratified by region.

Comments, suggestions, and criticisms of the Pragmatic Explanatory Continuum Indicator Summary-2 design tool: a citation analysis.

Introduction: The pragmatic explanatory continuum indicator summary (PRECIS) tool, initially published in 2009 and revised in 2015, was created to assist trialists to align their design choices with the intended purpose of their randomised controlled trial (RCT): either to guide real-world decisions between alternative interventions (pragmatic) or to test hypotheses about intervention mechanisms by minimising sources of variation (explanatory). There have been many comments, suggestions, and criticisms of PRECIS-2. This summary will be used to facilitate the development of to the next revision, which is PRECIS-3.

Randomised pilot study comparing a coach to SMARTPhone reminders to aid the management of heart failure (HF) patients: humans or machines.

Ambulatory management of congestive heart failure (HF) continues to be a challenging clinical problem. Recent studies have focused on the role of HF clinics, nurse practitioners and disease management programmes to reduce HF readmissions. This pilot study is a pragmatic factorial study comparing a coach intervention, a SMARTPHONE REMINDER system intervention and BOTH interventions combined to Treatment as USUAL (TAU).

Randomised trials conducted using cohorts: a scoping review.

Introduction: Cohort studies generate and collect longitudinal data for a variety of research purposes. Randomised controlled trials (RCTs) increasingly use cohort studies as data infrastructures to help identify and recruit trial participants and assess outcomes.

Objective: To examine the extent, range and nature of research using cohorts for RCTs and describe the varied definitions and conceptual boundaries for RCTs using cohorts.

Consensus Statement for Protocols of Factorial Randomized Trials: Extension of the SPIRIT 2013 Statement.

Importance: Trial protocols outline a trial's objectives as well as the methods (design, conduct, and analysis) that will be used to meet those objectives, and transparent reporting of trial protocols ensures objectives are clear and facilitates appraisal regarding the suitability of study methods. Factorial trials, in which 2 or more interventions are assessed in the same set of participants, have unique methodological considerations. However, no extension of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 Statement, which provides guidance on reporting of trial protocols, for factorial trials is available.

Reporting of Factorial Randomized Trials: Extension of the CONSORT 2010 Statement.

Importance: Transparent reporting of randomized trials is essential to facilitate critical appraisal and interpretation of results. Factorial trials, in which 2 or more interventions are assessed in the same set of participants, have unique methodological considerations. However, reporting of factorial trials is suboptimal.

Describing a complex primary health care population to support future decision support initiatives.

Introduction: Developing decision support tools using data from a health care organization, to support care within that organization, is a promising paradigm to improve care delivery and population health. Descriptive epidemiology may be a valuable supplement to stakeholder input towards selection of potential initiatives and to inform methodological decisions throughout tool development. We additionally propose that to properly characterize complex populations in large-scale descriptive studies, both simple statistical and machine learning techniques can be useful.

Perceptions of Artificial Intelligence Use in Primary Care: A Qualitative Study with Providers and Staff of Ontario Community Health Centres.

Purpose: To understand staff and health care providers' views on potential use of artificial intelligence (AI)-driven tools to help care for patients within a primary care setting.

Methods: We conducted a qualitative descriptive study using individual semistructured interviews. As part of province-wide Learning Health Organization, Community Health Centres (CHCs) are a community-governed, team-based delivery model providing primary care for people who experience marginalization in Ontario, Canada.

The Alliance for Healthier Communities' journey to a learning health system in primary care.

Introduction: The Alliance for Healthier Communities represents community-governed healthcare organizations in Ontario, Canada including Community Health Centres, which provide primary care to more disadvantaged populations.

Methods: In this experience report, we describe the Alliance's journey towards becoming a learning health system using examples for organizational culture, data and analytics, people and partnerships, client engagement, ethics and oversight, evaluation and dissemination, resources, identification and prioritization, and deliverables and impact.

Results: Many of the foundational elements for a learning health system were already in place at the Alliance including an integrated and accessible data platform.

Heterogeneity in pragmatic randomised trials: sources and management.

Background: Pragmatic trials aim to generate evidence to directly inform patient, caregiver and health-system manager policies and decisions. Heterogeneity in patient characteristics contributes to heterogeneity in their response to the intervention. However, there are many other sources of heterogeneity in outcomes.

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.

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.

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.

An analysis of published trials found that current use of pragmatic trial labels is uninformative.

Objectives: Randomized trials labelled as "pragmatic" are attractive to funders, patients, and clinicians as the label implies that the results are directly applicable to clinical care. We examined how authors justify use of the label (e.g.

Achieving holistic, quality-of-life focused care: description of a Compassion Care Community initiative in Canada.

The compassionate community movement as both a public health approach and a social model of care for various life stages is gaining traction in Canada and elsewhere. One example is the Windsor-Essex Compassion Care Community (WECCC), an evidence-based model and set of tools to improve the quality of life, health and wellness of vulnerable and aging populations by identifying and addressing upstream and downstream social and other risks to physical and mental health. This paper presents findings from the WECCC pilot evaluation.

The Alliance for Healthier Communities as a Learning Health System for primary care: A qualitative analysis in Ontario, Canada.

Rationale, Aims And Objectives: A learning health system model can be used to efficiently evaluate and incorporate evidence-based care into practice. However, there is a paucity of evidence describing key organizational attributes needed to ensure a successful learning health system within primary care. We interviewed stakeholders for a primary care learning health system in Ontario, Canada (the Alliance for Healthier Communities) to identify strengths and areas for improvement.

Identifying priorities for artificial intelligence and primary care in ontario: A multi-stakeholder engagement event.

CONTEXT: Artificial intelligence (AI) is increasingly being recognized as having potential importance to primary care (PC). However, there is a gap in our understanding about where to focus efforts related to AI for PC settings, especially given the current COVID-19 pandemic. OBJECTIVE: To identify current priority areas for AI and PC in Ontario, Canada.

Is primary health care ready for artificial intelligence? Stakeholder perspectives: Worth the risk as long as you do it well.

Context: The effective deployment of artificial intelligence (AI) in primary health care requires a match between the AI tools that are being developed and the needs of primary health care practitioners and patients. Currently, the majority of AI development targeted toward potential application in primary care is being conducted without the involvement of these stakeholders. Objective: 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.

Tools for assessing the scalability of innovations in health: a systematic review.

Background: The last decade has seen growing interest in scaling up of innovations to strengthen healthcare systems. However, the lack of appropriate methods for determining their potential for scale-up is an unfortunate global handicap. Thus, we aimed to review tools proposed for assessing the scalability of innovations in health.

Connecting artificial intelligence and primary care challenges: findings from a multi stakeholder collaborative consultation.

Unlabelled: Despite widespread advancements in and envisioned uses for artificial intelligence (AI), few examples of successfully implemented AI innovations exist in primary care (PC) settings.

Objectives: To identify priority areas for AI and PC in Ontario, Canada.

Methods: A collaborative consultation event engaged multiple stakeholders in a nominal group technique process to generate, discuss and rank ideas for how AI can support Ontario PC.