Mailed feedback to primary care physicians on antibiotic prescribing for patients aged 65 years and older: pragmatic, factorial randomised controlled trial.

OBJECTIVES: To evaluate whether providing family physicians with feedback on their antibiotic prescribing compared with that of their peers reduces antibiotic prescriptions. To also identify effects on antibiotic prescribing from case-mix adjusted feedback reports and messages emphasising antibiotic associated harms. DESIGN: Pragmatic, factorial randomised controlled trial. SETTING: Primary care physicians in Ontario, Canada PARTICIPANTS: All primary care physicians were randomly assigned a group if they were eligible and actively prescribing antibiotics to patients 65 years or older. Physicians were excluded if had already volunteered to receive antibiotic prescribing feedback from another agency, or had opted out of the trial. INTERVENTION: A letter was mailed in January 2022 to physicians with peer comparison antibiotic prescribing feedback compared with the control group who did not receive a letter (4:1 allocation). The intervention group was further randomised in a 2x2 factorial trial to evaluate case-mix adjusted versus unadjusted comparators, and emphasis, or not, on harms of antibiotics. MAIN OUTCOME MEASURES: Antibiotic prescribing rate per 1000 patient visits for patients 65 years or older six months after intervention. Analysis was in the modified intention-to-treat population using Poisson regression. RESULTS: 5046 physicians were included and analysed: 1005 in control group and 4041 in intervention group (1016 case-mix adjusted data and harms messaging, 1006 with case-mix adjusted data and no harms messaging, 1006 unadjusted data and harms messaging, and 1013 unadjusted data and no harms messaging). At six months, mean antibiotic prescribing rate was 59.4 (standard deviation 42.0) in the control group and 56.0 (39.2) in the intervention group (relative rate 0.95 (95% confidence interval 0.94 to 0.96). Unnecessary antibiotic prescribing (0.89 (0.86 to 0.92)), prolonged duration prescriptions defined as more than seven days (0.85 (0.83 to 0.87)), and broad spectrum prescribing (0.94 (0.92 to 0.95)) were also significantly lower in the intervention group compared with the control group. Results were consistent at 12 months post intervention. No significant effect was seen for including emphasis on harms messaging. A small increase in antibiotic prescribing with case-mix adjusted reports was noted (1.01 (1.00 to 1.03)). CONCLUSIONS: Peer comparison audit and feedback letters significantly reduced overall antibiotic prescribing with no benefit of case-mix adjustment or harms messaging. Antibiotic prescribing audit and feedback is a scalable and effective intervention and should be a routine quality improvement initiative in primary care. TRIAL REGISTRATION: ClinicalTrials.gov NCT04594200.

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. DESIGN: Scoping review. DATA SOURCES: Searches were undertaken in January 2021 in MEDLINE (Ovid) and EBM Reviews-Cochrane Methodology Registry (Final issue, third Quarter 2012). ELIGIBILITY CRITERIA: Reports published between January 2007 and December 2021 of (a) cohorts used or planned to be used, to conduct RCTs, or (b) RCTs which use cohorts to recruit participants and/or collect trial outcomes, or (c) methodological studies discussing the use of cohorts for RCTs. DATA EXTRACTION AND SYNTHESIS: Data were extracted on the condition being studied, age group, setting, country/continent, intervention(s) and comparators planned or received, unit of randomisation, timing of randomisation, approach to informed consent, study design and terminology. RESULTS: A total of 175 full-text articles were assessed for eligibility. We identified 61 protocols, 9 descriptions of stand-alone cohorts intended to be used for future RCTs, 39 RCTs using cohorts and 34 methodological papers.The use and scope of this approach is growing. The thematics of study are far-ranging, including population health, oncology, mental and behavioural disorders, and musculoskeletal conditions.Authors reported that this approach can lead to more efficient recruitment, more representative samples, and lessen disappointment bias and crossovers. CONCLUSION: This review outlines the development of cohorts to conduct RCTs including the range of use and innovative changes and adaptations. Inconsistencies in the use of terminology and concepts are highlighted. Guidance now needs to be developed to support the design and reporting of RCTs conducted 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. Objective: To develop a consensus-based extension to the SPIRIT 2013 Statement for factorial trials. Evidence Review: The SPIRIT extension for factorial trials was developed using the Enhancing the Quality and Transparency of Health Research (EQUATOR) methodological framework. First, a list of reporting recommendations was generated using a scoping review of methodological articles identified using a MEDLINE search (inception to May 2019), which was supplemented with relevant articles from the personal collections of the authors. Second, a 3-round Delphi survey (January to June 2022, completed by 104 panelists from 14 countries) was conducted to assess the importance of items and identify additional recommendations. Third, a hybrid consensus meeting was held, attended by 15 panelists to finalize selection and wording of the checklist. Findings: This SPIRIT extension for factorial trials modified 9 of the 33 items in the SPIRIT 2013 checklist. Key reporting recommendations were that the rationale for using a factorial design should be provided, including whether an interaction is hypothesized; the treatment groups that will form the main comparisons should be identified; and statistical methods for each main comparison should be provided, including how interactions will be assessed. Conclusions and Relevance: In this consensus statement, 9 factorial-specific items were provided that should be addressed in all protocols of factorial trials to increase the trial's utility and transparency.

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. Objective: To develop a consensus-based extension to the Consolidated Standards of Reporting Trials (CONSORT) 2010 Statement for factorial trials. Design: Using the Enhancing the Quality and Transparency of Health Research (EQUATOR) methodological framework, the CONSORT extension for factorial trials was developed by (1) generating a list of reporting recommendations for factorial trials using a scoping review of methodological articles identified using a MEDLINE search (from inception to May 2019) and supplemented with relevant articles from the personal collections of the authors; (2) a 3-round Delphi survey between January and June 2022 to identify additional items and assess the importance of each item, completed by 104 panelists from 14 countries; and (3) a hybrid consensus meeting attended by 15 panelists to finalize the selection and wording of items for the checklist. Findings: This CONSORT extension for factorial trials modifies 16 of the 37 items in the CONSORT 2010 checklist and adds 1 new item. The rationale for the importance of each item is provided. Key recommendations are (1) the reason for using a factorial design should be reported, including whether an interaction is hypothesized, (2) the treatment groups that form the main comparisons should be clearly identified, and (3) for each main comparison, the estimated interaction effect and its precision should be reported. Conclusions and Relevance: This extension of the CONSORT 2010 Statement provides guidance on the reporting of factorial randomized trials and should facilitate greater understanding of and transparency in their reporting.

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. CHC health care providers and staff were invited to participate. Interviews were audio-recorded and transcribed verbatim. We performed a thematic analysis using a team approach. RESULTS: We interviewed 27 participants across 6 CHCs. Participants lacked in-depth knowledge about AI. Trust was essential to acceptance of AI; people need to be receptive to using AI and feel confident that the information is accurate. We identified internal influences of AI acceptance, including ease of use and complementing clinical judgment rather than replacing it. External influences included privacy, liability, and financial considerations. Participants felt AI could improve patient care and help prevent burnout for providers; however, there were concerns about the impact on the patient-provider relationship. CONCLUSIONS: The information gained in this study can be used for future research, development, and integration of AI technology.

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. Leadership championed and embraced the movement towards a learning health system, which led to restructuring of the organization. This included role changes for data support personnel, better communication, and dissemination plans, strategies to engage clinicians and other front-line staff, restructuring of committees for more collaborative planning and prioritization of quality improvement and research initiatives, and the development of a new Practice-Based Learning Network for more opportunities to use the data for research and evaluation. Conclusions: Next steps will focus on continued clinical engagement and partnerships as well as ongoing reflection on the transition and success of the learning health system work.

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.

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.

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. Based on the expertise and judgements of the authors, we identify different sources of clinical and methodological heterogeneity, which translate into heterogeneity in patient responses-some we consider as desirable and some as undesirable. For each of them, we discuss and, using real-world trial examples, illustrate how heterogeneity should be managed over the whole course of the trial. MAIN TEXT: Heterogeneity in centres and patients should be welcomed rather than limited. Interventions can be flexible or tailored and control interventions are expected to reflect usual care, avoiding use of a placebo. Co-interventions should be allowed; adherence should not be enforced. All these elements introduce heterogeneity in interventions (experimental or control), which has to be welcomed because it mimics reality. Outcomes should be objective and possibly routinely collected; standardised assessment, blinding and adjudication should be avoided as much as possible because this is not how assessment would be done outside a trial setting. The statistical analysis strategy must be guided by the objective to inform decision-making, thus favouring the intention-to-treat principle. Pragmatic trials should consider including process analyses to inform an understanding of the trial results. Needed data to conduct these analyses should be collected unobtrusively. Finally, ethical principles must be respected, even though this may seem to conflict with goals of pragmatism; consent procedures could be incorporated in the flow of care.

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.

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., by referring to one or more PRECIS [PRagmatic Explanatory Continuum Indicator Summary]-2 domains). STUDY DESIGN AND SETTING: We reviewed primary trial reports published 2014-2019, registered in ClinicalTrials.gov and using the pragmatic label anywhere in the report. RESULTS: Among 415 trials, the label was justified by reference to at least one design element in 282 (68.0%); of these, 240 (85.1%) referenced trial characteristics that can be mapped to one or more of the PRECIS-2 domains, most commonly eligibility (91, 32.3%), setting (90, 31.9%), flexibility delivery (89, 31.6%), and organization (75, 26.6%); 42 (14.9%) referenced characteristics that are not PRECIS-2 domains, most commonly type of intervention/comparator (48, 17%), recruitment without consent (22, 7.8%), routinely collected data (22, 7.8%), and cluster randomization (20, 7.1%). Most reports referenced only one or two design elements. Overall, 9/415 (2.2%) provided PRECIS wheels. CONCLUSION: Current use of pragmatic labels is uninformative. Authors should clarify the decision the trial is intended to support and include a PRECIS-2 table to make the design transparent.

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 WECCC initiative provided one-on-one volunteer-supported quality of life assessment, resource navigation and goals support program (Catalyzing Community Connections). This was augmented with public education sessions on social connection and loneliness (Importance of Being Connected) for the broader population. The RE-AIM framework was used to frame evaluation of WECCC through the first 4 years. Questionnaires were used to evaluate participant outcomes related to implementation and effectiveness. Interviews and focus groups were completed to understand impacts. From 2017 to 2020, WECCC has engaged over 2,500 individuals, 65 organizations and 400 volunteers combined in both programs. Nearly all (82% to 95%) participants reported positive changes to health, quality of life and/or social connections. This developmental phase of a compassionate community initiative has allowed piloting of an evaluation framework focusing on reach, adoption, implementation and early signals of effectiveness and maintenance. This demonstration provides information on feasibility, acceptability and potential impacts of this type of over-arching community initiative.

The compassionate communities movement is a social and holistic approach to care that engages community members in caring for others. The movement is growing around the world. The philosophy is that all citizens benefit from participating in care for others who are aging, disabled, nearing end-of-life or struggling with determinants of health. The Windsor-Essex Compassion Care Community (WECCC) is a Canadian example of this model. WECCC seeks to improve the health of people at any stage of life by helping them to identify their life needs and goals and providing them support to meet them. The program has two core components: one-on-one volunteer-support for person-directed goals and navigation (Catalyzing Community Connections) and public education sessions about loneliness and social connection (Importance of Being Connected). Evaluation is a routine part of program delivery with questionnaires, interviews and focus groups used to understand the program's outcomes. From 2017 to 2020, WECCC has worked with over 2,500 individuals, 65 organizations and 400 volunteers. Satisfaction with the program is very high (95%). Nearly all participants reported positive changes to their quality of life (82%) and social connections (95%). Although pilot results are favorable, continued evaluation and stronger research designs are needed to comprehensively evaluate the WECCC program over time and to support growth and spread of the model.

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. METHOD: We conducted a qualitative descriptive study using individual semistructured interviews with Alliance stakeholders between December 2019 and March 2020. The Alliance delivers community-governed primary healthcare through 109 organizations including Community Health Centres (CHCs). All CHC staff within the Alliance were invited to participate. Interviews were audio-recorded and transcribed verbatim. We performed a thematic analysis using a team approach. RESULTS: We interviewed 29 participants across six CHCs, including Executive Directors, managers, healthcare providers and data support staff. We observed three foundational elements necessary for a successful learning health system within primary care: shared organizational goals and culture, data quality and resources. Building on this foundation, people are needed to drive the learning health system, and this is conditional on their level of engagement. The main factors motivating staff member's engagement with the learning health system included their drive to help improve patient care, focusing on initiatives of personal interest and understanding the purpose of different initiatives. Areas for improvement were identified such as the ability to extract and use data to inform changes in real-time, better engagement and protected time for providers to do improvement work, and more staff dedicated to data extraction and analysis. CONCLUSIONS: We identified key components needed to establish a learning health system in primary care. Similar primary care organizations in Canada and elsewhere can use these insights to guide their development as learning health systems.

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. METHODS: We conducted a systematic review following the COSMIN methodology. We included any empirical research which aimed to investigate the creation, validation or interpretability of a scalability assessment tool in health. We searched Embase, MEDLINE, CINAHL, Web of Science, PsycINFO, Cochrane Library and ERIC from their inception to 20 March 2019. We also searched relevant websites, screened the reference lists of relevant reports and consulted experts in the field. Two reviewers independently selected and extracted eligible reports and assessed the methodological quality of tools. We summarized data using a narrative approach involving thematic syntheses and descriptive statistics. RESULTS: We identified 31 reports describing 21 tools. Types of tools included criteria (47.6%), scales (33.3%) and checklists (19.0%). Most tools were published from 2010 onwards (90.5%), in open-access sources (85.7%) and funded by governmental or nongovernmental organizations (76.2%). All tools were in English; four were translated into French or Spanish (19.0%). Tool creation involved single (23.8%) or multiple (19.0%) types of stakeholders, or stakeholder involvement was not reported (57.1%). No studies reported involving patients or the public, or reported the sex of tool creators. Tools were created for use in high-income countries (28.6%), low- or middle-income countries (19.0%), or both (9.5%), or for transferring innovations from low- or middle-income countries to high-income countries (4.8%). Healthcare levels included public or population health (47.6%), primary healthcare (33.3%) and home care (4.8%). Most tools provided limited information on content validity (85.7%), and none reported on other measurement properties. The methodological quality of tools was deemed inadequate (61.9%) or doubtful (38.1%). CONCLUSIONS: We inventoried tools for assessing the scalability of innovations in health. Existing tools are as yet of limited utility for assessing scalability in health. More work needs to be done to establish key psychometric properties of these tools. Trial registration We registered this review with PROSPERO (identifier: CRD42019107095).

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

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. RESULTS: The consultation process produced nine ranked priorities: (1) preventative care and risk profiling, (2) patient self-management of condition(s), (3) management and synthesis of information, (4) improved communication between PC and AI stakeholders, (5) data sharing and interoperability, (6-tie) clinical decision support, (6-tie) administrative staff support, (8) practitioner clerical and routine task support and (9) increased mental healthcare capacity and support. Themes emerging from small group discussions about barriers, implementation issues and resources needed to support the priorities included: equity and the digital divide; system capacity and culture; data availability and quality; legal and ethical issues; user-centred design; patient-centredness; and proper evaluation of AI-driven tool implementation. DISCUSSION: Findings provide guidance for future work on AI and PC. There are immediate opportunities to use existing resources to develop and test AI for priority areas at the patient, provider and system level. For larger scale, sustainable innovations, there is a need for longer-term projects that lay foundations around data and interdisciplinary work. CONCLUSION: Study findings can be used to inform future research and development of AI for PC, and to guide resource planning and allocation.

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. Objective: To describe sociodemographic, clinical, and health care use characteristics of primary care clients served by the Alliance for Healthier Communities, which provides team-based primary health care through Community Health Centres (CHCs) across Ontario, Canada. Methods: We used electronic health record data from adult ongoing primary care clients served by CHCs in 2009-2019. We performed traditional table-based summaries for each characteristic; and applied three unsupervised learning techniques to explore patterns of common condition co-occurrence, care provider teams, and care frequency. Results: There were 221,047 eligible clients. Sociodemographics: We described 13 characteristics, stratified by CHC type and client multimorbidity status. Clinical characteristics: Eleven-year prevalence of 24 investigated conditions ranged from 1% (Hepatitis C) to 63% (chronic musculoskeletal problem) with non-uniform risk across the care history; multimorbidity was common (81%) with variable co-occurrence patterns. Health care use characteristics: Most care was provided by physician and nursing providers, with heterogeneous combinations of other provider types. A subset of clients had many issues addressed within single-visits and there was within- and between-client variability in care frequency. In addition to substantive findings, we discuss methodological considerations for future decision support initiatives. Conclusions: We demonstrated the use of methods from statistics and machine learning, applied with an epidemiological lens, to provide an overview of a complex primary care population and lay a foundation for stakeholder engagement and decision support tool development.

Reporting transparency and completeness in trials: Paper 3 - trials conducted using administrative databases do not adequately report elements related to use of databases.

OBJECTIVE: We evaluated reporting completeness and transparency in randomized controlled trials (RCTs) conducted using administrative data based on 2021 CONSORT Extension for Trials Conducted Using Cohorts and Routinely Collected Data (CONSORT-ROUTINE) criteria. STUDY DESIGN AND SETTING: MEDLINE and the Cochrane Methodology Register were searched (2011 and 2018). Eligible RCTs used administrative databases for identifying eligible participants or collecting outcomes. We evaluated reporting based on CONSORT-ROUTINE, which modified eight items from CONSORT 2010 and added five new items. RESULTS: Of 33 included trials (76% used administrative databases for outcomes, 3% for identifying participants, 21% both), most were conducted in the United States (55%), Canada (18%), or the United Kingdom (12%). Of eight items modified in the extension; six were adequately reported in a majority (>50%) of trials. For the CONSORT-ROUTINE modification portion of those items, three items were reported adequately in >50% of trials, two in <50%, two only applied to some trials, and one only had wording modifications and was not evaluated. For five new items, four that address use of routine data in trials were reported inadequately in most trials. CONCLUSION: How administrative data are used in trials is often sub-optimally reported. CONSORT-ROUTINE uptake may improve reporting.

Reporting transparency and completeness in Trials: Paper 2 - reporting of randomised trials using registries was often inadequate and hindered the interpretation of results.

OBJECTIVE: Registries are important data sources for randomized controlled trials (RCTs), but reporting of how they are used may be inadequate. The objective was to describe the current adequacy of reporting of RCTs using registries. STUDY DESIGN AND SETTING: We used a database of trials using registries from a scoping review supporting the development of the 2021 CONSORT extension for Trials Conducted Using Cohorts and Routinely Collected Data (CONSORT-ROUTINE). Reporting completeness of 13 CONSORT-ROUTINE items was assessed. RESULTS: We assessed reports of 47 RCTs that used a registry, published between 2011 and 2018. Of the 13 CONSORT-ROUTINE items, 6 were adequately reported in at least half of reports (2 in at least 80%). The 7 other items were related to routinely collected data source eligibility (32% adequate), data linkage (8% adequate), validation and completeness of data used for outcome assessment (8% adequate), validation and completeness of data used for participant recruitment (0% adequate), participant flow (9% adequate), registry funding (6% adequate) and interpretation of results in consideration of registry use (25% adequate). CONCLUSION: Reporting of trials using registries was often poor, particularly details on data linkage and quality. Better reporting is needed for appropriate interpretation of the results of these trials.

Reporting transparency and completeness in trials: Paper 4 - reporting of randomised controlled trials conducted using routinely collected electronic records - room for improvement.

OBJECTIVE: To describe characteristics of randomized controlled trials (RCTs) conducted using electronic health records (EHRs), including completeness and transparency of reporting assessed against the 2021 CONSORT Extension for RCTs Conducted Using Cohorts and Routinely Collected Data (CONSORT-ROUTINE) criteria. STUDY DESIGN: MEDLINE and Cochrane Methodology Register were searched for a sample of RCTs published from 2011-2018. Completeness of reporting was assessed in a random sample using a pre-defined coding form. RESULTS: Of the 183 RCT publications identified, 122 (67%) used EHRs to identify eligible participants, 139 (76%) used the EHR as part of the intervention and 137 (75%) to ascertain outcomes. When 60 publications were evaluated against the CONSORT 2010 item and the corresponding extension for the 8 modified items, four items were 'adequately reported' for most trials. Five new reporting items were identified for the CONSORT-ROUTINE extension; when evaluated, one was 'adequately reported', three were reported 'inadequately or not at all', the other 'partially'. There were, however, some encouraging signs with adequate and partial reporting of many important items, including descriptions of trial design, the consent process, outcome ascertainment and interpretation. CONCLUSION: Aspects of RCTs using EHRs are sub-optimally reported. Uptake of the CONSORT-ROUTINE Extension may improve reporting.