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Abstract Background Frailty is a complex condition that primary care providers (PCPs) are managing in increasing numbers, yet there is no clear guidance or training for frailty care. Objectives The present study examined eConsult questions PCPs asked specialists about patients with frailty, the specialists’ responses, and the impact of eConsult on the care of these patients. Design Cross-sectional observational study. Setting ChamplainBASE™ eConsult located in Eastern Ontario, Canada. Participants Sixty one eConsult cases closed by PCPs in 2019 that use the terms “frail” or “frailty” to describe patients 65 years of age or older. Measurements The Taxonomy of Generic Clinical Questions (TGCQ) was used to classify PCP questions and the International Classification for Primary Care 3 (ICPC-3) was used to classify the clinical content of each eConsult. The impact of eConsult on patient care was measured by PCP responses to a mandatory survey. Results PCPs most frequently directed their questions to cardiology (n = 7; 11%), gastroenterology (n = 7; 11%), and endocrinology (n = 6; 10%). Specialist answers most often pertained to medications (n = 63, 46%), recommendations for clinical investigation (n = 24, 17%), and diagnoses (n = 22, 16%). Specialist responses resulted in PCPs avoiding referral in 57% (n = 35) of cases whereas referrals were still required in 15% (n = 9) of cases. Specialists responded to eConsults in a median 1.11 days (IQR = 0.3–4.7), and 95% (n = 58) of cases received a response within 7 days. Specialists recorded a median of 15 min to respond (IQR = 10–20), with a median cost of $50.00 CAD (IQR = 33.33 – 66.66) per eConsult. Conclusions Through the analysis of questions and responses submitted to eConsult, this study provides novel information on PCP knowledge gaps and approaches to care for patients living with frailty. Furthermore, these analyses provide evidence that eConsult is a feasible and valuable tool for improving care for patients with frailty in primary care settings.

Introduction Rapid population ageing and associated health issues such as frailty are a growing public health concern. While early identification and management of frailty may limit adverse health outcomes, the complex presentations of frailty pose challenges for clinicians. Artificial intelligence (AI) has emerged as a potential solution to support the early identification and management of frailty. In order to provide a comprehensive overview of current evidence regarding the development and use of AI technologies including machine learning and deep learning for the identification and management of frailty, this protocol outlines a scoping review aiming to identify and present available information in this area. Specifically, this protocol describes a review that will focus on the clinical tools and frameworks used to assess frailty, the outcomes that have been evaluated and the involvement of knowledge users in the development, implementation and evaluation of AI methods and tools for frailty care in clinical settings.Methods and analysis This scoping review protocol details a systematic search of eight major academic databases, including Medline, Embase, PsycInfo, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Ageline, Web of Science, Scopus and Institute of Electrical and Electronics Engineers (IEEE) Xplore using the framework developed by Arksey and O’Malley and enhanced by Levac et al and the Joanna Briggs Institute. The search strategy has been designed in consultation with a librarian. Two independent reviewers will screen titles and abstracts, followed by full texts, for eligibility and then chart the data using a piloted data charting form. Results will be collated and presented through a narrative summary, tables and figures.Ethics and dissemination Since this study is based on publicly available information, ethics approval is not required. Findings will be communicated with healthcare providers, caregivers, patients and research and health programme funders through peer-reviewed publications, presentations and an infographic.Registration details OSF Registries (https://doi.org/10.17605/OSF.IO/T54G8).

Abstract Background Ontario Health Teams (OHTs) are an integrated care system introduced in Ontario, Canada in 2019 after the 14 Local Health Integrated Networks (LHINs) were dissolved. The objective of this study is to give an overview of the current state of the OHT model’s implementation, and what priority populations and transitions of care models were identified by OHTs. Methods This scan involved a structured search for each approved OHT of publicly available resources with three main sources: the full application submitted by the OHT, the OHT website, and a Google search with the name of the OHT. Results As of July 23, 2021, there were 42 approved OHTs and nine transitions of care programs were identified across nine OHTs. Of the approved OHTs, 38 had identified ten distinct priority populations, and 34 reported partnerships with organizations. Conclusions While the approved OHTs currently cover 86% of Ontario’s population, not all OHTs are at the same stage of activity. Several areas for improvement were identified, including public engagement, reporting, and accountability. Moreover, OHTs’ progress and outcomes should be measured in a standardized manner. These findings may be of interest to healthcare policy or decision-makers looking to implement similar integrated care systems and improve healthcare delivery in their jurisdictions.

BackgroundThe COVID-19 pandemic has highlighted the growing need for digital learning tools in postgraduate family medicine training. Family medicine departments must understand and recognize the use and effectiveness of digital tools in order to integrate them into curricula and develop effective learning tools that fill gaps and meet the learning needs of trainees. ObjectiveThis scoping review will aim to explore and organize the breadth of knowledge regarding digital learning tools in family medicine training. MethodsThis scoping review follows the 6 stages of the methodological framework outlined first by Arksey and O’Malley, then refined by Levac et al, including a search of published academic literature in 6 databases (MEDLINE, ERIC, Education Source, Embase, Scopus, and Web of Science) and gray literature. Following title and abstract and full text screening, characteristics and main findings of the included studies and resources will be tabulated and summarized. Thematic analysis and natural language processing (NLP) will be conducted in parallel using a 9-step approach to identify common themes and synthesize the literature. Additionally, NLP will be employed for bibliometric and scientometric analysis of the identified literature. ResultsThe search strategy has been developed and launched. As of October 2021, we have completed stages 1, 2, and 3 of the scoping review. We identified 132 studies for inclusion through the academic literature search and 127 relevant studies in the gray literature search. Further refinement of the eligibility criteria and data extraction has been ongoing since September 2021. ConclusionsIn this scoping review, we will identify and consolidate information and evidence related to the use and effectiveness of existing digital learning tools in postgraduate family medicine training. Our findings will improve the understanding of the current landscape of digital learning tools, which will be of great value to educators and trainees interested in using existing tools, innovators looking to design digital learning tools that meet current needs, and researchers involved in the study of digital tools. Trial RegistrationOSF Registries osf.io/wju4k; https://osf.io/wju4k International Registered Report Identifier (IRRID)DERR1-10.2196/34575