Artificial intelligence for family medicine research in Canada: current state and future directions: Report of the CFPC AI Working Group.

OBJECTIVE: To understand the current landscape of artificial intelligence (AI) for family medicine (FM) research in Canada, identify how the College of Family Physicians of Canada (CFPC) could support near-term positive progress in this field, and strengthen the community working in this field. COMPOSITION OF THE COMMITTEE: Members of a scientific planning committee provided guidance alongside members of a CFPC staff advisory committee, led by the CFPC-AMS TechForward Fellow and including CFPC, FM, and AI leaders. METHODS: This initiative included 2 projects. First, an environmental scan of published and gray literature on AI for FM produced between 2018 and 2022 was completed. Second, an invitational round table held in April 2022 brought together AI and FM experts and leaders to discuss priorities and to create a strategy for the future. REPORT: The environmental scan identified research related to 5 major domains of application in FM (preventive care and risk profiling, physician decision support, operational efficiencies, patient self-management, and population health). Although there had been little testing or evaluation of AI-based tools in practice settings, progress since previous reviews has been made in engaging stakeholders to identify key considerations about AI for FM and opportunities in the field. The round-table discussions further emphasized barriers to and facilitators of high-quality research; they also indicated that while there is immense potential for AI to benefit FM practice, the current research trajectory needs to change, and greater support is needed to achieve these expected benefits and to avoid harm. CONCLUSION: Ten candidate action items that the CFPC could adopt to support near-term positive progress in the field were identified, some of which an AI working group has begun pursuing. Candidate action items are roughly divided into avenues where the CFPC is well-suited to take a leadership role in tackling priority issues in AI for FM research and specific activities or initiatives the CFPC could complete. Strong FM leadership is needed to advance AI research that will contribute to positive transformation in FM.

Clinical learnings from a virtual primary care program monitoring mild to moderate COVID-19 patients at home.

BACKGROUND: Virtual consults have replaced in-person visits for many home-isolated patients with COVID-19 disease. OBJECTIVES: To describe the natural history, clinical management and outcomes of community-dwelling patients with COVID-19, who received support from a family medicine-led, virtual CovidCare@Home program in Toronto, Ontario, Canada. METHODS: Observational, descriptive study conducted by retrospective chart review of 98 patients enrolled during the first 5 weeks of program implementation (8 April-11 May 2020); 73 patients with laboratory-confirmed COVID-19, with symptom onset ≤ 14 days before initial consult were included for analysis. Patients were classified as mild, moderate or severe based on WHO Criteria. RESULTS: All patients in the program experienced mild (88%) or moderate (12.3%) disease. No patients were hospitalized or died. Patients were mainly female (70%); with mean age of 43.3 years. Most patients (82.2%) worked in higher risk, healthcare settings. Almost 40% had no medical co-morbidities. Common symptoms were cough (65.8%), fatigue (60.3%), headache (42.5%) and myalgia (39.7%), followed by fever (32.9%), sore throat (21.9%), nasal congestion (21.9%) and rhinorrhea (20.5%). Headache (51%) and anosmia (45.1%) were common among females; fever and breathlessness among males (40.9%). Nine patients (12.3%) experienced worsening of symptoms (mainly respiratory) or exacerbation of co-morbidities, which required care outside the virtual service. CONCLUSION: Patients with mild to moderate COVID-19 disease can be managed safely and effectively in a family medicine-led virtual program. Some sex differences in symptoms were observed. Future work should focus on long-term follow up in view of the existence of so-called 'long-haulers'.

Computer-Based Driving in Dementia Decision Tool With Mail Support: Cluster Randomized Controlled Trial.

BACKGROUND: Physicians often find significant challenges in assessing automobile driving in persons with mild cognitive impairment and mild dementia and deciding when to report to transportation administrators. Care must be taken to balance the safety of patients and other road users with potential negative effects of issuing such reports. OBJECTIVE: The aim of this study was to assess whether a computer-based Driving in Dementia Decision Tool (DD-DT) increased appropriate reporting of patients with mild dementia or mild cognitive impairment to transportation administrators. METHODS: The study used a parallel-group cluster nonblinded randomized controlled trial design to test a multifaceted knowledge translation intervention. The intervention included a computer-based decision support system activated by the physician-user, which provides a recommendation about whether to report patients with mild dementia or mild cognitive impairment to transportation administrators, based on an algorithm derived from earlier work. The intervention also included a mailed educational package and Web-based specialized reporting forms. Specialists and family physicians with expertise in dementia or care of the elderly were stratified by sex and randomized to either use the DD-DT or a control version of the tool that required identical data input as the intervention group, but instead generated a generic reminder about the reporting legislation in Ontario, Canada. The trial ran from September 9, 2014 to January 29, 2016, and the primary outcome was the number of reports made to the transportation administrators concordant with the algorithm. RESULTS: A total of 69 participating physicians were randomized, and 36 of these used the DD-DT; 20 of the 35 randomized to the intervention group used DD-DT with 114 patients, and 16 of the 34 randomized to the control group used it with 103 patients. The proportion of all assessed patients reported to the transportation administrators concordant with recommendation did not differ between the intervention and the control groups (50% vs 49%; Z=-0.19, P=.85). Two variables predicted algorithm-based reporting-caregiver concern (odds ratio [OR]=5.8, 95% CI 2.5-13.6, P<.001) and abnormal clock drawing (OR 6.1, 95% CI 3.1-11.8, P<.001). CONCLUSIONS: On the basis of this quantitative analysis, in-office abnormal clock drawing and expressions of concern about driving from caregivers substantially influenced physicians to report patients with mild dementia or mild cognitive impairment to transportation administrators, but the DD-DT tool itself did not increase such reports among these expert physicians. TRIAL REGISTRATION: ClinicalTrials.gov NCT02036099; https://clinicaltrials.gov/ct2/show/NCT02036099 (Archived by WebCite at http://www.webcitation.org/6zGMF1ky8).

Development of a decision-making tool for reporting drivers with mild dementia and mild cognitive impairment to transportation administrators.

BACKGROUND: Driving in persons with dementia poses risks that must be counterbalanced with the importance of the care for autonomy and mobility. Physicians often find substantial challenges in the assessment and reporting of driving safety for persons with dementia. This paper describes a driving in dementia decision tool (DD-DT) developed to aid physicians in deciding when to report older drivers with either mild dementia or mild cognitive impairment to local transportation administrators. METHODS: A multi-faceted, computerized decision support tool was developed, using a systematic literature and guideline review, expert opinion from an earlier Delphi study, as well as qualitative interviews and focus groups with physicians, caregivers of former drivers with dementia, and transportation administrators. The tool integrates inputs from the physician-user about the patient's clinical and driving history as well as cognitive findings, and it produces a recommendation for reporting to transportation administrators. This recommendation is translated into a customized reporting form for the transportation authority, if applicable, and additional resources are provided for the patient and caregiver. CONCLUSIONS: An innovative approach was needed to develop the DD-DT. The literature and guideline review confirmed the algorithm derived from the earlier Delphi study, and barriers identified in the qualitative research were incorporated into the design of the tool.