An Approach to Potentially Increasing Adoption of an Artificial Intelligence-Enabled Electronic Medical Record Encounter in Canadian Primary Care: Protocol for a User-Centered Design.

BACKGROUND: Primary care physicians are at the forefront of the clinical process that can lead to diagnosis, referral, and treatment. With electronic medical records (EMRs) being introduced and, over time, gaining acceptance by primary care users, they have now become a standard part of care. EMRs have the potential to be further optimized with the introduction of artificial intelligence (AI). There has yet to be a widespread exploration of the use of AI in primary health care and how clinicians envision AI use to encourage further uptake. OBJECTIVE: The primary objective of this research is to understand if the user-centered design approach, rooted in contextual design, can lead to an increased likelihood of adoption of an AI-enabled encounter module embedded in a primary care EMR. In this study, we use human factor models and the technology acceptance model to understand the results. METHODS: To accomplish this, a partnership has been established with an industry partner, TELUS Health, to use their EMR, the collaborative health record. The overall intention is to understand how to improve the user experience by using user-centered design to inform how AI should be embedded in an EMR encounter. Given this intention, a user-centered approach will be used to accomplish it. The approach of user-centered design requires qualitative interviewing to gain a clear understanding of users' approaches, intentions, and other key insights to inform the design process. A total of 5 phases have been designed for this study. RESULTS: As of March 2024, a total of 14 primary care clinician participants have been recruited and interviewed. First-cycle coding of all qualitative data results is being conducted to inform redesign considerations. CONCLUSIONS: Some limitations need to be acknowledged related to the approach of this study. There is a lack of market maturity of AI-enabled EMR encounters in primary care, requiring research to take place through scenario-based interviews. However, this participant group will still help inform design considerations for this tool. This study is targeted for completion in the late fall of 2024. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54365.

Online symptoms self-assessment during COVID-19 pandemic: an analysis of a COVID-19 portal responses from Canada.

COVID-19 case was first identified in Canada on January 25, 2020, on a Toronto resident who had travelled to Wuhan China, and not long after, the WHO declared the viral infection a pandemic. Ontario health West created an online self-assessment portal that allowed individuals in the health region and adjourning areas to report any COVID related symptoms. The purpose of this study was to evaluate the utility and usefulness of the Ontario Heath West online COVID-19 self-assessment portal. Record level data obtained from the Ontario Health West self-assessment portal was analyzed. Descriptive statistics using charts and graphs were used to characterize the distribution of responses to the portal. In-depth analysis using correlation, lead-lag analysis, and trend comparison with actual Government of Ontario COVID-19 cases for the region were also conducted. A total of 34,144 distinct responses were recorded on the portal between April 10 and July 29, 2020, with 1,250 (3.7%) responding positively to one of the emergency symptoms questions. Trend analysis showed a peak portal response in May 2020 with a smaller rise subsequently in July 2020, coinciding with the actual COVID-19 peak in the region. The five most reported symptoms on the portal were sore throat (17.2%), headache (12.9%), fatigue (12.3%), digestive problems (12.2%) and cough (9.1%). For four sub-regions, the trend of self-report on the portal positively lagged actual Public Health Ontario reported COVID-19 cases, while for one sub-region, the trend positively led the actual Public Health Ontario reported COVID-19 cases for the area. We found correlation between online COVID-19 self- assessment data and the confirmed COVID-19 cases in the Southwestern region of Ontario. Trends in the COVID-19 associated emergency symptoms reported on the portal also tracked confirmed COVID-19 cases in the community. Peak response to the portal coincided with the peak volume of confirmed cases in Ontario during the first wave of COVID-19 pandemic in Canada, suggesting some consistency between the experiences of portal users and patterns of COVID-19 illness in the community. The portal was a useful tool at the person-level because it provided guidance to individuals about how to access appropriate health services according to the symptoms that they reported and connected them with primary care, reducing unnecessary visit to health facilities for COVID-19 related care.