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Introduction We set out to assess the impact of Choosing Wisely Canada recommendations (2014) on reducing unnecessary health investigations and interventions in primary care across Southwestern Ontario. Methods We used the Deliver Primary Healthcare Information (DELPHI) database, which stores deidentified electronic medical records (EMR) of nearly 65,000 primary care patients across Southwestern Ontario. When conducting research using EMR data, data provenance (i.e., how the data came to be) should first be established. We first considered DELPHI data provenance in relation to longitudinal analyses, flagging a change in EMR software that occurred during 2012 and 2013. We attempted to link records between EMR databases produced by different software using probabilistic linkage and inspected 10 years of data in the DELPHI database (2009 to 2019) for data quality issues, including comparability over time. Results We encountered several issues resulting from this change in EMR software. These included limited linkage of records between software without a common identifier; data migration issues that distorted procedure dates; and unusual changes in laboratory test and medication prescription volumes. Conclusion This study reinforces the necessity of assessing data provenance and quality for new research projects. By understanding data provenance, we can anticipate related data quality issues such as changes in EMR data over time-which represent a growing concern as longitudinal data analyses increase in feasibility and popularity.

Abstract Background The onset of the COVID-19 pandemic necessitated a rapid shift in primary health care from predominantly in-person to high volumes of virtual care. The pandemic afforded the opportunity to conduct a deep regional examination of virtual care by family physicians in London and Middlesex County, Ontario, Canada that would inform the foundation for virtual care in our region post-pandemic. Objectives: (1) to determine volumes of in-person and virtual family physicians visits and characteristics of the family physicians and patients using them during the early COVID-19 pandemic; (2) to determine how virtual visit volumes changed over the pandemic, compared to in-person; and (3) to explore family physicians’ experience in virtual visit adoption and implementation. Methods We conducted a concurrent mixed-methods study of family physicians from March to October 2020. The quantitative component examined mean weekly number of total, in-person and virtual visits using health administrative data. Differences in outcomes according to physician and practice characteristics for pandemic periods were compared to pre-pandemic. The qualitative study employed Constructivist Grounded Theory, conducting semi-structured family physicians interviews; analyzing data iteratively using constant comparative analysis. We mapped themes from the qualitative analysis to quantitative findings. Results Initial volumes of patients decreased, driven by fewer in-person visits. Virtual visit volumes increased dramatically; family physicians described using telephone almost entirely. Rural family physicians reported video connectivity issues. By early second wave, total family physician visit volume returned to pre-pandemic volumes. In-person visits increased substantially; family physicians reported this happened because previously scarce personal protective equipment became available. Patients seen during the pandemic were older, sicker, and more materially deprived. Conclusion These results can inform the future of virtual family physician care including the importance of continued virtual care compensation, the need for equitable family physician payment models, and the need to attend to equity for vulnerable patients. Given the move to virtual care was primarily a move to telephone care, the modality of care delivery that is acceptable to both family physicians and their patients must be considered.

Abstract 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.

Abstract Background The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs. Methods Using an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products. Results A set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight). Conclusions This paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.

Background The use of portals might be expected to rise; however, adoption has been slow. Development of portals has occurred with limited patient involvement. This paper fills a need for literature concerning perspectives regarding the value of portals, how best to organize and provide portals, and critically how to seek patient involvement in implementation. Objective The objective was to explore the feelings, ideas, and expectations of patients and primary care providers concerning the implementation and use of patient portals. Methods The study employed a descriptive qualitative design interviewing seven patients and four providers from an interdisciplinary primary health care clinic in Ontario, Canada. Patients were older with at least one chronic condition. Interviews were analysed independently by three coders who then met to synthesize the findings. Results There was limited experience of portals and substantial convergence between patients and providers regarding concerns and potential benefits with an overall positive view. Four themes emerged: 1) the Context in which patient portal use takes place; 2) the Necessary conditions for use of a patient portal; 3) the Implementation of a patient portal; and 4) the Use of a patient portal for care. Conclusion Findings highlight that it is not sufficient to engage patients in the use of a portal; it is critical that patients be engaged in the early stages of implementation. With many health and fitness electronic tools available (e.g. Fitbit©), this study remind us that tools are not enough. Patient engagement requires patient-centred partnerships between patients and health care providers.

Background Electronic medical records (EMRs) have the potential to improve the provision of preventive care by allowing general practitioners (GPs) to track and recall eligible patients and record testing for feedback on their service provision. Objective This study evaluates the effect of an educational intervention and feedback tool designed to teach GPs how to use their EMRs to improve their provision of preventive care. Methods A randomised controlled trial comparing rates of mammography, Papanicolaou tests, faecal occult blood tests and albumin creatinine ratios one-year pre- and post-intervention was conducted. Nine primary care practices (PCPs) representing over 30 000 patients were paired by practice size and experience of GPs, and randomly allocated to intervention or control groups. Physicians at the four intervention practices received a two-hour feedback session on their current level of preventive care and training to generate eligible patient lists for preventive services from their EMR database. Results One-year post-intervention results provided no evidence of a difference. The intervention was not a significant predictor of the one-year post-intervention test rates for any of the four tests. On average, the intervention practices increased postintervention test rates on all tests by 16.8%, and control practices increased by 22.3%. Conclusion The non-significant results may be due to a variety of reasons, including the level of intensity of the educational intervention, the cointervention of a government programme which provided incentives to GPs meeting specific targets for preventive care testing or the level of recording of tests performed in the EMR.