Family physician virtual care during COVID-19 in London-Middlesex, Ontario, Canada: a mixed methods exploration.

Context: On March 14, 2020, the Ontario, Canada health insurance plan approved COVID-19 physician virtual billing codes; family physicians (FPs) rapidly adopted a new model of care. Virtual care may remain post-pandemic; however, its future should be informed by evidence that considers access and continuity. Objective: 1) to determine FP virtual visit volumes and patient characteristics and 2) to explore FPs' perspectives on virtual visit adoption and implementation. Study Design: Mixed methods: Secondary analysis of health administrative (HA) data and semi-structured qualitative interviews with FPs. Setting or Dataset: London and Middlesex County, Ontario, Canada. HA data through ICES, Ontario entity holding data. Population studied: FPs and their patients. Outcome Measures: Volumes of FP in-person and virtual visits during early pandemic; characteristics of patients receiving care; FPs' perspectives on adopting and delivering virtual care. Results: Overall visit volume dropped by 36% during first wave, recovered to pre-pandemic levels by October 2020. Sharp in-person visit drop of 73% and virtual visit uptake from 0.08% of total visits to 57% within two weeks of March 2020. FPs described this initial drop in volume as patients not seeking care and practices lacking PPE. The move to virtual care was largely to telephone visits. Patient characteristics compared to pre-pandemic, the proportion seeking care were older (46 vs 50 years), more vulnerable (38% vs 41%), and more multimorbidity (33% vs 41%). This was consistent with FP reports that healthier patients stayed away, routine care deferred, sicker patients needed to be seen. FPs believed most vulnerable patients had access to care but cautioned highly vulnerable such as those homeless did not have cell phone access or a safe place to receive calls. Rural FPs reported access issues because of lack of high-speed internet. FPs attributed success of virtual care to the continuity in relationships they had with patients that were established in person pre-pandemic. Conclusions: FPs moved rapidly to virtual care. FP offices remained open despite PPE concerns but overall volumes dropped initially. Vulnerable and sicker patients received care but FPs expressed concern for highly vulnerable and rural residents. FPs believed they could offer patient-centred care over the phone but indicated the importance of maintaining in-person care to build relationships.

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.

Abdominal aortic aneurysm screening in an academic family practice: Short-term impact of guideline changes.

OBJECTIVE: To investigate abdominal aortic aneurysm (AAA) screening rates in the 6 months before and after the introduction of updated Canadian Task Force on Preventive Health Care (CTFPHC) guidelines to determine effects on practice patterns, as well as to determine whether certain patient characteristics impact AAA screening rates. DESIGN: Retrospective chart review. SETTING: Academic family health centre in London, Ont. PARTICIPANTS: Male patients between the ages of 65 and 80. MAIN OUTCOME MEASURES: Screening rates for AAA before and after the guideline update were compared using the normal approximation of the binomial distribution. Analysis of demographic characteristic effects on screening rates was completed with the Fisher exact test. Number of visits to the clinic with a primary care provider within the study period and imaging type were collected. RESULTS: Of the 266 patients included in the study, 160 patients were eligible for screening at the start of the study period, 6 months before publication of the CTFPHC AAA guideline. Individuals eligible for screening visited the clinic an average (SD) of 2.44 (1.82) times in the 6 months before and 2.66 (1.99) times in the 6 months after. Overall, 69 individuals had AAA screening completed and 9 had a discussion of AAA screening without any imaging, for a total uptake rate of 88.5% for those who had screening recommended. The overall imaging rate was 48.9%. There was no statistically significant difference in screening rates between the time periods (P=.337) among those eligible for screening. For demographic characteristics for risk stratification, 7 individuals had a documented family history, of whom 5 had imaging of their abdominal aorta performed, plus 1 additional individual who had screening recommended but not completed. This was not statistically significant relative to the total population (P=.0598). Positive smoking status (active or ex-smoker) was more common, with 135 individuals having a relevant smoking history. Approximately half of these current and former smokers (68 individuals [50.4%]) had any sort of abdominal aortic imaging performed or recommended, which was not statistically significantly different compared with non-smokers (62 of 126 imaging performed or recommended, 49.2%; P=.9016). CONCLUSION: Screening practices did not change appreciably with the introduction of the CTFPHC AAA screening guidelines. Further research is needed to improve AAA screening rates. It is worth exploring electronic medical record-based reminders, nursing staff involvement in screening, screening programs via public health, and point-of-care ultrasound screening in a primary care setting.

A basic model for assessing primary health care electronic medical record data quality.

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.

Antibiotic Prescribing for Nonbacterial Acute Upper Respiratory Infections in Elderly Persons.

BACKGROUND: Reducing inappropriate antibiotic prescribing for acute upper respiratory tract infections (AURIs) requires a better understanding of the factors associated with this practice. OBJECTIVE: To determine the prevalence of antibiotic prescribing for nonbacterial AURIs and whether prescribing rates varied by physician characteristics. DESIGN: Retrospective analysis of linked administrative health care data. SETTING: Primary care physician practices in Ontario, Canada (January-December 2012). PATIENTS: Patients aged 66 years or older with nonbacterial AURIs. Patients with cancer or immunosuppressive conditions and residents of long-term care homes were excluded. MEASUREMENTS: Antibiotic prescriptions for physician-diagnosed AURIs. A multivariable logistic regression model with generalized estimating equations was used to examine whether prescribing rates varied by physician characteristics, accounting for clustering of patients among physicians and adjusting for patient-level covariates. RESULTS: The cohort included 8990 primary care physicians and 185 014 patients who presented with a nonbacterial AURI, including the common cold (53.4%), acute bronchitis (31.3%), acute sinusitis (13.6%), or acute laryngitis (1.6%). Forty-six percent of patients received an antibiotic prescription; most prescriptions were for broad-spectrum agents (69.9% [95% CI, 69.6% to 70.2%]). Patients were more likely to receive prescriptions from mid- and late-career physicians than early-career physicians (rate difference, 5.1 percentage points [CI, 3.9 to 6.4 percentage points] and 4.6 percentage points [CI, 3.3 to 5.8 percentage points], respectively), from physicians trained outside of Canada or the United States (3.6 percentage points [CI, 2.5 to 4.6 percentage points]), and from physicians who saw 25 to 44 patients per day or 45 or more patients per day than those who saw fewer than 25 patients per day (3.1 percentage points [CI, 2.1 to 4.0 percentage points] and 4.1 percentage points [CI, 2.7 to 5.5 percentage points], respectively). LIMITATION: Physician rationale for prescribing was unknown. CONCLUSION: In this low-risk elderly cohort, 46% of patients with a nonbacterial AURI were prescribed antibiotics. Patients were more likely to receive prescriptions from mid- or late-career physicians with high patient volumes and from physicians who were trained outside of Canada or the United States. PRIMARY FUNDING SOURCE: Ontario Ministry of Health and Long-term Care, Academic Medical Organization of Southwestern Ontario, Schulich School of Medicine and Dentistry, Western University, and Lawson Health Research Institute.

Understanding data provenance when using electronic medical records for research: Lessons learned from the Deliver Primary Healthcare Information (DELPHI) database.

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.

Virtual family physician care during COVID-19: a mixed methods study using health administrative data and qualitative interviews.

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.

Feedback and training tool to improve provision of preventive care by physicians using EMRs: a randomised control trial.

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 postintervention 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.

Using your electronic medical record for research: a primer for avoiding pitfalls.

In Canada, use of electronic medical records (EMRs) among primary health care (PHC) providers is relatively low. However, it appears that EMRs will eventually become more ubiquitous in PHC. This represents an important development in the use of health care information technology as well as a potential new source of PHC data for research. However, care in the use of EMR data is required. Four years ago, researchers at the Centre for Studies in Family Medicine, The University of Western Ontario created an EMR-based research project, called Deliver Primary Health Care Information. Implementing this project led us to two conclusions about using PHC EMR data for research: first, additional time is required for providers to undertake EMR training and to standardize the way data are entered into the EMR and second, EMRs are designed for clinical care, not research. Based on these experiences, we offer our thoughts about how EMRs may, nonetheless, be used for research. Family physician researchers who intend to use EMR data to answer timely questions relevant to practice should evaluate the possible impact of the four questions raised by this paper: (i) why are EMR data different?; (ii) how do you extract data from an EMR?; (iii) where are the data stored? and (iv) what is the data quality? In addition, consideration needs to be given to the complexity of the research question since this can have an impact on how easily issues of using EMR data for research can be overcome.

Quality of congestive heart failure care: assessing measurement of care using electronic medical records.

OBJECTIVE: To study the feasibility of using electronic medical record (EMR) data from the Deliver Primary Healthcare Information (DELPHI) database to measure quality of care for patients with congestive heart failure (CHF) in primary care and to determine the percentage of patients with CHF receiving the recommended care. DESIGN: Items listed on the Ontario Ministry of Health and Long-Term Care Heart Failure Patient Care Flow Sheet (CHF flow sheet) were assessed and measured using EMRs of patients diagnosed with CHF between October 1, 2005, and September 30, 2008. SETTING: Ten primary health care practices in southwestern Ontario. PARTICIPANTS: Four hundred eighty-eight patients who were considered to have CHF because at least 1 of the following was indicated in their EMRs: an International Classification of Diseases billing code for CHF (category 428), an International Classification of Primary Care diagnosis code for heart failure (ie, K77), or "CHF" reported on the problem list. MAIN OUTCOME MEASURES: Number of CHF flow sheet items that were measurable using EMR data from the DELPHI database. Percentage of patients with CHF receiving required quality-of-care items since the date of diagnosis. RESULTS: The DELPHI database contained information on 60 (65.9%) of the 91 items identified using the CHF flow sheet. The recommended tests and procedures were recorded infrequently: 55.5% of patients with CHF had chest radiographs; 32.6% had electrocardiograms; 32.2% had echocardiograms; 30.5% were prescribed angiotensin-converting enzyme inhibitors; 20.9% were prescribed ß-blockers; and 15.8% were prescribed angiotensin II receptor blockers. CONCLUSION: Low frequencies of recommended care items for patients with CHF were recorded in the EMR. Physicians explained that CHF care was documented in areas of the EMR that contained patient identifiers, such as the encounter notes, and was therefore not part of the DELPHI database. Extractable information from the EMR does not provide a complete picture of the quality of care provided to patients with CHF.

Implementing and Using a Patient Portal: A qualitative exploration of patient and provider perspectives on engaging patients.

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.ObjectiveThe objective was to explore the feelings, ideas, and expectations of patients and primary care providers concerning the implementation and use of patient portals. METHOD: 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. CONCLUSIONS: 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.