Telemedicine visits requiring follow-up in-person visits at an urban academic family medicine centre.

BACKGROUND: With the onset of the COVID-19 pandemic, telemedicine was rapidly implemented in care settings globally. To understand what factors affect the successful completion of telemedicine visits in our urban, academic family medicine clinic setting, we analysed telemedicine visits carried out during the pandemic. METHODS: We conducted a retrospective chart review of telemedicine visits from 2 clinical units within a family medicine centre. To investigate the association between incomplete visits and various factors (age, gender, presenting complaints, physician level of training [resident or staff] and patient-physician relational continuity), we performed a multivariable logistic regression on data from August 2020, February 2021, and May 2021. An incomplete visit is one that requires a follow-up in-person visit with a physician within 3 days. RESULTS: Of the 2,138 telemedicine patient visits we investigated, 9.6% were incomplete. Patients presenting with lumps and bumps (OR: 3.84, 95% CI: 1.44, 10.5), as well as those seen by resident physicians (OR: 1.77, 95% CI: 1.22, 2.56) had increased odds of incomplete visits. Telemedicine visits at the family medicine clinic (Site A) with registered patients had lower odds of incomplete visits (OR: 0.24, 95% CI: 0.15, 0.39) than those at the community clinic (Site B), which provides urgent/episodic care with no associated relational continuity between patients and physicians. CONCLUSION: In our urban clinical setting, only a small minority of telemedicine visits required an in-person follow-up visit. This information may be useful in guiding approaches to triaging patients to telemedicine or standard in-person care.

With the onset of the COVID-19 pandemic, telemedicine was rapidly implemented in care settings globally. To understand what factors affect the successful completion of telemedicine visits in our urban, academic family medicine clinic, we analysed telemedicine visits carried out during the pandemic. On the basis of patient charts, we investigated the association between incomplete visits (telemedicine visits requiring in-person follow-up within 3 days) and various factors (age, gender, presenting complaints, whether the treating physician was a resident or staff doctor, and whether the patient and physician had a prior clinical relationship). Patients presenting with lumps and bumps and those seen by resident physicians had higher odds of being asked to come in-person for further evaluation. Overall, though, these required in-person follow-ups were uncommon: less than 10% of telemedicine visits resulted in the patient physically coming to the clinic within 3 days. The findings of our study could help guide patients to appropriate care services.

Telemedicine perceptions and experiences of socially vulnerable households during the early stages of the COVID-19 pandemic: a qualitative study.

BACKGROUND: Early in the COVID-19 pandemic, efforts to decrease risk of viral transmission triggered an abrupt shift from ambulatory health care delivery toward telemedicine. In this study, we explore the perceptions and experiences of telemedicine among socially vulnerable households and suggest strategies to increase equity in telemedicine access. METHODS: Conducted between August 2020 and February 2021, this exploratory qualitative study involved in-depth interviews with members of socially vulnerable households needing health care. Participants were recruited from a food bank and primary care practice in Montréal. Digitally recorded telephone interviews focused on experiences and perceptions related to telemedicine access and use. In our thematic analysis, we employed the framework method to facilitate comparison, and the identification of patterns and themes. RESULTS: Twenty-nine participants were interviewed, 48% of whom presented as women. Almost all sought health care in the early stages of the pandemic, 69% of which was received via telemedicine. Four themes emerged from the analysis: delays in seeking health care owing to competing priorities and perceptions that COVID-19-related health care took precedence; challenges with appointment booking and logistics given complex online systems, administrative inefficiencies, long wait times and missed calls; issues around quality and continuity of care; and conditional acceptance of telemedicine for certain health problems, and in exceptional circumstances. INTERPRETATION: Early in the pandemic, participants report telemedicine delivery did not accommodate the diverse needs and capacities of socially vulnerable populations. Patient education, logistical support and care delivery by a trusted provider are suggested solutions, in addition to policies supporting digital equity and quality standards to promote telemedicine access and appropriate use.

Explainable Machine Learning Model to Predict COVID-19 Severity Among Older Adults in the Province of Quebec.

Context: Patients over the age of 65 years are more likely to experience higher severity and mortality rates than other populations from COVID-19. Clinicians need assistance in supporting their decisions regarding the management of these patients. Artificial Intelligence (AI) can help with this regard. However, the lack of explainability-defined as "the ability to understand and evaluate the internal mechanism of the algorithm/computational process in human terms"-of AI is one of the major challenges to its application in health care. We know little about application of explainable AI (XAI) in health care. Objective: In this study, we aimed to evaluate the feasibility of the development of explainable machine learning models to predict COVID-19 severity among older adults. Design: Quantitative machine learning methods. Setting: Long-term care facilities within the province of Quebec. Participants: Patients 65 years and older presented to the hospitals who had a positive polymerase chain reaction test for COVID-19. Intervention: We used XAI-specific methods (e.g., EBM), machine learning methods (i.e., random forest, deep forest, and XGBoost), as well as explainable approaches such as LIME, SHAP, PIMP, and anchor with the mentioned machine learning methods. Outcome measures: Classification accuracy and area under the receiver operating characteristic curve (AUC). Results: The age distribution of the patients (n=986, 54.6% male) was 84.5□19.5 years. The best-performing models (and their performance) were as follows. Deep forest using XAI agnostic methods LIME (97.36% AUC, 91.65 ACC), Anchor (97.36% AUC, 91.65 ACC), and PIMP (96.93% AUC, 91.65 ACC). We found alignment with the identified reasoning of our models' predictions and clinical studies' findings-about the correlation of different variables such as diabetes and dementia, and the severity of COVID-19 in this population. Conclusions: The use of explainable machine learning models, to predict the severity of COVID-19 among older adults is feasible. We obtained a high-performance level as well as explainability in the prediction of COVID-19 severity in this population. Further studies are required to integrate these models into a decision support system to facilitate the management of diseases such as COVID-19 for (primary) health care providers and evaluate their usability among them.

Telehealth in Primary Healthcare: A Portrait of its Rapid Implementation during the COVID-19 Pandemic.

OBJECTIVE: This study documents the adoption of telehealth by various types of primary healthcare (PHC) providers working in teaching PHC clinics in Quebec during the COVID-19 pandemic. It also identifies the perceived advantages and disadvantages of telehealth. METHOD: A cross-sectional study was conducted between May and August 2020. The e-survey was completed by 48/50 teaching primary care clinics representing 603/1,357 (44%) PHC providers. RESULTS: Telephone use increased the most, becoming the principal virtual modality of consultation, during the pandemic. Video consultations increased, with variations by type of PHC provider: between 2% and 16% reported using it "sometimes." The main perceived advantages of telehealth were minimizing the patient's need to travel, improved efficiency and reduction in infection transmission risk. The main disadvantages were the lack of physical exam and difficulties connecting with some patients. CONCLUSION: The variation in telehealth adoption by type of PHC provider may inform strategies to maximize the potential of telehealth and help create guidelines for its use in more normal times.