Telemedicine in allergy/immunology in the era of COVID-19: a Canadian perspective
Bibliographic record
Abstract
BACKGROUND: In the era of COVID-19, utilization of telemedicine has dramatically increased. In addition to reduced travel times, patient expenses, and work or school days missed, telemedicine allows clinicians to provide continued care while minimizing face-to-face interactions, maintaining social distancing, and limiting potential COVID-19 exposures. Clinical Immunology and Allergy (CIA), like many specialties, has adapted to incorporate telemedicine into practice. Previous studies have demonstrated similar patient satisfaction between virtual and in-person visits. However, evidence from fully publicly funded health care systems such as Canada has been limited. METHODS: We performed a quality improvement (QI) initiative to assess the feasibility of telemedicine. Between 1 March and 30 September 2020, patient encounters of two academic allergists at a single institution in London, Ontario, Canada were analyzed. Assessments were categorized into in-person or telemedicine appointments. A random sample of patients assessed virtually completed a voluntary patient satisfaction survey. Qualitative analysis was performed on survey comments. RESULTS: In total 3342 patients were seen. The majority were adults (n = 2162, or 64.7%) and female (n = 1872, or 56%). 1543 (46.2%) assessments were virtual and 1799 (53.8%) assessments were in-person. 67 of 100 random patient surveys sent to those in the virtual assessment group were completed. 89.6% (n = 60) agreed or strongly agreed when asked if they were satisfied with their telemedicine visit. 64.2% (n = 43) felt they received the same level of care compared to in-person assessments and 91% (n = 61) stated they would attend another virtual appointment. 95.4% (n = 62) of patients reported saving time with virtual assessment, the majority (n = 42, 62.7%) estimating between 1-4 h saved. Reported shortcomings included technical difficulties, "feeling rushed", and missing in-person interactions. CONCLUSIONS: Our quality improvement initiative demonstrated high patient satisfaction and time savings with virtual assessment in a publicly funded health care system. Studies suggest that CIA may be uniquely situated to benefit from permanent integration of virtual care into regular practice for both new and follow-up appointments. We anticipate continued increased utilization of telemedicine, signifying a lasting beneficial change in the delivery of healthcare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".