604. Impact of COVID-19 Pandemic on Telehealth Practices in Pediatric Infectious Diseases
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic has led to changes in clinical practice, including a significant increase in the use of telehealth (TH). We sought to assess the impact of the pandemic on the use and perceptions of TH by pediatric infectious diseases (PID) clinicians. Figure 1. Modalities Figure 2. Comfort Methods The PIDS* Telehealth Working Group developed a 26-question online survey to assess telehealth practices among PID clinicians. The survey was available via Survey Monkey® from 12/6/2020-2/26/2021 to members of PIDS, PICNIC*, AAMI and AAP*. Clinicians in active practice in North America were included in the analysis. Figure 3. Platforms Figure 4. Barriers Results The response rate was 10% (n=253) of 2,550 PID clinicians. Physicians accounted for 98.4% of the cohort. The remaining 1.6% were allied health professionals. 81 survey respondents (32%) were in 4 US states (CA, TX, OH and NY) and the province of Quebec. 62.8% of respondents were women, 37% of respondents were 36-45 years old, with 42.7% devoting about 50-99% of their time to direct patient care. TH usage increased during the pandemic with the most gain in provider-patient communications with 65.6% increase for synchronous and 22.1% for asynchronous TH (Figure 1). Gains in provider-provider TH were less than 20%. Respondents reported a 6-fold gain in comfort with TH usage versus pre-pandemic level (Figure 2). Most respondents report being satisfied with their current platform and modality. Once the COVID-19 waivers expire, 70% of respondents plan to continue using TH. The most common TH modality used was an EMR-integrated TH platform (Figure 3). The main perceived barriers to TH adoption were lack of complete physical examination (73.7%), dealing with new technology (21.5%), and insufficient reimbursement (20.8%) (Figure 4). Conclusion The COVID-19 pandemic has resulted in a significant increase in the use of TH by PID specialists versus pre-pandemic usage. Respondents gained comfort with use of different telehealth modalities during the pandemic. This data can help clinicians and organizations in planning and resource allocation for telehealth programs in a post-pandemic environment. Disclosures All Authors: No reported disclosures
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".