P-2367. Qualitative Analysis of Barriers to Outpatient Antiviral Treatment for COVID-19 and Influenza Patients Observed by Infectious Disease Specialists in North America, 2024
Bibliographic record
Abstract
Abstract Background Antiviral medications for COVID-19 and influenza can mitigate disease severity in high-risk outpatients if taken early in the course of illness yet are underutilized. We sought to understand barriers to providers prescribing these medications. Table Thematic summary from infectious disease specialists outlining barriers to outpatient antiviral treatment for COVID-19 and influenza patients, United States, 2024 Methods We conducted an on-line survey regarding knowledge, attitudes, and practices of prescribing antiviral treatment for outpatients with COVID-19 and influenza. Respondents were asked about perceived barriers among providers treating COVID-19 and influenza outpatients in their institutions. Questions were structured using a Likert scale and were analyzed using a thematic analysis approach in Microsoft Excel. Results Of 1,898 infectious disease specialists across the United States and Canada who received the survey between 1/10/24 to 2/5/24, 565 (30%) responded, of whom 93% were infectious disease physicians and 7% were healthcare professionals. Surveyed physicians worked in university (45%), non-university teaching (24%), community (20%), city/county (4%), outpatient (0.4%), and veteran affairs hospital settings (6%) and 144 (25%) provided free text responses. The primary barrier to prescribing antivirals was provider skepticism (47%), due to patient symptoms deemed too mild for treatment and needing more evidence about effectiveness. This was followed by perceptions of limited accessibility (31%) related to high cost, limited hospital access, and difficulty administering some medications. Pharmacologic limitations (18%) were concerns about drug interactions, side effects, and incomplete medical history. Other barriers were timeliness of treatment within a short therapeutic window (15%) and patient skepticism (13%). Additional free text responses described successful hospital protocols and suggestions for encouraging antiviral prescribing. Conclusion These themes demonstrate a need for better education of providers about antiviral risks and benefits as well as improved access and coverage of life-saving medications. Infectious disease specialists provided useful insights which can help to shape clinical recommendations, future research for antiviral therapeutics, and public health messaging for improved patient care. Disclosures Philip M. Polgreen, MD, Eli Lily: Advisor/Consultant|Pfizer: Grant/Research Support
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".