766. Telehealth Practices, Barriers, and Future Interest among Pediatric Infectious Disease Clinicians in the United States: Results from the 2019 Pediatric Infectious Diseases Society (PIDS) Telehealth Working Group Survey
Bibliographic record
Abstract
Abstract Background There is a paucity of access to pediatric infectious diseases (PID) physicians in the United States. To improve access, PID clinicians spend significant time providing nonreimbursed curbside consultations (CCs) to community providers. While there is increasing utilization of telehealth technologies to increase access to PID physicians, there is limited knowledge regarding adoption of these technologies and how they may be used to improve care and reimbursement. Methods The PIDS Telehealth Working Group developed a 33-question online survey to collect individual- and practice-level data on the burden of CCs, current telehealth practices and barriers, and interest in providing future telehealth services. It was emailed to the PIDS Listserv (n = 1,213) in April 2019. Results A total of 161 (13%) providers completed the survey (100% MD/DO), representing 37 states; most are university- (n = 100, 62%) and/or hospital- (n = 74, 46%) employed. Respondents’ practices provide a mean of 1–10 CCs/week to outside institutions (median 3–5 hours/week), with a median of 6–10% resulting in referrals. Outside nonreimbursed CCs are performed by phone/paging systems (n = 156, 98%), secure email (n = 66, 42%), text messaging (n = 46, 29%), and EMR-messaging (n = 38, 24%); they include a variety of services (Figure 1). Only 46 (29%) of individual respondents provide any type of reimbursed telehealth at their practices (Figure 2). Reimbursement mechanisms include fee-for-service (31%), Medicaid/Medicare (25%), private insurance (24%), and internal institutional (i.e., internal RVU) payments (16%). The majority of respondents were unaware of credentialing (n = 90, 64%) and liability coverage needs for telehealth (n = 68, 47%). Though most respondents (n = 81, 57%) were not satisfied with their current telehealth program and barriers were significant (Figure 3), the majority (n = 144, 95%) were interested in implementing a variety of reimbursable telehealth services and modalities (Figure 4). Conclusion PID survey respondents indicated a lack of knowledge on key aspects of telehealth and perceive significant barriers to implementing telehealth at their institutions. Nonetheless, there is a strong interest in participating in a variety of telehealth services to increase access to care, with appropriate institutional support. Disclosures All authors: No reported disclosures.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".