Acceptability and Feasibility of a Prototype Regional Disaster Teleconsultation System for COVID-19 Pandemic Response: Pilot Field Test
Bibliographic record
Abstract
Background: Disaster telehealth can be used to provide rapid access to remote specialty expertise and virtual surge capacity for overwhelmed local clinicians. The Regional Disaster Health Response System (RDHRS) is developing a disaster teleconsultation system for cross-jurisdictional care in the United States. In 2020, the Region 1 RDHRS provided Massachusetts hospitals access to disaster teleconsultation services with out-of-state critical care experts during the first wave of the COVID-19 pandemic response. Objective: We aimed to field-test (1) the acceptability and feasibility of using a prototype, web-based disaster teleconsultation platform with minimal-to-no user training and (2) the feasibility of deploying a national volunteer expert pool to access out-of-state expertise. Methods: This was a prospective, mixed methods, observational study. We recruited field clinicians from Massachusetts hospitals and out-of-state critical-care physicians as experts for a 2-week pilot (June 2020). Experts were trained to use a prototype platform, while field clinicians received a just-in-time tool. Field clinicians requested teleconsultations for hospitalized patients with COVID-19 (clinical call) or simulated patients (test call). We collected demographics, call performance data, and Telehealth Usability Questionnaire (TUQ) ratings to measure acceptability (primary outcome; total usability score ≥6 of 7) and feasibility (secondary outcome; interface, interaction quality, and reliability items), and interviewed participants. We report descriptive statistics and key themes using the Technology Acceptance Model framework. Results: Ten experts from 6 states and 17 field clinicians from 4 hospitals participated. All experts and 10 field clinicians completed postpilot questionnaires (74% response overall). Of these, 20% had previously used telemedicine in a disaster. In total, 50 test calls and no clinical calls were logged. Most (70%) made ≥1 call; 22% (95% CI 10%-34%) connected successfully. The median time to connect was 1.6 (IQR 3.2) minutes. Among field clinician respondents, 50% used smartphone devices, 40% hospital desktop computers, and 10% laptop computers to access RDHRS teleconsultation services. Calls failed due to platform routing errors (49%), hospital computers without cameras or microphones (10%), firewalls (8%), and expert notification failures (5%). The mean total usability score was 5.6 (SD 1.3). TUQ item scores were highest in usefulness (mean 6.0, SD 1.1) and ease-of-use (mean 6.0, SD 1.4), and lowest in reliability (mean 2.4, SD 1.4). Participants were comfortable using the platform. Those with difficulty identified discomfort with technology as the cause. All experts were willing to participate in a national expert registry and obtain emergency licensure, and most (80%) were willing to serve on a volunteer, unpaid basis. Conclusions: Clinicians found the prototype platform acceptable, but the workflow requires revision to reduce call failure and improve feasibility and reliability for future use with minimal-to-no training. Using familiar clinical workflows for emergency consultation and mobile devices with camera and microphone capabilities could improve call performance and reliability.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".