Solving Community SARS-CoV-2 Testing With Telehealth: Development and Implementation for Screening, Evaluation and Testing
Bibliographic record
Abstract
BACKGROUND: Telehealth has emerged as a crucial component of the SARS-CoV-2 pandemic emergency response. Simply stated, telehealth is a tool to provide health care from a distance. Jefferson Health has leveraged its acute care telehealth platform to screen, order testing, and manage patients with COVID-19-related concerns. OBJECTIVE: This study aims to describe the expansion and results of using a telehealth program to increase access to care while minimizing additional potential exposures during the early period of the COVID-19 pandemic. METHODS: Screening algorithms for patients with SARS-CoV-2-related complaints were created, and 150 new clinicians were trained within 72 hours to address increased patient demand. Simultaneously, Jefferson Health created mobile testing sites throughout eastern Pennsylvania and the southern New Jersey region. Visit volume, the number of SARS-CoV-2 tests ordered, and the number of positive tests were evaluated, and the volume was compared with preceding time periods. RESULTS: From March 8, 2020, to April 11, 2020, 4663 patients were screened using telehealth, representing a surge in visit volume. There were 1521 patients sent to mobile testing sites, and they received a telephone call from a centralized call center for results. Of the patients who were tested, nearly 20% (n=301) had a positive result. CONCLUSIONS: Our model demonstrates how using telehealth for a referral to central testing sites can increase access to community-based care, decrease clinician exposure, and minimize the demand for personal protective equipment. The scaling of this innovation may allow health care systems to focus on preparing for and delivering hospital-based care needs.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".