On the Front (Phone) Lines: Results of a COVID-19 Hotline in Northeast Ohio
Notice bibliographique
Résumé
ABSTRACT Importance: Severe acute respiratory syndrome coronavirus (SARS-CoV-2) and the associated coronavirus disease of 2019 (COVID-19) have presented immense challenges for health care systems. Many regions have struggled to adapt to disruptions to health care practice and employ systems that effectively manage the demand for services. Objective: To examine the effectiveness of the first five weeks of a 24/7 physician-staffed COVID-19 hotline. Design: Cohort study using electronic health records. Setting: A single large health care system in Northeast Ohio. Participants: During 5 weeks of operation, 10,112 patients called the hotline (callers) and were evaluated by a registered nurse (RN) using standardized protocols. Of these, 4,213 (42%) were referred for a physician telehealth visit (telehealth patients). The mean age of callers was 42 years. 67% were female, 51% white, and 46% were on Medicaid or uninsured. Intervention: Physician telehealth visits for COVID-19. Main Outcomes and Measures: We describe clinical diagnosis, patient characteristics (age, sex race/ethnicity, smoking status, insurance status), and visit disposition. We use logistic regression to evaluate associations between patient characteristics, visit disposition and subsequent emergency department use, hospitalization, and SARS-Cov-2 PCR testing. Results: Common caller concerns included cough, fever, and shortness of breath. Most telehealth patients (79%) were advised to self-isolate at home, 14% were determined to be unlikely to have COVID-19, 3% were advised to seek emergency care, and 4% had miscellaneous other dispositions. A total of 287 (7%) patients had a subsequent ED visit, and 44 (1%) were hospitalized with a COVID-19 diagnosis. Of the callers, 482 (5%) had a COVID-19 test reported with 69 (14%) testing positive. Among patients advised to stay at home, 83% had no further face-to-face visits. In multivariable results, only a physician recommendation to seek emergency care was associated with emergency room use (OR=4.73, 95%CI 1.37-16.39, p=.014). Only older age was associated with having a positive test result. Conclusions and Relevance: Robust, physician-directed telehealth services can meet a wide range of needs during the acute phase of a pandemic, conserving scarce resources such as personal protective equipment and testing supplies and preventing the spread of infections to patients and health care workers.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».