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Record W4382182344 · doi:10.2196/44164

Hospital-Level Implementation Barriers, Facilitators, and Willingness to Use a New Regional Disaster Teleconsultation System: Cross-Sectional Survey Study

2023· article· en· W4382182344 on OpenAlexvenueno aff
Tehnaz P. Boyle, Krislyn M. Boggs, Jingya Gao, Maureen McMahon, Rachel Bedenbaugh, Lauren Schmidt, Kori S. Zachrison, Eric Goralnick, Paul D. Biddinger, Carlos A. Camargo

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteHealth Resources and Services AdministrationNational Institutes of Health
KeywordsCredentialingTelehealthMedical emergencyMedicineHealth careTelemedicineNursingPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The Region 1 Disaster Health Response System project is developing new telehealth capabilities to provide rapid, temporary access to clinical experts across US jurisdictions to support regional disaster health response. OBJECTIVE: To guide future implementation, we identified hospital-level barriers, facilitators, and willingness to use a novel regional peer-to-peer disaster teleconsultation system for disaster health response. METHODS: We used the National Emergency Department Inventory-USA database to identify all 189 hospital-based and freestanding emergency departments (EDs) in New England states. We digitally or telephonically surveyed emergency managers regarding notification systems used for large-scale no-notice emergency events, access to consultants in 6 disaster-relevant specialties, disaster credentialing requirements before system use, reliability and redundancy of internet or cellular service, and willingness to use a disaster teleconsultation system. We examined state-wise hospital and ED disaster response capability. RESULTS: Overall, 164 (87%) hospitals and EDs responded-126 (77%) completed telephone surveys. Most (n=148, 90%) receive emergency notifications from state-based systems. Forty (24%) hospitals and EDs lacked access to burn specialists; toxicologists, 30 (18%); radiation specialists, 25 (15%); and trauma specialists, 20 (12%). Among critical access hospitals (CAHs) or EDs with <10,000 annual visits (n=36), 92% received routine nondisaster telehealth services but lacked toxicologist (25%), burn (22%), and radiation (17%) specialist access. Most hospitals and EDs (n=115, 70%) require disaster credentialing of teleconsultants before system use. Among 113 hospitals and EDs with written disaster credentialing procedures, 28% expected completing disaster credentialing within 24 hours, and 55% within 25-72 hours, which varied by state. Most (n=154, 94%) reported adequate internet or cellular service for video-streaming; 81% maintained cellular service despite internet disruption. Fewer rural hospitals and EDs reported reliable internet or cellular service (19/22, 86% vs 135/142, 95%) and ability to maintain cellular service with internet disruption (11/19, 58% vs 113/135, 84%) than urban hospitals and EDs. Overall, 133 (81%) were somewhat or very likely to use a regional disaster teleconsultation system. Large-volume EDs (annual visits ≥40,000) were less likely to use the service than smaller ones; all CAHs and nearly all rural hospitals or freestanding EDs were likely to use disaster consultation services. Among hospitals and EDs somewhat or very unlikely to use the system (n=26), sufficient consultant access (69%) and reluctance to use new technology or systems (27%) were common barriers. Potential delays (19%), liability (19%), privacy (15%), and hospital information system security restrictions (15%) were infrequent concerns. CONCLUSIONS: Most New England hospitals and EDs have access to state emergency notification systems, telecommunication infrastructure, and willingness to use a new regional disaster teleconsultation system. System developers should focus on ways to improve telecommunication redundancy in rural areas and use low-bandwidth technology to maintain service availability to CAHs and rural hospitals and EDs. Policies and procedures to accelerate and standardize disaster credentialing are needed for implementation across jurisdictions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.153
GPT teacher head0.448
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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