Teleneurology service provided via tablet technology: 3-year outcomes and physician satisfaction
Bibliographic record
Abstract
INTRODUCTION: This study aimed to demonstrate that teleneurology consultations conducted via tablet technology are an efficient and cost-effective means of managing acute neurologic emergencies at community-based hospitals and that utilizing such technology yields high community physician satisfaction. METHOD: During a 39-month period, Vanderbilt University Medical Center in Tennessee USA, provided teleneurology services to 10 community-based hospitals that lacked adequate neurology coverage. Hospitalists at one community-based hospital were not comfortable treating any patient with a neurologic symptom, resulting in 100% of those patients being transferred. This facility now retains more than 60% of neurology patients. For less than US$1200, these hospitals were able to meet the only capital expenditure required to launch this service: the purchase of handheld tablet computers. Real-time teleneurology consultations were conducted via tablet using two-way video conferencing, radiologic image sharing, and medical record documentation. Community physicians were regularly surveyed to assess satisfaction. RESULTS: From February 2014 to May 2017, 3626 teleneurology consultations were conducted. Community physicians, in partnership with neurologists, successfully managed 87% of patients at the community-based hospital. Only 13% of patients required transfer to another facility for a higher level of care. The most common diagnoses included stroke (34%), seizure (11%), and headache/migraine (6%). The average time for the neurologist to answer a request for consultation page and connect with the community physician was 10.6 minutes. Ninety-one percent of community physicians were satisfied or somewhat satisfied with the overall service. CONCLUSION: In the assessment of neurology patients, tablets are a more cost-effective alternative to traditional telehealth technologies. The devices promote efficiency in consultations through ease of use and low transfer rates, and survey results indicate community physician satisfaction.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".