MétaCan
Menu
Back to cohort
Record W3110817035 · doi:10.1136/bmjoq-2020-ihi.9

9 Rheum service: improving virtual care during COVID-19

2020· article· en· W3110817035 on OpenAlexaff
Joseph Carson, Stephanie Gottheil

Bibliographic record

VenueOral Presentations · 2020
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsWestern University
Fundersnot available
KeywordsPhoneVideoconferencingTelemedicineComputer scienceTelehealthService (business)MultimediaMedical emergencyMedicineHealth care

Abstract

fetched live from OpenAlex

<h3>Background</h3> During COVID-19, rheumatology outpatients need timely access to care while social distancing. Video consults have potential to improve virtual assessments, however, some patients and providers are apprehensive about using this technology. <h3>Objectives</h3> Provide delightful and effective video consults for 90% of new patients by July 1. <h3>Methods</h3> We redesigned video appointments to create a seamless virtual experience. PDSA Series 1 identified improvement opportunities with a process map, fishbone, and driver diagram. PDSA Series 2 tested and implemented change ideas: digital appointment confirmations, reminders, and forms; video consults with limited pre-call testing; digital reports, requisitions, and messaging. PDSA Series 3 refined changes by decreasing reminders, increasing pre-call tests, and adding backup video platforms. Outcome measures were: 1)% consults by video, 2)% requesting more video appointments. Process measures were: 1) pre-call tests completed, 2) technical difficulties. Our balance measure was% virtual diagnoses modified after in-person visits. We collected data over ten weeks and emailed anonymized patient surveys one week after video consults. We analyzed data with run charts and descriptive statistics. <h3>Results</h3> We scheduled 135 new consults: 120 (89%) video, 14 (10%) phone, and 1 (1%) office. Twenty-one patients (16%) did not own a video-enabled device. Pre-visit, 12 patients (10%) participated in pre-call testing. Video consults were initiated for 97% of scheduled patients; of these, 6% suffered technical difficulties, requiring a switch to phone or another video platform. Surveys were completed after 40% (48/120) of video visits: 68% of patients wanted another video appointment; 28% were ‘not sure’; 4% declined. Virtual diagnoses stayed the same for 84% (32/38) of patients with follow-up in-person assessments. <h3>Conclusions</h3> While video consults proved effective for most patients, sociodemographic and technological barriers prevented others from participating. Next steps include improving access to video-enabled devices and providing more pre-visit training to reduce these barriers.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.109
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.065
GPT teacher head0.390
Teacher spread0.324 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueOral PresentationsSame topicTelemedicine and Telehealth ImplementationFrench-language works237,207