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Record W3133837984 · doi:10.1093/jcag/gwab002.084

A86 INFLAMMATORY BOWEL DISEASE PATIENTS’ SATISFACTION WITH TELEHEALTH: DURING THE COVID-19 PANDEMIC

2021· article· en· W3133837984 on OpenAlexaffabout
M Painchaud, Sonal Singh, Robert M. Penner

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2021
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsTelehealthMedicinePandemicPatient satisfactionTelemedicineFamily medicinePopulationCoronavirus disease 2019 (COVID-19)Quality (philosophy)Health careDiseaseInflammatory bowel diseaseMedical emergencyNursingInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background Due to the COVID-19 pandemic, clinics were forced to implement telehealth into clinical practice. Inflammatory bowel disease (IBD) patients are a unique population that require long-term care to achieve and maintain deep remission of disease. Thus, they require stable and continuous contact with healthcare providers, often with multiple appointments. We examined an IBD predominant practice, also providing care for general gastrointestinal (GI) conditions in Kelowna, British Columbia. As telehealth has the potential to become a standard of care for clinics, patient satisfaction must be considered. We hypothesize that with the efficacy and ease of remote appointments, there will be an increase in patient satisfaction, quality of care, and quality of communication. Aims We aim to compare the level of patient satisfaction between in-person appointments pre-pandemic, and current remote appointment telehealth practices. Methods An online survey was sent to the 608 patients who had participated in one or more remote appointment between March 15-June 15, 2020. The survey compared the level of patient satisfaction, quality of care, and quality of communication between patient and doctor before and during the pandemic. It was also determined if patients would elect to continue with remote appointments in the future due to ease of use, and time/financial resources saved. Results Of the 273 participants, 80% were IBD patients while 20% were treated for other GI conditions. A total of 78% reported that they would elect to continue with remote appointments as their primary point of care with their doctor. The remaining 22% reported that they prefer in-person visits due to the necessity of a physical exam, yet specified that communication by these remote means was still of good quality. Levels of patient satisfaction before and during the pandemic remained consistent, where 59% of patients assigned a satisfaction rating of 10 (highest) to their pre-pandemic in-person appointments, and 54% of patients assigned a rating of 10 to their remote appointments during the pandemic. Similar consistent results were found for quality of care and quality of communication. A total of 70% of patients reported that if this service had not been available, they would have sought out other forms of care; 18% of the total responses considering emergency care. Conclusions IBD patients at Kelowna Gastroenterology perceived similar levels of satisfaction, quality of care, and quality of communication with both in-person and telehealth appointments. This suggests that telehealth practices may be a cost-effective, sustainable appointment style that provides comparable quality to in-person appointments. Funding Agencies None

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.274
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2021
Admission routes2
Has abstractyes

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