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Record W4225136235 · doi:10.2196/37196

Virtual Specialist Care During the COVID-19 Pandemic: Multimethod Patient Experience Study

2022· article· en· W4225136235 on OpenAlexaffvenueabout
Katie N. Dainty, M. Bianca Seaton, Antonio Estacio, Lisa K. Hicks, Trevor Jamieson, Sarah Ward, Catherine Yu, Jeffrey D. Mosko, Charles D. Kassardjian

Bibliographic record

VenueJMIR Medical Informatics · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsSt. Michael's HospitalNorth York General Hospital
Fundersnot available
KeywordsSpecialtyMedicineFamily medicinePandemicTelemedicineDescriptive statisticsCoronavirus disease 2019 (COVID-19)Health careInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Transitioning nonemergency, ambulatory medical care to virtual visits in light of the COVID-19 global pandemic has been a massive shift in philosophy and practice that naturally came with a steep learning curve for patients, physicians, and clinic administrators. OBJECTIVE: We undertook a multimethod study to understand the key factors associated with successful and less successful experiences of virtual specialist care, particularly as they relate to the patient experience of care. METHODS: This study was designed as a multimethod patient experience study using survey methods, descriptive qualitative interview methodology, and administrative virtual care data collected by the hospital decision support team. Six specialty departments participated in the study (endoscopy, orthopedics, neurology, hematology, rheumatology, and gastroenterology). All patients who could speak and read English and attended a virtual specialist appointment in a participating clinic at St. Michael's Hospital (Toronto, Ontario, Canada) between October 1, 2020, and January 30, 2021, were eligible to participate. RESULTS: During the study period, 51,702 virtual specialist visits were conducted in the departments that participated in the study. Of those, 96% were conducted by telephone and 4% by video. In both the survey and interview data, there was an overall consensus that virtual care is a satisfying alternative to in-person care, with benefits such as reduced travel, cost, time, and SARS-CoV-2 exposure, and increased convenience. Our analysis further revealed that the specific reason for the visit and the nature and status of the medical condition are important considerations in terms of guidance on where virtual care is most effective. Technology issues were not reported as a major challenge in our data, given that the majority of "virtual" visits reported by our participants were conducted by telephone, which is an important distinction. Despite the positive value of virtual care discussed by the majority of interview participants, 50% of the survey respondents still indicated they would prefer to see their physician in person. CONCLUSIONS: Patient experience data collected in this study indicate a high level of satisfaction with virtual specialty care, but also signal that there are nuances to be considered to ensure it is an appropriate and sustainable part of the standard of care.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.429
Teacher spread0.364 · 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.

Study designQualitative
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

Citations17
Published2022
Admission routes3
Has abstractyes

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