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Record W4285818572 · doi:10.1177/23743735221112216

Why are Patient Portals Important in the Age of COVID-19? Reflecting on Patient and Team Experiences From a Toronto Hospital Network

2022· article· en· W4285818572 on OpenAlexaffabout
Brian Lo, Rebecca Charow, Sarah Laberge, Vasiliki Bakas, Laura Williams, David Wiljer

Bibliographic record

VenueJournal of Patient Experience · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental HealthUniversity Health Network
Fundersnot available
KeywordsPatient portalPandemicCoronavirus disease 2019 (COVID-19)Test (biology)Health careEarly adopterTelemedicinePerceptionNursingMedical emergencyPsychologyMedicineBusinessPolitical scienceMarketingDisease

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has changed how care is being delivered in Canada. With conventional in-person care being transitioned to virtual care, the approach that patients are able to engage and access their care has dramatically changed. At the University Health Network (UHN), which is Canada's largest academic and teaching hospital network, we expanded the myUHN Patient Portal in 2017 after its early adopter phase to enable patients and family members to view parts of their clinical notes and test results. As the pandemic progressed, we observed high adoption of myUHN to support virtual care and rapid delivery of COVID-19 test results in real time. In this article, we share and reflect on our experience of adapting myUHN to support the demands of the pandemic, including portal adoption outcomes across multiple waves of the pandemic, the impetus for increased patient experience staff dedicated for myUHN support, and patients' perceptions of the value of the portal and virtual care. Based on these reflections, we outline our perspectives on the future role of patient portals to support patient care and experience in a post-pandemic environment.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.477

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)0.0000.000

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 head0.043
GPT teacher head0.379
Teacher spread0.336 · 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 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

Citations12
Published2022
Admission routes2
Has abstractyes

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