MétaCan
Menu
Back to cohort
Record W4223586268 · doi:10.2196/32528

Empowering Patients Through Virtual Care Delivery: Qualitative Study With Micropractice Clinic Patients and Health Care Providers

2022· article· en· W4223586268 on OpenAlexafffundvenue
Lindsay Burton, Kathy L. Rush, Mindy A Smith, Selena Davis, Patricia Rodriguez Echeverria, Lina Suazo Hidalgo, Matthias Görges

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsBC Children's HospitalOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersMichael Smith Health Research BC
KeywordsTelehealthHealth carePhoneContext (archaeology)Focus groupTelemedicineNursingPatient satisfactionPatient portalVideoconferencingDigital healthMedicineBusinessMultimediaComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Prior to the wider adoption of digital health technologies during the COVID-19 pandemic, applications of virtual care were largely limited to specialist visits and remote care using telehealth (phone or video) applications. Data sharing approaches using tethered patient portals were mostly built around hospitals and larger care systems. These portals offer opportunities for improved communication, but despite a belief that care has improved, they have so far shown few outcome improvements beyond medication adherence. Less is known about use of virtual care and related tools in the outpatient context and particularly in rural community contexts. OBJECTIVE: This study aims to reflect on the opportunities and barriers for sustainable virtual care through an example of a digitally enabled rural micropractice, which has provided 10%-15% virtual care since 2016 and 70% virtual care since March 2020. METHODS: Three focus groups, 1 with providers (physician and medical office manager) and 2 with a total of 8 patients from a rural micropractice in British Columbia, were conducted in November 2020 and December 2020. Virtual care delivery was explored through the topics of communication approach, mixing virtual and in-person care, the practice team's journey in developing these approaches, and provider and patient satisfaction with the care model. Interviews were transcribed, checked for accuracy against recordings, and thematically analyzed. RESULTS: Both patients and providers reported ease of communication and high satisfaction. Either could initiate communication, and patients found the ability to share health information asynchronously through the portal allowed time to reflect and prepare their thoughts. Patients were highly engaged and reported feeling empowered and true partners in their health care, although they noted limited care coordination with specialists. The mix of virtual and in-person visits was highly regarded by patients and providers, and patients reported feeling safe and cared for 24/7, although both expressed concern about work spilling into the provider's home life. The physician worried about missed diagnoses with virtual care. With respect to establishing the micropractice, solutions took about 5 years to optimize, with providers noting a learning curve requiring technical support for both themselves and their patients and a willingness to respond to patient feedback to identify the best solutions. Despite a mature virtual practice, patients reported deferred care due to COVID-19. CONCLUSIONS: The micropractice's hybrid care model encouraged patients to be true partners in their care and resulted in high patient engagement and satisfaction; yet, success may rely on the patient population being willing to engage and being comfortable with technology. Barriers lie in gaps in care coordination and provider fear that signs or symptoms more evident with an in-person exam could be missed. Even in this setting, deferral of care in light of COVID-19 was present, and opportunities to address care gaps should be sought.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.105
GPT teacher head0.537
Teacher spread0.432 · 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

Citations36
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueJMIR Formative ResearchSame topicTelemedicine and Telehealth ImplementationFrench-language works237,207