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Record W7000355645

Examining the relationship between the “real world” adoption of digital health tools and primary care experience

2022· dissertation· en· W7000355645 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2022
Typedissertation
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionPrimary careHealth careLogistic regressionDigital healthPatient experiencePatient portalMEDLINEPrimary health care
DOInot available

Abstract

fetched live from OpenAlex

Background: Patient experience is a crucial measure of patient-centeredness and quality care delivery. Digital health may contribute to patient experience by offering tailored and accessible avenues of care. Purpose: I explored how access to digital health, including telehealth, electronic health records, and online booking, may be associated with improved primary care experience for Ontario adults. Methods: This cross-sectional study included Ontario adults (16 years or older) who responded to waves 27 to 29 of the Health Care Experience Survey (HCES) between May 2019 and February 2020. Adults who did not see their primary care provider within the past 12 months or did not have a primary care provider were excluded. Outcomes included a summed patient experience score derived from five HCES experience-related questions and time to appointment for a health concern. Associations between outcomes and digital health interventions were tested through chi-square tests and logistic regression while adjusting for confounders and stratifying by health care utilization. Results: 3,700 participants met the inclusion criteria, where 2204 remotely communicated with their primary care provider (59.6%), 98 digitally accessed health records (2.6%), and 120 booked an appointment online (3.2%). We observed no significant associations between digital health tools and patient experience or time to appointments through chi-square tests. Participants with over three primary care visits in the past year who accessed online booking were 84% less likely to report poorer experience scores than participants without online booking access [Adjusted OR 0.16, 95% CI 0.02 – 0.56, p < 0.05]. Participants with three or fewer primary care encounters who accessed online booking, compared to the same reference group, were 72% less likely to report having a same or next day appointment with their primary care provider [Adjusted OR 0.25, 95% CI 0.08 – 0.64, p < 0.01]. Significant associations were observed between other sociodemographic factors and patient experience and access to care outcomes. Interpretation: The associations between digital health access and patient experience and access to care were inconsistent across different analyses. Despite experimental studies observing the benefits of digital health adoption in primary care, the effect is unclear in the real-world context. Furthermore, drawing conclusions on the relationship between digital health and quality care outcomes was limited due to the lack of adoption of digital health before the COVID-19 pandemic. As digital health adoption grows, future research should utilize the availability of further data to evaluate the effectiveness of digital health in Ontario primary 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 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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.071
GPT teacher head0.312
Teacher spread0.241 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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