Providers’ Perspectives on Electronic Data- Sharing with Patients: A Qualitative Descriptive Study
Bibliographic record
Abstract
Little is known about data sharing between healthcare providers and their patients.In the wake of the COVID-19 pandemic and the dramatic shift to virtual care delivery, there is a growing imperative to understand the types of patient-generated data used in patient care; the digital tools, functionalities, and processes used in sharing these data; and the barriers and facilitators to data sharing.This descriptive qualitative study explored the electronic data-sharing practices of primary healthcare providers with their patients.Providers' (n=14) electronic data-sharing practices during the pandemic and their use of asynchronous and synchronous digital data-sharing modalities were highly variable.Most providers used telephone as their main synchronous modality for data sharing, with asynchronous modalities used to a limited extent or to complement synchronous data sharing.Providers who rarely used asynchronous modalities only collected patient-generated data in select circumstances.Barriers and facilitators of datasharing practices included digital infrastructure, integration, cost, patient factors, and provider factors such as care team composition, capacity, and percentage of virtual to in-person visits.Identifying solutions to support and enable providers and their patients with integrated digital tools and technologies and best practices for data sharing is necessary to optimize quality care and address care gaps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".