Patient-Perceived Usefulness of Online Electronic Medical Records: Employing Grounded Theory in the Development of Information and Communication Technologies for Use by Patients Living with Chronic Illness
Bibliographic record
Abstract
OBJECTIVE: Patient use of online electronic medical records (EMR) holds the potential to improve health outcomes. The purpose of this study is to discover how patients living with chronic inflammatory bowel disease (IBD) value Internet-based patient access to electronic patient records. DESIGN: This was a qualitative, exploratory, descriptive study using in-depth interviews and focus groups of a total of 12 patients with IBD of at least one-year duration at University Health Network, a tertiary care center in Toronto, Ontario. RESULTS: Four themes have been elucidated that comprise a theoretical framework of patient-perceived information and communication technology usefulness: promotion of a sense of illness ownership, of patient-driven communication, of personalized support, and of mutual trust. CONCLUSIONS: For patients with chronic IBD, simply providing access to electronic medical records has little usefulness on its own. Useful technology for patients with IBD is multifaceted, self-care promoting, and integrated into the patient's already existing health and psychosocial support infrastructure. The four identified themes can serve as focal points for the evaluation of information technology designed for patient use, thus providing a patient-centered framework for developers seeking to adapt existing EMR systems to patient access and use for the purposes of improving health care quality and health outcomes. Further studies in other populations are needed to enhance generalizability of the emergent theory.
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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 | 0.039 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".