The Perceptions of and Factors Associated With the Adoption of the Electronic Health Record Sharing System Among Patients and Physicians: Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: The electronic health record sharing system (eHRSS) was implemented as a new health care delivery platform to facilitate two-way communication between the public and private sectors in Hong Kong. OBJECTIVE: This study aimed to investigate the perceptions of and factors associated with the adoption of eHRSS among patients, the general public, and private physicians. METHODS: Telephone interviews were conducted in 2018 by using a simple random sampling strategy from a list of patients who had enrolled in the eHRSS and a territory-wide telephone directory for nonenrolled residents. We completed 2000 surveys (1000 each for enrolled and nonenrolled individuals). Private physicians completed self-administered questionnaires, including 762 valid questionnaires from 454 enrolled physicians and 308 nonenrolled physicians. RESULTS: Most participants (707/1000, 70.70%) were satisfied with the overall performance of the eHRSS. Regarding registration status, most nonenrolled patients (647/1000, 64.70%) reported that "no recommendation from their physicians and family members" was the major barrier, whereas more than half of the physicians (536/1000, 53.60%) expressed concerns on "additional workload due to use of eHRSS." A multivariate regression analysis showed that patients were more likely to register when they reported "other service providers could view the medical records" (adjusted odds ratio [aOR] 6.09, 95% CI 4.87-7.63; P<.001) and "friends' or family's recommendation or assistance in registration" (aOR 3.51, 95% CI 2.04-6.03; P=.001). Physicians were more likely to register when they believed that the eHRSS could improve the quality of health care service (aOR 4.70, 95% CI 1.77-12.51; P=.002) and were aware that the eHRSS could reduce duplicated tests and treatments (aOR 4.16, 95% CI 1.73-9.97; P=.001). CONCLUSIONS: Increasing the possibility of viewing patients' personal medical record, expanding the sharable data scope for patients, and highlighting the benefits of the system for physicians could be effective to enhance the adoption of the eHRSS.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".