Primary care physician eHealth profile and care coordination: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Digital health holds promise for enhancing care coordination and supporting patient self-management. However, various barriers, including at the healthcare professional level, hinder its adoption. This cross-sectional study explored the eHealth profile of primary care physicians and its relationship with care coordination. METHODS: As part of "The Commonwealth Fund's 2022 International Health Policy Survey of Primary Care Physicians in 10 Countries", 1114 physicians in Switzerland completed a questionnaire on their sociodemographic and workplace characteristics, digital health use and care coordination practices. Based on their responses concerning the modality, frequency and application of digital health tools, we created a digital health score. Based on responses describing the collaboration with specialists and paramedical health professionals, we created a care coordination score. The associations between both scores were assessed using stratified analyses and multiple linear regression. RESULTS: Among the 1114 participants (46% women, mean age 52 years), 83% used electronic patient records, 96% used teleconsultations for less than 5% of consultations, and 63% never used connected health tools to monitor patients with chronic diseases. Further, 16% allowed online appointments, 20% online medical prescriptions, 52% the possibility of electronically communicating lists of medications with other healthcare professionals, and 89% the possibility of email or web communications with the patient. The eHealth score was positively associated with the number of weekly working hours, being an internal medicine specialist or practising physician, the number of full-time equivalents in the practice and being in a group practice setting. The higher the eHealth profile score, the higher the care coordination score. CONCLUSION: Digital health and care coordination were positively associated. This could underscore the potential benefits of digital health in enhancing collaborative and interprofessional care practices.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".