Electronic health record and primary care physician self-reported quality of care: a multilevel study in China
Bibliographic record
Abstract
Background: Health information technology is one of the building blocks of a highperforming health system.However, the evidence regarding the influence of an electronic health record (EHR) on the quality of care remains mixed, especially in low-and middleincome countries.Objective: This study examines the association between greater EHR functionality and primary care physician self-reported quality of care.Methods: A total of 224 primary care physicians from 38 community health centres (CHCs) in four large Chinese cities participated in a cross-sectional survey to assess CHC care quality.Each CHC director scored their CHC's EHR functionality on the availability of ten typical features covering health information, data, results management, patient access, and clinical decision support.Data analysis utilised hierarchical linear modelling.Results: The availability of five EHR features was positively associated with physician selfreported clinical quality: share records online with providers outside the practice (β = 0.276, p = 0.04), access records online by the patient (β = 0.325, p = 0.04), alert provider of potential prescription problems (β = 0.353, p = 0.04), send the patient reminders for care (β = 0.419, p = 0.003), and list patients by diagnosis or health risk (β = 0.282, p = 0.04).However, no association was found between specific features availability or total features score and physician self-reported preventive quality.Conclusions: This study provides evidence that the availability of EHR systems, and specific features of these systems, was positively associated with physician self-reported quality of care in these 38 CHCs.Future longitudinal studies focused on standardised quality metrics, and designed to control known confounding variables, will further inform quality improvement efforts in primary care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".