Features and frequency of use of electronic health records in primary care across 20 countries: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: Variation exists in the capabilities of electronic healthcare records (EHRs) systems and the frequency of their use by primary care physicians (PCPs) from different settings. We aimed to examine the factors associated with everyday EHRs use by PCPs, characterise the EHRs features available to PCPs, and to identify the impact of practice settings on feature availability. STUDY DESIGN: Cross-sectional study. METHODS: PCPs from 20 countries completed cross-sectional online survey between June and September 2020. Responses which reported frequency of EHRs use were retained. Associations between everyday EHRs use and PCP and practice factors (country, urbanicity, and digital maturity) were explored using multivariable logistic regression analyses. The effect of practice factors on the variation in availability of ten EHRs features was estimated using Cramer's V. RESULTS: Responses from 1520 out of 1605 PCPs surveyed (94·7%) were retained. Everyday EHRs use was reported by 91·2% of PCPs. Everyday EHRs use was associated with PCPs working >28 h per week, having more years of experience using EHRs, country of employment, and higher digital maturity. EHRs features concerning entering, and retrieving data were available to most PCPs. Few PCPs reported having access to tools for 'interactive patient education' (37·3%) or 'home monitoring and self-testing of chronic conditions' (34·3%). Country of practice was associated with availability of all EHRs features (Cramer's V range: 0·2-0·6), particularly with availability of tools enabling patient EHRs access (Cramer's V: 0·6, P < 0.0001). Greater feature availability of EHRs features was observed with greater digital maturity. CONCLUSIONS: EHRs features intended for patient use were uncommon across countries and levels of digital maturity. Systems-level research is necessary to identify the country-specific barriers impeding the implementation of EHRs features in primary care, particularly of EHRs features enabling patient interaction with EHRs, to develop strategies to improve systems-wide EHRs use.
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How this classification was reachedexpand
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 | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".