The impact of electronic health record components on quality of patient care : a secondary data analysis
Bibliographic record
Abstract
Aims: The study examined the unique effects of electronic health record (EHR) components used by Canadian nurses in direct patient care on the quality of patient care, while controlling for individual nurses’ characteristics. Specifically, the study aimed to measure how specific EHR components (i.e., computerized clinical decision support, computerized provider order entry, electronic medication administration record, nursing information systems) impact the quality of patient care. Background: The implementation of EHR systems and components in Canada are occurring at rapid pace with a goal to enhance quality, increase access and reduce costs. Research findings related to the impact of EHRs on quality of care remain inconclusive, as the majority of studies had small sample sizes and ambiguous descriptions of EHR components. Methods: This cross-sectional secondary analysis drew upon data from 1031 direct patient care nurses from a Canadian survey conducted by Canada Health Infoway. Results: One EHR component and three nurse characteristics were significantly associated with quality of patient care. The clinical decision support tool was positively associated with quality of patient care. Nursing experience was inversely associated to quality of patient care. Experience documenting in EHRs and EHR satisfaction were positively associated with quality of patient care. Conclusion: EHR component use, specifically clinical decision support systems, may impact quality of care. Nursing experience, experience documenting in EHRs and EHR satisfaction may also influence quality of care. Implications: Further research should be conducted to explore the impact of EHR use on nurses’ perception of quality of care. Researchers should include adequate descriptions of their EHR components and use psychometrically validated tools to increase the generalizability of their findings. Specifically, Infoway should continue to deploy their national nursing survey on health technology use to monitor EHR trends in Canada.
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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 | 0.020 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".