Electronic Health Records and Antimicrobial Stewardship Research: a Narrative Review
Bibliographic record
Abstract
Purpose of Review: This review summarises epidemiological research using electronic health records (EHR) for antimicrobial stewardship. Recent Findings: EHRs enable surveillance of antibiotic utilisation and infection consultations. Prescribing for respiratory tract infections has declined in the UK following reduced consultation rates. Reductions in prescribing for skin and urinary tract infections have been less marked. Drug selection has improved and use of broad-spectrum antimicrobics reduced. Diagnoses of pneumonia, sepsis and bacterial endocarditis have increased in primary care. Analytical studies have quantified risks of serious bacterial infections following reduced antibiotic prescribing. EHRs are increasingly used in interventional studies including point-of-care trials and cluster randomised trials of quality improvement. Analytical and interventional studies indicate patient groups for whom antibiotic utilisation may be more safely reduced. Summary: EHRs offer opportunities for surveillance and interventions that engage practitioners in the effects of improved prescribing practices, with the potential for better outcomes with targeted study designs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".