Effect of Pharmacist Involvement in Emergency Department Microbiological Culture Follow‐Up Services on Clinical Outcomes and Workflow: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Introduction Patients are often discharged from the emergency department (ED) while results from microbiological cultures are still pending. Most institutions provide culture follow‐up, but there is wide variability in the structure and personnel involved. Pharmacists are well suited for involvement in culture follow‐up services; though no systematic review has evaluated the impact of pharmacists in this clinical role. Objectives To assess the impact of pharmacists in ED culture follow‐up services on: (1) clinical outcomes; and (2) workflow. To characterize pharmacist roles/responsibilities as part of ED culture follow‐up services. Methods A search of the following databases was conducted: Medline, International Pharmaceutical Abstracts, and Web of Science. Studies were included if they described culture follow‐up services with pharmacist involvement in an ED setting. Screening and quality assessment were performed using Covidence software and the Newcastle‐Ottawa Scale, respectively. Data extraction focused on study design, participants, types of cultures, and clinical and time‐based outcomes. Results Thirty‐three studies met inclusion criteria. Pharmacist involvement was consistently associated with reduced time from culture result to first attempted patient contact (ranging from 9.1–62.3 h, compared with 23.6–72 h without pharmacist involvement). Some studies found absolute reductions in ED revisit or readmission rates ranging from 4.2% to 15.4%, while others found no difference. Pharmacists significantly improved the appropriateness of antimicrobial therapy, with fewer missed interventions and less unnecessary antibiotic use. Current literature is limited by heterogeneity in study designs, small sample sizes, and a lack of randomized controlled trials. Conclusion Pharmacist involvement in ED culture follow‐up services demonstrates clear benefits in reducing time to initial review and patient contact. Further research is needed to address methodological limitations of current literature and to explore the benefits of negative culture follow‐up services, expanded pharmacy services through collaborative practice agreements, and strategies to enhance the efficiency of culture follow‐up services.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".