Pharmacists as Interprofessional Collaborators and Leaders through Clinical Pathways
Bibliographic record
Abstract
Pharmacists possess pivotal competencies and expertise in developing clinical pathways (CPs). We present a tertiary care facility experience of pharmacists vis-a-vis interprofessional collaboration for designing and implementing CPs. We participated in the development of CPs as leading members of a collaborative team of healthcare professionals. We reviewed literature, aligning it with hospital formulary and institutional standards, and participated in weekly team meetings for six months. Several tools and services were adapted to guide prescribing and standardization of care through time-bound order sets. Fifteen CPs leading to admissions in medical wards were developed and integrated into Computerized Prescriber Order Entry (CPOE) sets. Tools and services included (1) reporting of creatinine clearance to guide optimum dosing; (2) advisory flags for dosing and infusion rates; (3) piloting of medication reconciliation and counseling services before discharge were initiated; (4) Arabic drug leaflets were designed to educate patients; and (5) five CPs were included in pragmatic randomized control trials with a clinical pharmacist as co-investigator. Clinical pharmacists conducted continuous orientation to various healthcare professionals throughout the process. CPs provide unique opportunities for establishing and evaluating patient-centered pharmaceutical services and allow clinical pharmacists to demonstrate interprofessional leadership in collaboration with multidisciplinary teams.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".