Role of a Pharmacist in Postdischarge Care for Patients With Kidney Disease: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: The objective was to explore and describe the role of pharmacists in providing postdischarge care to patients with kidney disease. DATA SOURCES: PubMed, Embase (Elsevier), CINAHL (Ebscohost), Web of Science Core Collection, and Scopus were searched on January 30, 2023. Publication date limits were not included. Search terms were identified based on 3 concepts: kidney disease, pharmacy services, and patient discharge. Experimental, quasi-experimental, observational, and qualitative studies, or study protocols, describing the pharmacist's role in providing postdischarge care for patients with kidney disease, excluding kidney transplant recipients, were eligible. STUDY SELECTION AND DATA EXTRACTION: Six unique interventions were described in 10 studies meeting inclusion criteria. DATA SYNTHESIS: Four interventions targeted patients with acute kidney injury (AKI) during hospitalization and 2 evaluated patients with pre-existing chronic kidney disease. Pharmacists were a multidisciplinary care team (MDCT) member in 5 interventions and were the sole provider in 1. Roles commonly identified include medication review, medication reconciliation, medication action plan formation, kidney function assessment, drug dose adjustments, and disease education. Some studies showed improvements in diagnostic coding, laboratory monitoring, medication therapy problem (MTP) resolution, and patient education; prevention of hospital readmission was inconsistent. Limitations include lack of standardized reporting of kidney disease, transitions of care processes, and differences in outcomes evaluated. RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE: This review identifies potential roles of a pharmacist as part of a postdischarge MDCT for patients with varying degrees of kidney disease. CONCLUSIONS: The pharmacist's role in providing postdischarge care to patients with kidney disease is inconsistent. Multidisciplinary care teams including a pharmacist provided consistent identification and resolution of MTPs, improved patient education, and increased self-awareness of diagnosis.
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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".