Virtual Pharmacy: An Integrated Collaborative Redesign Targeting Medication-Related Problems in Patients with Chronic Kidney Disease
Bibliographic record
Abstract
INTRODUCTION: Collaborative management of kidney disease relies on coordinated and effective partnerships between multiple providers. Siloed traditional health systems often result in delays, barriers to treatment access, and inefficient monitoring. METHODS: We conducted a 1-year observational mixed-methods study. We included all consecutive referrals except for patients without telephone access. We assessed 4 domains of outcomes: (1) patient and caregiver experience, (2) provider experience (e.g., physicians and pharmacists), (3) clinical outcomes specific to medication-related outcomes (e.g., adherence, adverse drug events [ADEs]), and (4) value and efficiency (i.e., medication access, defined as time to treatment and resolution of medication reimbursement issues). RESULTS: Sixty-five patients were referred to the integrated virtual pharmacy (iVRx) model. Most (72%) patients were male. Patients had a median (min, max) age of 60 (27, 85) years and were taking 8 (4, 13) medications. Compared with traditional care delivery models, medication access improved for 56% of participants. Direct home delivery of medication resulted in 91% of patients receiving prescriptions within 2 days of a nephrologist visit. During more than 2,000 pharmacist-patient encounters, 208 ADEs were identified that required clinician intervention to prevent patient harm. When these ADEs were classified by severity, 53% were mild, 45% were moderate (e.g., delaying dose titration in patients initiated on glucagon-like peptide 1 (GLP-1) agonists due to intolerable gastrointestinal side effects), and the remaining 2% of ADEs were severe, meaning clinical intervention was required to prevent a serious outcome (e.g., uncontrolled blood pressure, prevention of acute kidney injury). Nephrologists reported high satisfaction with iVRx, citing efficiency, timely response, and collaboration with pharmacists as key facilitators. Of the 65 patient participants, 98% reported being extremely satisfied. CONCLUSIONS: The iVRx is an acceptable and feasible clinical strategy. Our pilot program was associated with improved kidney care by increasing medication access for patients and avoiding potential harms associated with ADEs.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".