AI-Enabled Virtual Clinic Impact on Pharmacist Confidence in Managing Warfarin: Implications for Experiential Education
Bibliographic record
Abstract
OBJECTIVE: Despite an expanding scope of practice, uptake of advanced clinical services is hindered by pharmacists' self-efficacy in high-stakes decision-making. Traditional experiential learning builds confidence but is constrained by preceptor shortages, scheduling conflicts, and travel requirements. This study evaluates an artificial intelligence-enabled virtual clinical training program designed to replace in-person warfarin-management rotations. METHODS: This mixed-methods study assessed the impact of the Management of Oral Anticoagulation Therapy course on learners' confidence and satisfaction. The course combines online modules with a virtual clinic that simulates a full clinical rotation, including patient encounters, therapeutic decisions, documentation, and structured feedback. Surveys administered after the online modules and again after completing the virtual clinic captured confidence (5-point Likert) and program satisfaction; free-text responses underwent content analysis. RESULTS: Of 287 participants (96.9% pharmacists), mean confidence on a 5-point Likert scale increased from baseline (1.92) to post-modules (3.85) and further after the virtual clinic (4.24). Approximately 40% experienced additional gains following simulations. Virtual simulations were ranked the most valuable component (69%). Qualitative analysis yielded 4 themes: experiential consolidation of learning, virtual experience comparable to in-person training, complementary role of preparatory materials, and technical refinements needed. CONCLUSION: The artificial intelligence-enabled virtual clinic effectively replaced traditional clinical rotations and enhanced confidence in warfarin care. Graduates' patients later achieved the highest time in therapeutic range reported in Canadian general practice, supporting real-world impact. The Management of Oral Anticoagulation Therapy model may mitigate preceptor shortages and deliver standardized experiential training; broader validation across therapeutic areas and learner groups is warranted.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".