ArticlePharmacy (Basel, Switzerland)2025
Blueprint for Constructing an AI-Based Patient Simulation to Enhance the Integration of Foundational and Clinical Sciences in Didactic Immunology in a US Doctor of Pharmacy Program: A Step-by-Step Prompt Engineering and Coding Toolkit.
Article in Pharmacy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Distilling AI Workforce-Readiness Competencies for U.S. Pharmacists from Job Advertisements: Implications for Pharmacy Education.Pharmacy (Basel, Switzerland) · 2026Article
- Promise, Mimicry, and Surveillance: Responsibly Integrating Artificial Intelligence With Socratic Inquiry in Medical Education.JMIR medical education · 2026Article
- From Competence to Character: Reimagining Neurology Education for a Complex and Changing World.Neurology. Education · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
While pharmacy education successfully employs various methodologies including case-based learning and simulated patient interactions, providing consistent, individualized guidance at scale remains challenging in team-based learning environments. Artificial intelligence (AI) offers potential solutions through automated facilitation, but its possible utility in pharmacy education remains unexplored. We developed and evaluated an AI-guided patient case discussion simulation to enhance learners' ability to integrate foundational science knowledge with clinical decision-making in a didactic immunology course in a US PharmD program. We utilized a large language model programmed with specific educational protocols and rubrics. Here, we present the step-by-step prompt engineering protocol as a toolkit. The system was structured around three core components in an immunology team-based learning activity: (1) symptomatology analysis, (2) laboratory test interpretation, and (3) pharmacist role definition and PPCP. Performance evaluation was conducted using a comprehensive rubric assessing multiple clinical reasoning and pharmaceutical knowledge domains. The standardized evaluation rubric showed reliable assessment across key competencies including condition identification (30% weighting), laboratory test interpretation (40% weighting), and pharmacist role understanding (30% weighting). Our AI patient simulator offers a scalable solution for standardizing clinical case discussions while maintaining individualized learning experiences.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.