Three systems that show how AI, hospital data, and pharmacist review can be turned into usable clinical infrastructure.
A medication-safety workflow that turns clinical messages and hospital-data triggers into reviewable ADR signals and report drafts.
A daily workbench that brings patient data, medication review, TDM/PK, TPN, MTM, pathway review, and pharmacy quality work into one interface.
A training-management system that standardizes paper-style tasks, mentor review, progress tracking, and batch exports.
Visual previews distilled from the local systems’ actual front-end structure.
The interface is organized around message monitoring, signal mining, report management, patient self-reporting, and rule configuration, so the pharmacist can move from signal to report without leaving the workflow.
The workstation reads like a real pharmacist cockpit: patient cards, patient 360, ward-round notes, consultations, TDM/PK, neonatal pharmacy, clinical pathways, order review, statistics, and quality control.
The training base turns fragmented teaching records into a managed workflow: trainees submit paper-style forms, mentors review them, administrators watch progress, and outputs can be exported.
The emphasis is not that the systems are large; it is that they connect messy clinical data to usable pharmacist decisions.
Rules and LLM extraction help surface and structure candidate information, while review and submission remain pharmacist-led.
Orders, labs, EMR narratives, nursing records, cultures, exams, vitals, and identifiers are reorganized into practical work surfaces.
The systems produce reports, progress views, and editable Word or Excel outputs that match real hospital documentation needs.