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Module 2 Digital Dispensing OSCE published — 10 cases, 110-mark rubric•New AI Research Methods lab live•All practicals follow the Aim · Principle · Theory · Procedure standard
Run rigorous, AI-scored practicals right in your browser — every lab mapped to the syllabus and built on the Aim · Principle · Theory · Requirements · Procedure · Guided-Method standard.
A 10-case objective structured clinical examination for dispensing — with full FEFO/FIFO stock logic, a 110-mark rubric and automatic critical-fail detection, evaluated instantly in the browser.
Practicals are available to logged-in Alizon students. A few may be opened to outside participants by the office.
Hands-on molecular workflows and AI-assisted screening mapped to the syllabus.
Open lab →Interactive scenarios on research ethics, consent and integrity in AI healthcare.
Open lab →Work a live hospital case: verify a prescription, judge the interactions, pick a formulary alternative — and catch the AI recommending a drug your patient is allergic to.
Open lab →Pick any module, choose your units and how many questions — the system shuffles a fresh set each time, with the explanation after every answer. 800 questions across 8 modules.
Start practice →Proctored-style paper with question palette, marked-for-review and a live timer. Any module, any unit, or the full 100-question paper. Institution-wide rank on submission.
Open exam →10 cases · FEFO/FIFO · 110-mark rubric with critical-fail scoring.
Open lab →Work a suspected adverse drug reaction from the first phone call to a submitted case — take the history, classify seriousness, score causality and file inside the clock. Includes a full How to Report guide: the four minimum elements, the PvPI form section by section, and where the report goes.
Open lab →Carry one suspected reaction the whole way — ward, AMC, NCC-PvPI at Ghaziabad, then VigiBase at the Uppsala Monitoring Centre. MedDRA and WHODrug coding decide whether it was worth reporting.
Open lab →A 90-minute live investigation for teams of 4–5, in collaboration with Mar Dioscorus College of Pharmacy. Thirteen stages from a Kerala emergency department to the global database — the patient answers only what you ask, the form arrives incomplete on purpose, and a correct non-serious verdict becomes serious two hours later.
Open lab →Interview a simulated patient who volunteers nothing, corroborate against doctor, pharmacist, record and laboratory, then file the ICSR and judge the signal. Seven cases, levels 1–7. Open to everyone — no sign-in needed.
Open workshop →Work through an AI clinical decision-support system with guided cases.
Open lab →Rational antibiotic selection, resistance and stewardship decision-making.
Open lab →Safe handling and double-check protocols for high-alert medications.
Open lab →Research design, data handling and AI methods for healthcare projects.
Open lab →Temperature monitoring, excursions and cold-chain integrity workflows.
Open lab →Quality-assurance documentation, SOPs and audit-ready records.
Open lab →Identify Phase I–IV trials from real scenarios — objective, participants and sample size.
Open lab →Sit on an IEC/IRB — review a protocol dossier and recommend approval, modification or rejection.
Open lab →Inspect a virtual trial site across 6 departments — find the GCP breaches before the auditors do.
Open lab →Eight locked rooms, 90 minutes, one multimorbid patient — rank the evidence, reconcile three guidelines and write the right prescription.
Open lab →Search, screen, appraise and pool the evidence in 90 minutes — then catch the AI assistant citing a paper that does not exist.
Open lab →Run a live trial across 8 levels against a 20-minute clock — every non-compliant call costs you the audit.
Open lab →Audit a year of dispensing data before analysing it. One defect announces itself with a 300% spike. The other changed a column's meaning half-way through the year and looks entirely normal.
Open lab →Three readmission models, one to deploy. The most accurate finds almost nobody; the best-discriminating one collapses for the over-80s.
Open lab →A steep demand trend that is really a contract, and a medicine worth pennies that must never run out.
Open lab →Six prescribers and one obvious outlier. Adjust for case mix and the outlier turns out to be the best of them — while the real problem sat mid-table all along.
Open lab →The board wants a picking robot. Picking is not the constraint — and automating a shelf layout that groups look-alikes just reproduces the error faster.
Open lab →Six packs at goods-in. The falsified one scans as verified, because its serial is real — it was decommissioned 900 km away three weeks ago.
Open lab →Mean turnaround fell 22% and the slowest 5% doubled — and a fifth of that tail is medicines where lateness is the harm.
Open lab →A controlled-drug discrepancy with an obvious suspect. Follow the evidence instead — the nurse who cannot make the count balance is the one who reported it.
Open lab →Seven referrals graded by a screening system. Two of the three most dangerous are badged below HIGH — including a vancomycin level drawn at the wrong time.
Open lab →Six genotype results. A child who metabolises codeine ultra-rapidly is at risk of toxicity, not treatment failure — because codeine is a prodrug.
Open lab →A shift of portal messages and dashboards. One message is labelled 'not urgent' and describes chest pain in its second sentence.
Open lab →Work a ward round of eight orders ranked by an AI risk tool — and discover that the most dangerous prescription on the list is the one it scored lowest.
Open lab →Interactive patient scenarios with AI-scored decision points and feedback.
Open lab →Nine roles with enforced permissions on one synthetic record — timeline, encounters, lab and radiology workflows, bed board, insurance, discharge clearance and a full audit trail. The system will let you discharge without clearance, and record that it was you.
Open lab →Eight cases run as investigate → analyse → verify → decide. Three of the eight AI outputs are not supported by the record, including a previous admission the system invents for a handover.
Open lab →These are real decision points from three ALIZON AOS practicals. In every one the system in front of you — a screening tool, a completeness engine, a correct calculation — is confident, and wrong. See how you do.
Every module has a written textbook, authored in-house and issued to enrolled students. The contents of each are listed in full below, with an opening extract you can read now. The complete text opens in the student library once you are enrolled.
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