PRACTICE LABS
Learn by doing — in working labs.
Hands-on environments with synthetic patients you can't break. Every lab runs at your level: a guided visual mode for beginners, a pro mode when you're ready.
All data is synthetic · nothing real, ever
Digital Health & Informatics
14 labsThe core track: how modern healthcare moves, stores and reasons over information — standards, terminologies, integration, clinical decision support, and the data & research methods behind evidence-based care. Leads to the SIDHI certification.
Clinical NLP · Annotation & Extraction
Read a synthetic clinical note and label the medicines, doses, problems and lab values — build the gold standard, then review what the AI extracts.
FHIR Mapper · HL7 v2 → FHIR R4
Map a hospital admit message into a FHIR Patient resource, field by field, and validate it against a profile. Visual mapper or pro editors.
HL7 v2 Message Explainer
Paste any raw HL7 v2 message and get a plain-English, segment-by-segment breakdown of every field — the fast way to learn the format.
Shabda · Terminology & Code Lookup
Look up any clinical concept — SNOMED CT, ICD-10-CM, ICD-11, LOINC — and trace its crosswalks, hierarchy and coding rules. Switch to Pro mode to reveal all billable codes.
ICD-10-CM Clinical Coding
Work through 8 realistic clinical scenarios and assign the most specific ICD-10-CM code. Guided mode gives hints; Pro mode scores you without help.
Clinical Integration Engine
Trace a message through a hospital integration engine — routing rules, transformation steps, ACK/NACK handling, and multi-destination delivery. The plumbing that connects hospital systems.
CDS Hooks · Decision Support
Author a CDS service response — cards, suggestions, and feedback links — triggered by real EHR hook events. Understand how decision support fires at the point of care.
FHIR API Sandbox
Make live FHIR REST calls against a synthetic server — GET a Patient, POST an Observation, run a search, create a Bundle transaction. See the request and response in full.
Health Data Analytics Lab
Write real SQL against a synthetic patient dataset. Query across 5 tables — patients, encounters, diagnoses, vitals, and lab results — to answer clinical questions. Guided mode scaffolds the query; Pro mode is a blank editor.
Health Data Science Lab
Write Python with pandas and matplotlib against a synthetic 20-patient hospital dataset — all running in your browser via WebAssembly. Five scenarios from demographics to length-of-stay analysis. Guided mode provides starter code; Pro mode is a blank slate.
Biostatistics Lab
Analyse a synthetic randomized controlled trial with Python, scipy and statsmodels — running in your browser. Five scenarios: descriptive statistics, the independent t-test, chi-square, correlation & linear regression, and Kaplan-Meier survival analysis. Guided mode provides starter code; Pro mode is a blank slate.
Epidemiology & EBM Lab
Turn study data into evidence with Python — running in your browser. Five scenarios: disease frequency, measures of association (RR / OR / NNT), diagnostic-test evaluation (sensitivity, specificity, ROC/AUC), confounding & Mantel-Haenszel adjustment, and an evidence-based-medicine appraisal (ARR / RRR / NNT with confidence intervals).
Data Harmonization · OMOP CDM
Harmonize messy source data into the OMOP Common Data Model — the foundation of real-world-evidence and pharma research. Map local codes to standard concepts, build a patient cohort, run drug–condition queries, and compute prevalence across standard concepts. Synthetic data only; the CDM structure is open.
Pharmacovigilance · MedDRA
Detect drug-safety signals with Python on a synthetic spontaneous-reporting database. Navigate the MedDRA hierarchy (PT → SOC), code and roll up adverse events, and compute disproportionality (PRR / ROR) with the classic signal criteria. Synthetic MedDRA codes only — the dictionary is licensed.
Life & Biomedical Sciences
2 labsAn adjacent track for computational skills in the life and biomedical sciences — sequence analysis and biomedical signal processing. For biotech, life-sciences and biomedical-engineering learners.
Bioinformatics Lab
Work with DNA and protein sequences in Python — GC content, reverse complement, transcription and translation, FASTA parsing and pairwise alignment — entirely in the browser. Built for biotech, life-sciences and bioinformatics students.
Bioinstrumentation Lab
Process biomedical signals in Python with scipy.signal: generate and filter a synthetic ECG, detect heartbeats, and analyse the frequency spectrum with the FFT. Built for biomedical-engineering students.