Full Domain Training — Healthcare & Life Sciences (HLS)
Gain domain fluency across clinical systems, payers, life sciences, population health, and AI-driven health solutions. Practical, compliance-aware training for technologists and healthcare professionals.
Program Snapshot
- • EHR interoperability (FHIR, HL7, DICOM) and clinical data engineering
- • Payer systems, claims pipelines, and fraud/anomaly detection
- • Pharma data standards (CDISC) and clinical trial integrations
- • Population health analytics, ML for clinical risk, and IoT/edge telemetry
Overview
Healthcare and Life Sciences require both clinical understanding and robust data engineering to deliver safe, compliant, and effective digital solutions. This program blends domain lectures, standards training, and engineering labs so teams can design, build, and operate health systems and data products with confidence.
Who should attend
Data engineers, ML practitioners, healthcare IT professionals, product managers, and clinicians interested in data/AI transformation.
Core benefits
Understand EHR data, implement FHIR integrations, apply ML to clinical data responsibly, and deliver compliant analytics & pipelines.
Prerequisites
Basic programming (Python preferred), SQL, and familiarity with healthcare concepts helpful but not required.
Program Tracks — Core Modules
Track 1 — Healthcare Systems & Clinical Workflows
- Care models: hospitals, clinics, ambulatory care, telehealth
- EMR/EHR fundamentals: Epic, Cerner concepts, openEHR
- Interoperability: HL7v2, FHIR resources, DICOM imaging basics
- Patient journey mapping, scheduling, and encounter lifecycle
- Case Study: Hospital patient flow optimization
Track 2 — Health Insurance & Payer Operations
- Payer architecture: policy admin, claims adjudication, provider networks
- EDI and X12 transactions, claim formats, and remittance
- Risk adjustment, utilization management, and prior authorization flows
- Case Study: Claims pipeline with anomaly detection
Track 3 — Life Sciences & Pharma Data
- Drug discovery & clinical development lifecycle
- Data standards: CDISC (SDTM, ADaM), SEND for nonclinical
- Pharmacovigilance, regulatory submissions, and GxP basics
- Case Study: Clinical trial data integration in Snowflake
Track 4 — Healthcare Analytics & Population Health
- Data warehousing patterns for health systems (Snowflake, Databricks)
- Population health metrics, cohort analysis, and SDoH data
- Predictive models: readmission risk, utilization forecasting
- Case Study: Readmission risk prediction and intervention planning
Track 5 — AI, Cloud & Digital Transformation
- AI in healthcare: diagnostics support, medical imaging, clinical NLP
- IoT & edge: wearables, remote patient monitoring, telemetry ingestion
- Security & compliance: HIPAA, GDPR, data de-identification, audit trails
- Case Study: Patient engagement platform with AI-driven personalization
Hands-on Labs & Projects
Each lab produces deployable artifacts, documented pipelines, and compliance-aware templates.
Clinical — Patient Journey Dashboard
Integrate sample EHR data (FHIR resources), clean and model clinical events, and build a dashboard for operational KPIs (throughput, LOS, wait times).
Insurance — Claims Analytics Platform
Build an ETL pipeline for claims (X12/EDI simulation), run anomaly detection, and surface suspicious claims for review.
Life Sciences — Clinical Trials Integration
Create a CDISC-compliant ingestion pipeline, validate SDTM datasets, and store trial data in Snowflake for analysis.
Population Health — Readmission Risk Model
Feature engineering on clinical and SDoH data, train a risk model (MLflow), and produce intervention lists for care managers.
AI/IoT — Wearable Monitoring & Alerts
Stream wearable telemetry, detect anomalies in near real-time, and trigger clinical alerts with audit logging and privacy filters.
Capstone — HLS PoV & Executive Briefing
Design a PoV for a hospital or pharma unit: data products, compliance plan, deployment roadmap, and estimated ROI.
Deliverables & Certification
- FHIR/EHR integration templates and FHIR sandbox exercises
- CDISC pipeline templates and Snowflake notebooks
- Clinical ML notebooks, MLflow experiments, and model governance checklist
- Regulatory & compliance playbook (HIPAA, GxP, GDPR considerations)
- Capstone PoV slides, technical appendix, and Yukti Certified HLS Professional badge
Pricing & Delivery Options
Self-paced
Module videos, lab guides, and notebooks — recommended 10–16 week timeline.
Cohort (Instructor-led)
12-week live cohort with industry case studies, mentorship, and capstone review.
Enterprise
Private cohorts, tailored labs, integration with hospital/pharma systems, and on-site workshops.
Request Info / Enroll
Tell us about your organization and use cases — we'll respond with a tailored syllabus, pricing, and timeline for pilots or cohorts.