SAS Clinical Trial Programming
Become Industry-Ready with End-to-End CDISC, SDTM, ADaM & TLF Training
📘 Course Overview
This course is designed to equip you with the complete skill set required for SAS Clinical Trial Programming, covering Base & Advanced SAS, along with industry standards like CDISC, SDTM, ADaM, and TLF. Ideal for aspiring clinical programmers, biostatisticians, and life sciences professionals, this hands-on training prepares you for real-world projects and global regulatory submissions.
🧠 What You Will Learn
Fundamentals of SAS Programming (Base & Advanced)
Working with clinical trial data using industry-accepted best practices
Mastering CDISC Standards including SDTM and ADaM
Automating reports, listings, and statistical tables using Macros & PROC SQL
Generating regulatory-compliant outputs (TLFs) for FDA submissions
🧩 Course Modules
🔹 Module 1: Introduction to Clinical SAS
Clinical trial workflow and data flow
Regulatory bodies: FDA, EMA, ICH
Role of a SAS Clinical Programmer
🔹 Module 2: Base SAS Programming
DATA and PROC steps
Importing, cleaning, and transforming clinical data
Conditional logic, loops, formats, and functions
🔹 Module 3: Advanced SAS Techniques
PROC SQL for complex data queries
Macro programming for automation
Combining datasets, merging, and reshaping data
🔹 Module 4: CDISC Fundamentals
Introduction to CDISC, SDTM, and ADaM
Standards for clinical data submission
Annotated CRFs and metadata standards
🔹 Module 5: SDTM Implementation
Mapping raw data to SDTM domains (DM, AE, LB, VS, etc.)
Creating define.xml and reviewer’s guide
Validation using Pinnacle 21 (optional add-on)
🔹 Module 6: ADaM Development
ADaM dataset structure and traceability
Deriving variables for analysis
Compliance with ADaMIG and best practices
🔹 Module 7: TLF (Tables, Listings & Figures)
Creating tables using PROC REPORT
Listings with PROC PRINT and PROC SQL
Figures and graphing using ODS and PROC SGPLOT
Pharmacovigilance Software Training with AI Integration
Master Drug Safety Operations with Cutting-Edge AI Tools & Automation
📘 Course Overview
This course offers a comprehensive understanding of Pharmacovigilance (PV) processes along with hands-on training in modern PV software systems enhanced by Artificial Intelligence (AI). You will learn to manage adverse event reporting, regulatory submissions, safety databases, and signal detection — all integrated with AI-powered tools for automation and decision-making.
Ideal for life science graduates, pharmacists, clinicians, and IT professionals entering the drug safety domain.
🔍 What You Will Learn
Core principles of Pharmacovigilance and Drug Safety
Use of AI tools in adverse event detection and reporting
Hands-on training in PV software like Argus Safety, ARISg, VigiFlow
Automating case processing and narrative writing with AI & NLP
Signal detection and benefit-risk analysis using AI-driven analytics
Regulatory compliance (ICH E2B, MedDRA, GVP)
🧩 Course Modules
🔹 Module 1: Introduction to Pharmacovigilance
What is Pharmacovigilance?
History, Importance, and Regulatory Framework
Life cycle of a drug and role of PV
🔹 Module 2: Pharmacovigilance Workflow
Case intake and triage
Medical coding with MedDRA & WHO-DD
Seriousness assessment and expectedness
Narrative writing and causality assessment
🔹 Module 3: PV Software Systems
Overview of PV software: Oracle Argus, ARISg, VigiFlow, SafetyEasy
Case data entry and workflow navigation
Regulatory submission via E2B/XML
🔹 Module 4: AI Tools in Pharmacovigilance
Using NLP for automated case narrative generation
Signal detection using AI-based dashboards
Role of Machine Learning in adverse event pattern recognition
Integration with automation platforms (Power Automate, UiPath, Python scripts)
🔹 Module 5: Regulatory Guidelines & Compliance
Understanding ICH Guidelines (E2A to E2F)
Global PV regulations: US FDA, EMA, MHRA, CDSCO
GVP Modules and CIOMS Working Groups
Audits and Inspections – what to expect
⚙️ Tools & Technologies Covered
🧠 AI Platforms: ChatGPT, Google Vertex AI, Python NLP
💻 PV Software: Oracle Argus, ARISg, VigiFlow (Demo Access)
📊 Automation: Power Automate, UiPath (optional module)
📚 Databases & Coding: MedDRA, WHO Drug Dictionary
🎓 Certification & Support
Course Completion Certificate
Option for GVP Certification Prep Support
Resume & Interview Preparation
Placement Assistance (India & International)
✅ Who Should Join?
Pharmacy, Life Sciences, Nursing, MBBS graduates
Professionals looking to switch to drug safety roles
IT professionals interested in AI-PV integration
📞 Enroll Now – Start your career in AI-powered Pharmacovigilance
3.Clinical Data Management Software Training with AI
Master Clinical Trials Data Handling Using Modern CDM Systems & Artificial Intelligence
Course Overview
This program equips you with in-demand skills in Clinical Data Management (CDM) using leading CDM software and AI-powered tools. Learn to collect, validate, clean, and manage clinical trial data in compliance with global regulatory standards, while leveraging automation and machine learning to optimize data workflows.
Whether you're a clinical research professional or an IT graduate entering life sciences, this course prepares you for high-demand CDM roles in pharma, CROs, and healthcare analytics.
🧠 What You Will Learn
Foundations of Clinical Trials and Data Management Lifecycle
Hands-on use of popular CDM software: Medidata Rave, Oracle Clinical, OpenClinica
AI-based data validation, discrepancy management, and coding automation
CRF (Case Report Form) design and annotation
Query management using NLP and automation tools
Compliance with GCP, 21 CFR Part 11, and CDISC standards
🧩 Course Modules
🔹 Module 1: Introduction to Clinical Trials & CDM
Phases of clinical trials (Phase I-IV)
CDM process: data collection, validation, cleaning, database lock
Roles & responsibilities in a CDM team
🔹 Module 2: Clinical Data Management Software Training
Overview of EDC systems: Medidata Rave, Oracle Clinical, OpenClinica
Case Report Form (CRF) design & annotation
User roles, project setup, and data entry
Edit checks, discrepancy handling, and query resolution
Lab data handling and external data import
🔹 Module 3: AI Integration in CDM
Using AI/NLP to automate discrepancy generation and resolution
AI for medical coding (MedDRA, WHO-DD) automation
Machine Learning for predictive data cleaning
Robotic Process Automation (RPA) in CDM workflows
🔹 Module 4: Compliance & Standards
GCP (Good Clinical Practice)
21 CFR Part 11 (electronic records)
CDISC: SDTM basics for CDM professionals
Audit trails and data integrity
⚙️ Tools & Technologies Covered
CDM Software: Medidata Rave, Oracle Clinical, OpenClinica (Demo-based)
AI Tools: ChatGPT for NLP, Power Automate, Python (optional)
Medical Coding: MedDRA, WHO Drug Dictionary
Compliance Platforms: E-signatures, eCRFs, audit tracking
💼 Hands-On Projects
Realistic CRF creation and annotation
Edit check programming and testing
Discrepancy generation & resolution with AI
Final data validation and database lock simulation
🏆 Certification & Career Support
CDM Training Certificate
Guidance for certifications like ACDM, CCDM
Resume & Interview Prep
Career pathways into CDM, Clinical Research, and Data Science in Healthcare
✅ Who Should Enroll?
B.Pharm, M.Pharm, MBBS, BDS, Nursing, Life Sciences Graduates
Clinical Research Professionals and Coordinators
IT graduates aspiring to enter pharma analytics and automation
📞 Enroll Today and become a next-gen Clinical Data Manager using AI!
4.CRM with AI in Healthcare
Revolutionize Patient Engagement, Streamline Operations, and Personalize Healthcare with AI-Driven CRM
📘 Overview
In the fast-evolving world of healthcare, traditional Customer Relationship Management (CRM) systems are no longer enough. This program (or solution) introduces you to AI-powered CRM tailored for healthcare institutions — enabling better patient communication, appointment automation, EMR integration, marketing, and predictive insights.
Whether you're a hospital, clinic, diagnostics center, or healthcare tech provider, AI-driven CRM helps you deliver personalized, proactive, and data-backed care to every patient.
🧠 Key Features & Benefits
✅ AI-Enhanced Patient Management
360° patient view with real-time updates
Smart reminders and follow-ups powered by AI
Predictive care planning based on historical data
✅ Automation & Workflow Optimization
AI chatbots for appointment scheduling & FAQs
Automated alerts for medication, health check-ups
Integration with EMRs, HIS, and lab systems
✅ Predictive Analytics for Better Decision-Making
Disease risk modeling
Patient churn prediction
Intelligent segmentation for targeted campaigns
✅ Personalized Communication
AI-generated emails/SMS/WhatsApp based on patient journey
Voice assistants for elderly care
Feedback collection and sentiment analysis
🧩 Core Modules / Components
Patient Relationship Lifecycle
Lead capture, registration, onboarding
Post-treatment engagement
Family health tracking
AI Tools Integration
NLP chatbots (e.g., ChatGPT, Vertex AI)
Predictive analytics (TensorFlow, Azure ML)
RPA for repetitive tasks (e.g., Power Automate, UiPath)
CRM Platform Examples
Salesforce Health Cloud
Zoho CRM with healthcare plugins
Microsoft Dynamics 365 Healthcare Accelerator
Custom CRM with AI plug-ins (Python APIs, Zapier, etc.)
Security & Compliance
HIPAA & GDPR compliant workflows
Role-based access control
Audit logs and consent management
🩺 Use Cases in Healthcare
Hospitals: Automate admissions, manage discharges, and track readmissions
Clinics: Streamline appointment systems & patient feedback
Diagnostics Labs: Manage test reports, schedule sample pickups, communicate results
Telemedicine Platforms: Enhance virtual care delivery with CRM-backed AI support
Healthcare Marketing Teams: Run intelligent outreach campaigns and measure outcomes
🎓 For Training Programs (Optional Addition)
"Learn how to build, integrate, and manage an AI-powered CRM for healthcare. Ideal for healthcare professionals, IT consultants, and CRM admins."
✅ Who Should Use or Learn This?
Hospitals & Clinics (Operations, IT, Marketing Teams)
HealthTech Startups & Entrepreneurs
Healthcare CRM Admins & Consultants
Students in Health Informatics / AI in Healthcare
📞 Ready to Transform Your Healthcare Practice?
💡 Book a Demo | 🎓 Join the Training | 🔧 Request Custom CRM Development
5.Regulatory Affairs in Healthcare Using AI
Accelerate Compliance, Reduce Risk, and Streamline Submissions with AI-Powered Regulatory Intelligence
📘 Overview
Navigating regulatory landscapes in healthcare and pharmaceuticals requires precision, speed, and compliance with ever-evolving global standards. AI-powered Regulatory Affairs transforms this process by automating documentation, analyzing regulatory databases, predicting compliance risks, and simplifying dossier submissions.
This training/service equips professionals with the knowledge and tools to manage regulatory submissions using artificial intelligence, automation, and data-driven decision support.
🔍 What You Will Learn / Offer
✅ For Training Programs:
Overview of regulatory frameworks: US FDA, EMA, CDSCO, MHRA, TGA
Understanding CTD/eCTD formats and regulatory dossier preparation
Using AI tools for:
Document summarization and auto-classification
Regulatory trend analysis
Submission tracking and lifecycle management
NLP-based automation for:
Literature screening
Labeling and safety updates
Regulatory intelligence gathering
✅ For AI-Based Service or Product:
End-to-end eCTD submission automation
AI models for early warning on regulatory changes
Integration with global databases (FDA Orange Book, EMA SPOR, etc.)
Smart dashboards to monitor timelines, agency communications, and deadlines
🧩 Key Modules or Features
Regulatory Frameworks and Guidelines
ICH (Q, E, M, S series), GxP compliance
CTD & eCTD structures
Submission requirements by region (FDA, EMA, India CDSCO, etc.)
AI Tools for Regulatory Affairs
NLP engines for scanning scientific literature
Document intelligence platforms (Microsoft Syntex, ChatGPT, SciBite)
ML for trend prediction and impact assessment
AI auto-tagging and classification of submission documents
Automated Workflows
Labeling updates and comparisons using AI
Pharmacovigilance compliance automation (tie-in with QMS & PV tools)
Real-time dashboarding and notification systems
Data Integrity & Compliance
21 CFR Part 11 & Annex 11 AI compliance
Audit trail generation and retention policies
Risk-based monitoring
💡 Use Cases
Regulatory Submissions: Faster compilation and validation of CTD/eCTD
Labeling Management: AI-driven SmPC/PI comparison & change control
Global Compliance Monitoring: Automate scanning of regulatory portals
Regulatory Intelligence: Stay ahead with AI-curated updates and country-specific requirements
Lifecycle Management: Track all communications, renewals, and commitments
🎓 Certification & Support (for training)
Regulatory Affairs with AI Certificate (endorsed by [Your Org/Partner])
Interview prep, resume support, and career guidance
Optional add-on: Hands-on with tools like Veeva Vault RIM, MasterControl, OpenAI APIs
✅ Who Should Enroll / Use This?
Regulatory Affairs Professionals
Pharma & Biotech Companies
HealthTech Product Managers
Clinical Research and Pharmacovigilance Experts
AI Developers in Health Compliance
📞 Ready to revolutionize your Regulatory Affairs with AI?
🖥️ Join our training | 📊 Book a Demo | 🧠 Customize an AI Tool for Your Team
6.Image Processing in Healthcare Using AI
Revolutionizing Medical Diagnosis, Imaging, and Decision Support through AI-Powered Vision Systems
📘 Overview
Medical imaging is a cornerstone of modern diagnostics. With the integration of Artificial Intelligence (AI) and image processing technologies, healthcare professionals can now detect, diagnose, and treat diseases with unprecedented accuracy and speed.
This program or service teaches how to leverage AI-powered image analysis in radiology, pathology, ophthalmology, and dermatology, using state-of-the-art tools like CNNs, deep learning, and medical imaging libraries.
🔍 What You Will Learn / Offer
✅ For Training Programs:
Fundamentals of image processing and medical imaging formats (DICOM, NIfTI)
Building AI models for:
Tumor detection in X-rays, MRIs, CT scans
Diabetic retinopathy from retina images
Skin lesion classification
Hands-on training in:
OpenCV, TensorFlow, PyTorch, and MONAI (Medical Open Network for AI)
Pre-trained models for segmentation, classification & object detection
Regulatory considerations: FDA/CE approvals for AI in imaging
✅ For AI Solution or Product:
Automated image segmentation and anomaly detection
PACS integration with AI APIs
Real-time decision support for radiologists and clinicians
AI dashboards for image classification and prediction analytics
Custom AI model deployment for hospitals or diagnostic centers
🧩 Core Modules / Components
Medical Image Formats & Preprocessing
Understanding DICOM, NIfTI, PNG, TIFF formats
Image enhancement, noise reduction, normalization
Annotating medical images for training datasets
AI & Deep Learning Techniques
Convolutional Neural Networks (CNNs)
Transfer learning with ResNet, U-Net, VGG
Segmentation, classification & object detection pipelines
Application Areas
Radiology: AI for X-ray, CT, MRI interpretation
Pathology: Whole-slide image analysis
Ophthalmology: Retinal image grading (e.g., diabetic retinopathy)
Dermatology: Skin cancer detection using dermatoscopic images
Deployment & Integration
Using cloud platforms (AWS, Azure, Google AI) for model hosting
Integration with EMR/PACS systems
Compliance with medical device regulations
⚙️ Tools & Technologies Covered
Libraries: OpenCV, PyTorch, TensorFlow, Keras, SimpleITK, MONAI
Frameworks: FastAI, Detectron2, scikit-image
Data Sources: NIH ChestX-ray14, LIDC-IDRI, HAM10000, DRIVE
Platforms: Google Colab, Jupyter Notebook, Docker for deployment
🎓 Certification & Outcomes (For Training)
Certificate in AI-Based Image Processing in Healthcare
Hands-on project portfolio
Resume & interview support for AI imaging roles
Guidance to publish research papers or enter hackathons
👩⚕️ Who Should Join / Use This?
Healthcare AI Developers
Radiologists, Pathologists, Dermatologists
Bioinformatics & Data Science Professionals
HealthTech Startups, Diagnostic Centers, Hospitals
Students in Biomedical Engineering or AI in Medicine
📞 Harness the power of AI in Medical Imaging Today!
🧠 Join the Course | 📊 Book a Demo | ⚙️ Build a Custom Imagin
7.Bioinformatics in Healthcare Using AI
Harnessing Artificial Intelligence to Decode Biological Data for Precision Medicine and Advanced Research
📘 Overview
Bioinformatics is the engine behind genomic medicine, drug discovery, and personalized healthcare. With Artificial Intelligence (AI), bioinformatics has evolved into a powerful tool for analyzing massive biological datasets — from DNA sequences to protein structures.
This course/service equips you to apply AI and machine learning in genomics, transcriptomics, proteomics, and healthcare data, enabling smarter diagnosis, therapy design, and research acceleration.
🔍 What You Will Learn / Offer
✅ For Training Programs:
Fundamentals of bioinformatics: DNA/RNA/protein sequence analysis
AI/ML applications in gene prediction, mutation analysis, and biomarker discovery
Hands-on projects using:
Python, Biopython, Scikit-learn, TensorFlow, and BioPerl
Real-world datasets: NCBI, Ensembl, TCGA, GEO
Tools for drug target discovery and pathway analysis
AI in personalized medicine and genomics
✅ For AI-Enabled Solutions or Services:
Deep learning for genomic pattern recognition
Variant calling and mutation impact analysis using AI
Integration of EHR + genomics for precision medicine
Predictive modeling of disease risks using omics data
AI in vaccine design, protein folding, and drug repurposing
🧩 Core Modules / Features
Bioinformatics Essentials
Sequence alignment, BLAST, FASTA/FASTQ formats
Genome annotation and SNP detection
Next-Gen Sequencing (NGS) pipeline overview
AI & Machine Learning Techniques
Supervised & unsupervised learning for biological data
Neural networks for mutation classification
Clustering, PCA, t-SNE for expression analysis
Deep learning for protein structure prediction (e.g., AlphaFold)
Applications in Healthcare
Genomics: AI for identifying disease-causing mutations
Transcriptomics: AI in gene expression profiling
Proteomics: Protein-protein interaction modeling
Pharmacogenomics: AI for drug-gene interaction predictions
Precision Oncology: Tailored cancer treatment using omics data
Tools & Platforms
Languages/Libraries: Python, Biopython, R, BioConductor, TensorFlow
Databases: NCBI, UniProt, PubMed, Ensembl, KEGG, TCGA
Cloud & Visualization: Galaxy, Jupyter, Power BI for genomics
🎓 Certification & Outcomes (Training Focus)
Certificate in AI-Powered Bioinformatics in Healthcare
Project work on real clinical datasets
Preparation for roles in Genomics AI, Bioinformatics Analyst, Clinical Data Scientist
Research guidance and paper publishing support
👩⚕️ Who Should Join / Use This?
Life Science Graduates, BSc/MSc in Bioinformatics, Genetics, Biotech
Medical researchers, biostatisticians, and data scientists
Pharma & Healthcare R&D teams
Tech startups and companies working in Genomics, AI, and Drug Discovery
Students seeking careers in Precision Medicine and Computational Biology
📞 Join the Future of Genomic Medicine and AI Today!
🧠 Enroll in the Course | ⚙️ Request Custom AI Bioinformatics Tools | 🔍 Book a Demo for Labs or Hospitals
8.Data Analytics in Healthcare Using AI
Unlock Actionable Insights, Improve Patient Outcomes, and Drive Smart Decisions with AI-Powered Healthcare Analytics
📘 Overview
The healthcare industry is generating enormous volumes of data — from Electronic Health Records (EHRs) to lab results and insurance claims. Artificial Intelligence (AI) and advanced analytics turn this raw data into powerful insights that improve clinical outcomes, reduce operational costs, and support real-time decision-making.
This program or solution introduces you to how data analytics combined with AI is revolutionizing healthcare systems, predictive care, and population health management.
🔍 What You Will Learn / Offer
✅ For Training Programs:
Fundamentals of healthcare data (EHR, ICD codes, lab reports, claims)
AI/ML models for predictive analytics and risk stratification
Data visualization using Power BI, Tableau, and Python
Natural Language Processing (NLP) for analyzing clinical notes
Real-time data dashboards for hospital operations and patient care
✅ For Healthcare AI Services / Products:
Patient outcome prediction models (e.g., readmission risk, sepsis alerts)
AI-powered population health analytics
Automated claims and billing pattern detection
Disease surveillance and early outbreak prediction
Integration with hospital systems (EMR, HIS, LIS, PACS)
🧩 Core Modules / Components
Healthcare Data Landscape
EHR/EMR data structure
Clinical coding systems: ICD, CPT, SNOMED
HL7, FHIR data exchange standards
Data privacy and HIPAA/GDPR compliance
AI & Machine Learning for Analytics
Supervised learning: diagnosis prediction, patient stratification
Unsupervised learning: clustering patient behavior or treatment patterns
Time-series analysis for vital signs & trends
Predictive modeling for chronic disease management
Data Engineering & Tools
ETL processes for healthcare data
Tools: SQL, Python, Pandas, Scikit-learn, Power BI
Cloud analytics: AWS HealthLake, Azure Synapse, Google BigQuery
Advanced Applications
Clinical Decision Support Systems (CDSS)
Patient Journey Mapping
Revenue Cycle Management Analytics
Quality of Care & Performance Monitoring
📈 Real-World Use Cases
Hospitals: Bed occupancy prediction, ER triage analysis, KPI dashboards
Insurance Firms: Fraud detection, claims automation
Public Health: Epidemiological data modeling and forecasting
Pharmaceuticals: Trial analytics, pharmacoeconomic evaluations
Telemedicine: Behavioral pattern analytics and engagement optimization
🎓 Certification & Benefits (For Learners)
Certificate in AI-Driven Healthcare Data Analytics
Hands-on projects using real or synthetic datasets
Career readiness in roles like Healthcare Data Analyst, AI Consultant, BI Developer
Access to case studies, tools, and resume-building sessions
👨⚕️ Who Should Join / Use This?
Healthcare Analysts, IT Professionals, Data Scientists
Hospital Administrators & Quality Managers
HealthTech Startup Founders
Students in Data Science, Healthcare Informatics, or Public Health
Pharma & Insurance Professionals
📞 Transform Your Healthcare Ecosystem with AI Analytics!
🎓 Join the Training | ⚙️ Request AI Analytics Tool | 📊 Book a Healthcare Data Demo
9.Electronic Health Records (EHR) System in Healthcare Using AI
Redefining Patient Care with Smart, AI-Enabled Electronic Health Records
📘 Overview
Electronic Health Records (EHRs) are the backbone of modern healthcare. With the power of Artificial Intelligence (AI), EHR systems are evolving into intelligent platforms that go beyond data storage — offering predictive insights, automation, and clinical decision support.
This course/service focuses on how AI transforms EHR systems to enhance care delivery, patient safety, and hospital efficiency.
🔍 What You Will Learn / Offer
✅ For Training Programs:
Introduction to EHR architecture and components
AI integration in EHR workflows: diagnosis support, patient alerts, and auto-documentation
Hands-on with FHIR (Fast Healthcare Interoperability Resources) and HL7 standards
AI use cases in clinical NLP, risk scoring, and patient profiling
Privacy, security, and regulatory frameworks (HIPAA, GDPR)
✅ For AI-Powered EHR Services or Solutions:
Smart clinical decision support (CDSS) embedded into EHRs
AI-based voice and text transcription for physicians
Predictive analytics for hospital readmission and patient deterioration
Patient engagement tools powered by conversational AI
Integration with IoT, lab, pharmacy, and radiology systems
🧩 Core Modules / Solution Features
EHR System Fundamentals
Patient record structure: demographics, vitals, history, labs, medication
Structured vs unstructured data in EHR
FHIR/HL7-based interoperability between healthcare systems
AI in Clinical Data Processing
Natural Language Processing (NLP) for doctor notes and discharge summaries
Machine learning models for risk prediction (e.g., diabetes, heart failure)
Voice-enabled data entry and digital scribes
Decision Support & Automation
Alerts for drug interactions and allergy risks
AI recommendations for diagnostic tests and treatment plans
Real-time vitals monitoring with AI-driven alerting
Patient Engagement & Portals
AI chatbots for scheduling, reminders, and FAQ
Personalized health dashboards and care plans
Multilingual support for diverse patient populations
Compliance & Security
HIPAA, GDPR, and Indian NDHM (ABDM) compliance
Role-based access controls and audit logging
AI for anomaly detection and cybersecurity in EHR access
⚙️ Tools & Technologies
AI/ML: TensorFlow, Scikit-learn, SpaCy, BERT for Clinical NLP
Integration: HL7, FHIR APIs, SMART on FHIR
Platforms: Epic, Cerner, OpenEMR, Medplum, Google Health APIs
Deployment: Cloud (AWS HealthLake, Azure Health Data Services) or On-Premise
🎓 Certification & Benefits (If a Course)
Certification in AI-Enabled EHR Systems
Projects on building intelligent EHR modules
Pathways to roles like EHR Consultant, Health IT Analyst, Clinical AI Developer
👩⚕️ Who Should Join / Use This?
Health IT Professionals & EHR Admins
Hospital CIOs & Clinical Informatics Teams
Developers of health platforms and hospital ERP systems
Medical practitioners interested in health tech innovation
Students in Health Informatics or Digital Health
📞 Make Your EHR System Smarter with AI!
🧠 Enroll in Training | ⚙️ Deploy a Smart EHR Solution | 📊 Request a Live Demo
10.Course Title: Laboratory Information Management Systems in Healthcare using AI
Duration: 2 months (Flexible: Online/Hybrid Mode)
Level: Beginner to Intermediate
Mode: Instructor-led + Hands-on Projects + AI Tool Demos
📘 Course Modules & Duration
Week 1: Introduction to LIMS in Healthcare (6 hrs)
Overview of LIMS and its role in clinical and diagnostic labs
Key components: Sample tracking, test results, reporting
Regulatory compliance (HIPAA, NABL, FDA)
Types of LIMS (Clinical, Research, Diagnostic, Pharma)
Week 2: Digital Lab Operations & Data Management (6 hrs)
Sample lifecycle & workflow automation
Electronic Lab Notebooks (ELN) integration
Inventory and reagent tracking systems
Barcode & RFID integration in labs
Week 3: AI in Laboratory Data Management (6 hrs)
AI for sample classification and test recommendation
Predictive analytics in lab diagnostics
Natural Language Processing (NLP) in lab reports
Case Study: AI-driven pathology reporting
Week 4: Integration with Healthcare Ecosystem (6 hrs)
LIMS and Electronic Health Records (EHR)
Interoperability: HL7, FHIR standards
Integration with diagnostic devices and imaging systems
Security and data privacy in AI-LIMS workflows
Week 5: AI Tools and Software Platforms (6 hrs)
Open-source LIMS with AI capabilities (e.g., Bika LIMS, OpenELIS)
Commercial platforms (STARLIMS, LabWare with AI plugins)
AI model integration: Python, TensorFlow, ML pipelines
Hands-on: Create a basic AI model for lab result prediction
Week 6: Capstone Project & Assessment (6 hrs)
Project: Design an AI-integrated LIMS prototype for a diagnostic lab
Industry use-case simulation
Evaluation, feedback, and certification
Career & freelance opportunities in AI-LIMS
🎓
11.Health Informatics in Healthcare using AI
Master the Integration of AI in Digital Health Information Systems
📅 Total Duration: 2 Months(Flexible for Fast-track or Weekend Batches)
Mode: Online / Hybrid / Self-paced / Instructor-led
Level: Intermediate to Advanced
📘 Course Modules & Duration
Week 1: Introduction to Health Informatics & Digital Health
Overview of Health Informatics
Evolution of Electronic Health Records (EHR)
Standards in Health IT (HL7, FHIR, SNOMED CT)
Stakeholders & Ecosystem in Digital Health
⏱️ Duration: 6 Hours
Week 2: Foundations of Artificial Intelligence in Healthcare
Basics of AI, ML, and Deep Learning
Use cases of AI in healthcare: diagnosis, triage, automation
Natural Language Processing (NLP) in clinical documentation
Overview of AI tools (e.g., Python, Scikit-learn, TensorFlow, HuggingFace)
⏱️ Duration: 8 Hours
Week 3: AI in Clinical Data Management
Data cleaning, integration & interoperability
Predictive analytics using EHR data
Case Study: AI in Population Health Management
⏱️ Duration: 6 Hours
Week 4: AI Applications in Health Informatics
Clinical Decision Support Systems (CDSS)
AI in Medical Imaging and Diagnostics
AI for Remote Patient Monitoring & Wearables
Chatbots and Virtual Health Assistants
⏱️ Duration: 8 Hours
Week 5: Ethical, Legal & Regulatory Framework
Data privacy laws (HIPAA, GDPR, DISHA in India)
AI Ethics in healthcare
Bias, transparency, and explainability in AI models
Responsible AI frameworks
⏱️ Duration: 5 Hours
Week 6: Hands-on Projects & Case Studies
AI-enabled EHR analytics dashboard (using Python/Pandas)
Health chatbot using NLP
Predictive model for hospital readmission
Capstone project presentation
⏱️ Duration: 10 Hours
12.Course Title:
Document Management Systems (DMS) for Regulatory Submissions using AI in Healthcare
🎯 Course Objective:
Equip healthcare professionals, regulatory associates, and compliance teams with the skills to efficiently manage and automate regulatory documentation workflows using AI-driven Document Management Systems (DMS).
🕒 Total Duration: 2 Months
Format: 3 Sessions/Week x 2 Hours = 6 Hours/Week
📚 Course Modules & Duration
Week 1: Introduction to Regulatory Submissions and DMS (6 Hours)
Overview of Regulatory Requirements (FDA, EMA, CDSCO, etc.)
Role of Document Management in Compliance
Types of Regulatory Submissions (eCTD, NDA, IND, ANDA)
Introduction to DMS Tools (Veeva Vault, MasterControl, etc.)
Importance of AI in DMS
Week 2: Fundamentals of Document Lifecycle Management (6 Hours)
Document Creation, Versioning, and Archiving
Metadata, Tagging, Indexing, and Templates
Access Control and Audit Trails
Standard Operating Procedures (SOPs) in DMS
AI for Document Classification and Tagging
Week 3: AI Integration in Document Management Systems (6 Hours)
Natural Language Processing (NLP) for Document Analysis
Machine Learning for Smart Search and Auto-tagging
AI-based Risk Assessment and Compliance Monitoring
Automated Document Summarization
Case Study: AI-Enhanced Veeva Vault Implementation
Week 4: Regulatory Submission Preparation using AI (6 Hours)
eCTD Structure & Compilation
AI Tools for Submission Readiness Review
Data Validation and Consistency Checks
Conversion and Formatting using AI tools (PDF, XML)
Regulatory Timelines and Project Planning with AI Support
Week 5: Hands-on Practice with Tools (6 Hours)
Working with DMS Tools: Veeva Vault, MasterControl (Demo/Simulation)
Using AI for:
Document parsing
Duplicate detection
Redaction
Real-time tracking and version control
Week 6: Compliance, Audit, and Future Trends (6 Hours)
21 CFR Part 11, GxP Compliance in Document Handling
Audit Preparation and Regulatory Inspections
Data Security and AI Ethics in DMS
Future of AI in Regulatory Tech (RegTech)
Final Capstone Project: Create a mock AI-enabled DMS regulatory submission package
👨💼 Who Should Attend?
Regulatory Affairs Professionals
Clinical Research Associates
Pharmacovigilance Experts
Quality & Compliance Managers
Health Informatics Graduates
📜 Certification Provided
Upon successful completion with project submission.
13.Course Title:
Genomics & Precision Medicine Tools Using AI in Healthcare
🎯 Course Objective:
To provide a comprehensive understanding of how Artificial Intelligence (AI) is transforming genomics and enabling precision medicine, from genetic data analysis to clinical decision-making and personalized treatment planning.
🕒 Total Duration: 2.5 Months
Format: 3 Sessions/Week × 2 Hours per Session = 6 Hours/Week
📚 Course Modules & Duration
Week 1: Foundations of Genomics & Precision Medicine (6 Hours)
Human Genome Project & Basics of Genetics
DNA, RNA, SNPs, and Gene Expression
Introduction to Precision Medicine
Applications in Oncology, Cardiology, Rare Diseases
Week 2: Role of AI in Genomics (6 Hours)
How AI supports Genomic Research
Machine Learning for Variant Calling
Deep Learning in Genomic Sequence Analysis
Case Study: Google's DeepVariant
Week 3: Genomic Data Analysis Techniques (6 Hours)
Next-Generation Sequencing (NGS) Overview
Genomic Data Formats (FASTQ, BAM, VCF)
Tools: GATK, Bioconductor, IGV
AI Models for Pattern Recognition in Genomic Data
Week 4: AI in Disease Risk Prediction (6 Hours)
Polygenic Risk Scores and AI
AI for Early Detection of Cancer and Genetic Disorders
Disease Association Studies using ML
Precision Diagnostics with AI Algorithms
Week 5: Drug Discovery & Pharmacogenomics (6 Hours)
AI in Target Identification & Validation
Personalized Drug Response Prediction
Pharmacogenomic Markers
Applications in Drug Repurposing using AI
Week 6: Clinical Decision Support Systems (CDSS) (6 Hours)
AI Integration in EHRs for Genomic Decision-Making
Case Study: IBM Watson for Genomics
Ethical, Legal, and Regulatory Considerations
Role of AI in Clinical Trials with Genomic Data
Week 7: AI Tools, Platforms & Hands-on Projects (6 Hours)
Hands-on: Use of AI Tools (TensorFlow, scikit-learn) on genomic datasets
Platforms: NVIDIA Clara, DNAnexus, Illumina BaseSpace
Practical: Build a simple AI pipeline for SNP classification
Week 8: Future Trends & Capstone Project (6 Hours)
Multi-omics Data Integration
AI in Population Genomics & Public Health
Final Capstone Project: Build and present an AI-based precision medicine case study or prototype
👨💻 Who Should Enroll?
Bioinformatics & Life Science Graduates
Healthcare IT Professionals
Medical Researchers & Clinicians
Data Scientists & AI Enthusiasts in Healthcare
📜 Certification Awarded on Completion
Includes capstone project and assessment-based evaluation.
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