Generative Ai For Healthcare Data Analyst & Professionals

1000+ Prompts Mapping Generative AI Across the Clinical & Healthcare Data Workflow

What you'll learn

Understand the core concepts of Generative AI, large language models (LLMs), and their applications in healthcare.

Access a 1000+ expert-level prompts tailored to healthcare data analysis tasks.

Distinguish between structured, unstructured, and imaging data types in clinical settings.

Design and implement instructional vs. analytical prompts for real-world medical use cases.

Apply zero-shot, one-shot, and few-shot prompting techniques to clinical datasets.

Build multi-step AI workflows using prompt chaining for tasks like documentation and triage.

Generate readable summaries from patient visit notes and electronic health record (EHR) narratives.

Auto-draft discharge summaries, SOAP notes, and clinical progress reports using AI.

Forecast hospital readmission risks from historical patient data using AI prompts.

Generate synthetic patient datasets for privacy-safe training and model validation.

Write AI-generated risk narratives, compliance notes, and audit-ready documentation.

Auto-label clinical text with disease terms, medications, and procedural entities using prompt-based methods.

Identify outliers, missing values, and anomalies in large-scale health datasets using generative techniques.

Create and deploy health analytics chatbots and multi-turn medical dialogue interfaces.

Convert natural language to SQL to query healthcare databases and patient records.

Generate HL7/FHIR-compliant messages, pre-authorization letters, and insurance claims with minimal input.

Produce clinical documentation artifacts such as PowerPoint decks, KPI dashboards, and markdown reports.

Translate complex AI output into clear, clinician-friendly narratives for operational trust.

Requirements

Basic Knowledge of Healthcare System

Description

The “Generative AI for Healthcare Data Analyst & Professionals” course offers a comprehensive, practical exploration of how modern AI systems like LLMs can transform clinical data workflows, documentation, compliance, and decision-making in healthcare environments. Starting with foundational knowledge, learners are introduced to what Generative AI is, the architecture of models like GPT, and how they process structured, unstructured, and multi-modal data—from tabular EHR entries to physician notes and imaging metadata. The course then delves into the practical application of instructional and analytical prompts tailored to medical contexts, emphasizing advanced strategies like zero-shot, one-shot, and few-shot prompting for various use cases, including patient segmentation, SOAP note generation, and readmission forecasting.Participants will learn how to chain prompts together for end-to-end task automation, from summarizing visit notes to drafting discharge summaries. Tools such as LangChain, LlamaIndex, and Azure OpenAI Studio are introduced to operationalize these capabilities in clinical data pipelines. A strong emphasis is placed on using Generative AI for healthcare reporting and documentation, such as generating HL7/FHIR messages, insurance claims, pre-authorization letters, audit narratives, markdown summaries, PowerPoint presentations, and KPI dashboards. Additional focus areas include synthetic data generation for model training, risk prediction narratives, compliance report generation, and missing value imputation through AI.In the final modules, learners will build real-world applications—such as multi-turn medical dialogue systems, natural language to SQL converters, and AI-powered health analytics chatbots—culminating in over 1000+ expertly crafted prompt examples for immediate use. By the end of the course, learners will be equipped to safely, ethically, and effectively apply Generative AI tools across the healthcare data lifecycle, improving workflow efficiency, clinical collaboration, documentation accuracy, and data interpretability.

Overview

Section 1: Introduction to Generative AI in Healthcare

Lecture 1 What is Generative AI? Concepts, Models, and Modalities

Lecture 2 Role of Generative AI in Healthcare Data Workflows

Section 2: Download Files

Lecture 3 Dataset File

Lecture 4 Prompts Used in Live Demos

Section 3: Foundations of Prompt Engineering for Healthcare

Lecture 5 Instructional vs. Analytical Prompts in Medical Contexts

Lecture 6 Zero-shot, One-shot, Few-shot Prompting with Patient Data

Section 4: AI-Assisted Clinical Documentation and Summarization

Lecture 7 Summarizing Patient Visit Notes and EHR Narratives

Lecture 8 Auto-filling Templates for SOAP Notes and Progress Reports

Lecture 9 Streamlining Physician Documentation with AI-Generated Text

Section 5: Generative AI for Predictive Analytics and Risk Modeling

Lecture 10 Forecasting Readmission Risk using Historical Data with GenAI

Lecture 11 AI-Powered Risk Narratives and Compliance Notes

Section 6: Data Cleaning, Labeling, and Augmentation with GenAI

Lecture 12 Automating Labeling of Medical Entities in Clinical Texts

Lecture 13 Prompt-Based Outlier Detection in Health Datasets

Lecture 14 Generating Missing Values and Enhancing Low-Sample Data

Lecture 15 Automating Data Quality Audits with GenAI

Section 7: Conversational Interfaces and Querying Health Data

Lecture 16 Natural Language to SQL for Querying EHR Tables

Section 8: Visualization and Communication with GenAI

Lecture 17 Creating PowerPoint Presentations for Clinical Outcomes

Lecture 18 Generating Markdown Reports for Doctors and Nurses

Lecture 19 AI-Driven Health KPI Dashboards: From Prompt to Plot

Lecture 20 Explaining AI-Generated Results to Clinical Teams

Section 9: AI for Regulatory, Insurance, and Policy Documentation

Lecture 21 Generating HL7/FHIR-Compliant Messages from Patient Data

Lecture 22 Drafting Insurance Claims and Pre-Authorization Letters

Lecture 23 Compliance Report Summarization and Audit Narratives

Lecture 24 GenAI Workflows for Legal and Policy Teams in Healthcare

Section 10: 1000+ Prompts - Generative AI for Healthcare Data Analyst

Lecture 25 Patient Segmentation from Structured Healthcare Data

Lecture 26 Identifying High-Risk Patients Using AI Prompts

Lecture 27 Classifying Admission Types Based on Medical History

Lecture 28 Tagging Patient Conditions from Clinical Notes

Lecture 29 Grouping Patients by Treatment Outcomes or Drug Response

Lecture 30 Generating AI-Driven Dashboards from EHR Tables

Lecture 31 Visualizing Readmission Rates by Diagnosis

Lecture 32 Creating Heatmaps for Hospital Bed Occupancy

Lecture 33 Trend Analysis of Lab Test Results Over Time

Lecture 34 Prompt-Based KPI Reporting for Hospital Operations

Lecture 35 Generating Discharge Summaries from Patient Records

Lecture 36 Summarizing Physician Notes and SOAP Entries

Lecture 37 Automating Markdown Reports for Clinical Teams

Lecture 38 Weekly Patient Progress Summaries using Prompts

Lecture 39 Summarizing Medical Compliance Notes for Audit

Lecture 40 Early Disease Prediction Using Risk Factors

Lecture 41 Estimating Readmission Risk Using Historical Data

Lecture 42 AI-Generated Risk Narratives for Patient Profiles

Lecture 43 Classifying Disease Severity with Few-Shot Prompts

Lecture 44 Comparing Diagnoses Across Similar Patient Profiles

Lecture 45 Filling Out SOAP Notes Automatically

Lecture 46 Auto-generating Insurance Claims from Visit Details

Lecture 47 Drafting Prior Authorization Letters via Prompts

Lecture 48 Generating FHIR-Compliant Medical Records

Lecture 49 Writing AI-Generated Patient Consent Summaries

Lecture 50 Prompting AI to Detect Missing Values

Lecture 51 Generating Synthetic Data to Fill Sparse Records

Lecture 52 Outlier Detection in Clinical Tables

Lecture 53 Checking Anomalies in Diagnosis-to-Treatment Flow

Lecture 54 Data Quality Audits via Generative AI Prompts

Lecture 55 Natural Language to SQL for EHR Queries

Lecture 56 Prompting GenAI to Explain SQL Logic to Clinicians

Lecture 57 Creating Parameterized Queries via Prompts

Lecture 58 Prompt-Based Record Filtering (by diagnosis, drug, etc.)

Lecture 59 Prompting to Generate Join Conditions for Tables

Lecture 60 Zero-shot, One-shot, Few-shot Prompting with EHR Data

Lecture 61 Prompt Chaining for Medical Data Workflows

Lecture 62 Instructional vs. Analytical Prompts in Healthcare

Lecture 63 Using Templates to Auto-Label Patient Records

Lecture 64 Designing Explainable Prompts for Clinical Teams

Lecture 65 De-identification of Medical Text Using Prompts

Lecture 66 Differential Privacy Prompts for Data Sharing

Lecture 67 Red-teaming Medical Prompts for Hallucinations

Lecture 68 Risk-Based Escalation Prompting (Tiered SOPs)

Lecture 69 Prompt-Based Logging for HIPAA Audits

Lecture 70 Creating AI-Powered Chat Interfaces for Clinicians

Lecture 71 Building Multi-turn Patient Inquiry Dialogues

Lecture 72 Translating Medical Jargon for Patient Literacy

Lecture 73 Auto-Drafting Referral Letters and Handover Notes

Lecture 74 Explaining AI Output to Non-Technical Medical Staff

Healthcare Data Analysts seeking to generate summaries, dashboards, and SQL queries from patient records using AI.,Clinical Informaticists and EHR Specialists who want to streamline SOAP notes, discharge summaries, and referral letters with prompt-based automation.,Medical Researchers and Biostatisticians interested in synthetic data generation, risk modeling, and pattern discovery.,AI Engineers and Prompt Designers looking to specialize in healthcare applications using LLMs and RAG systems.,Hospital IT Teams aiming to build chatbots, integrate FHIR-compatible outputs, and automate compliance reports.,Healthcare Administrators and Compliance Officers needing tools for documentation, audit support, and policy enforcement via AI.,Public Health Analysts working on large datasets and population-level risk narratives or forecasts.,Medical Students and Technologists interested in bridging clinical knowledge with next-generation AI capabilities.