Generative Ai For Healthcare Data Analyst & Professionals
Published 7/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 0m | Size: 467 MB
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.
Who this course is for
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.
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