Generative AI for Healthcare Data Analyst & Professionals

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.