Intro to Big Data & Decisions in Healthcare , Plain & Simple

Explore what big data means in healthcare by examining sources like EHRs, pharmacy, imaging, genomics, and smart devices, and discuss challenges in volume, velocity, and storage.

Discover the four v's of big data—volume, variety, velocity, and veracity—and their impact on healthcare data sources from electronic records to wearables, powering reliable analytics for clinical decisions.

Compare the two flavors: structured data’s highly organized, searchable format and unstructured data’s flexible, harder-to-search nature, using examples like Excel spreadsheets, census data, and photo tags.

Big data in health care serves as a core asset, supporting analytics, artificial intelligence, clinical decision making, research, genomics, and personalized care through electronic health records to improve patient outcomes.

Ensure data accuracy for big data by data scrubbing and trusted stewards, guarding each data stream under governance to ensure availability, integrity, security, and usability; avoid garbage in, garbage out.

Explore the power of big data through three examples: Amazon, Netflix, and UPS that show predictive analytics, user profiling, and data-driven routing to optimize decisions and outcomes.

Explore how big data enables a centralized view of patient information, personalized care, and improved patient experience, while empowering clinicians with analytics and artificial intelligence to reduce errors and readmissions.

Explore the dangers of big data, from Cambridge Analytica’s use of 50 million Facebook users to China’s social credit system and Target’s pregnancy analytics, and consider data ethics.

Explain how databases organize data, contrasting structured data with unstructured data, and show how records and fields power searches in Excel and a search index for clinical decision making.

Explore data islands and interoperability across health data, from EHRs to imaging, and learn how the enterprise master patient index seeks to unify records with privacy and accuracy.

Unlock insights from clean, governed big data through analytics to inform clinical and operational decisions, using ethnography and personas to map patient journeys and drive care improvements.

Examine how data analytics inform decision making across health care and everyday choices, using decision support tools to predict costs, outcomes, and customer satisfaction.

Understand that correlation does not equal causation, and use clear questions to guide data-driven health care decisions while balancing data with common sense and ethics.

Explore how clinical decision support and electronic coaching can reduce medical errors and prevent deaths, using data from training, protocols, research, and patient insights.

Explore clinical decision support in pharmacy, illustrating how thousands of drugs and 2.5 million potential interactions drive prescribing risks and inform tools that coach physicians and patients toward safer care.

The lecture explains how the ACR appropriateness criteria guide imaging choices, with a radiology example and guidance on placing a decision support engine within CPO and EMR workflows.

Celebrate completing the course and reflect on the value gained. Continue applying the tools and the skills learned to future endeavors.