Resources for Your Team
Making AI Work in Healthcare
Healthcare leaders don’t need more AI hype. They need solutions built around real clinical data challenges.
4 Things Healthcare AI Should Never Do Without Clinical Review
Where should healthcare AI defer to human expertise? Explore four situations where clinical review remains essential to trusted abstraction.
Death, Taxes, and Data Quality Issues: What Health Plans Can Actually Control
Health plans can’t control every clinical data issue, but they can improve how data is validated, monitored and put to…
Compliance is a Revenue Strategy: Why Proactive Audits Pay Off
Move audits upstream, prioritize risk, and strengthen denial appeals with a proactive, technology-supported compliance strategy.
What Should You Automate With AI? Lessons From Building Prodigy™
Effective healthcare AI starts with knowing what to automate and what to leave to people. See how AI reliability and…
From Data Burden to Data Asset: How Health IT Leaders Are Driving Strategic Decision-Making with Data and AI
Healthcare leaders share strategies for overcoming data silos, strengthening governance, applying AI, and unlocking greater value from clinical data.
Healthcare Compliance in an Evolving Landscape: Data, Technology, Risk and Trust
Explore how data, technology and risk are reshaping healthcare compliance and trust.
Transforming Cardiovascular Care with AI: Insights from the 2026 ACC Quality Summit
Insights from the 2026 ACC Quality Summit show how AI can support cardiovascular teams, expand access to care, and improve…
How Does Your Health System’s Data Factor into AI Improvement?
Explore how Prodigy approaches healthcare AI data privacy, keeping clinical data isolated while supporting improvements in data abstraction.
Raising the Standard for Medical Record Quality Control
Effective quality control helps healthcare organizations identify record errors, protect sensitive information and strengthen compliance.
