Health data analysis is at an inflection point, and where it goes next depends on understanding how it got here. Almost four decades of compounding change across data, technology, policy, and economics produced the conditions for what may soon become AI-driven healthcare. Over these years, healthcare leveraged advances in data infrastructure and computation alongside policy shifts and changing care-delivery models, transforming health analytics from a transactional record-keeping function into a continuously evolving clinical support capability. This paper surveys that transformation, tracing the arc from descriptive statistics in the early 1990s into predictive analytics in the 2000s, machine learning in the late 2010s, and the current frontier of generative AI. The paper examines how five interdependent pillars---data and analytics, technology, care delivery trends, policy, and economics---shaped each other across the last several decades. Taken together, these interdependencies reveal a field converging toward proactive, continuous health intelligence and establish a historical baseline for future IEEE AI Coalition work on AI in healthcare.