AI in HEOR: Reflections on Early Innovations

My involvement in AI and predictive modeling within Health Economics and Outcomes Research (HEOR) began during a pivotal time in the 2000s when traditional statistical modeling was increasingly complemented by machine learning techniques. Initially, predictive modeling focused on straightforward statistical tools to forecast healthcare utilization and costs using large healthcare datasets, primarily insurance claims data. Although these early methods weren't called "AI," they laid essential groundwork by enabling data-driven patient risk stratification.

Early Risk Stratification Models

In the 1990s and early 2000s, foundational tools such as the Chronic Disease Score and Johns Hopkins' Adjusted Clinical Groups (ACGs) were developed to classify patients based on their healthcare utilization patterns. A landmark study by Powers and colleagues in 2005 introduced the Pharmacy Health Dimensions (PHD) system, highlighting the effectiveness of pharmacy claims data in predicting total healthcare expenditures. Remarkably, simpler statistical approaches often matched the predictive accuracy of more complex econometric methods, underscoring an important lesson: detailed data could outperform sophisticated algorithms.

Innovations at CVS Caremark

During my tenure at CVS Caremark in the mid-2000s, we took predictive modeling a step further by incorporating medication adherence into pharmacy-based predictive models. Our team employed boosted regression trees, an advanced machine learning technique, to predict healthcare costs more accurately. Our 2006 study demonstrated that integrating adherence metrics significantly improved predictions, showcasing AI's potential to capture complex interactions among medication use patterns. However, it also taught us crucial lessons about the risks of overfitting, highlighting the need for rigorous validation.

These experiences illustrated that machine learning could enhance traditional actuarial models by detecting nonlinear relationships and nuanced patient behaviors. My work, alongside contemporaries exploring neural networks and decision trees, signaled a pivotal shift in HEOR analytics toward more sophisticated, data-driven methods.

Patient-Reported Outcomes Integration

Another notable trend during this period was the integration of patient-reported outcomes (PROs). Researchers began applying clustering algorithms and early natural language processing to PRO data, expanding predictive modeling beyond claims and utilization data. While computational limitations and data availability constrained early efforts, these pioneering attempts hinted at today's rapidly evolving use of AI in personalized medicine and outcome prediction.

Key Insights from Early AI in HEOR

Reflecting on these early milestones, several key insights stand out:

• ●Pharmacy-based data significantly improved predictive accuracy, often more effectively than complex econometric models.
• ●Machine learning methods, such as boosted regression trees, offered valuable but incremental improvements over traditional statistical models.
• ●The complexity of AI models required careful handling to ensure generalizability and prevent overfitting.

Ultimately, my early work at CVS Caremark, along with foundational contributions by other researchers, set the stage for the sophisticated predictive analytics and AI-driven approaches that now characterize modern HEOR. Understanding this journey highlights the evolution and ongoing potential of AI in healthcare research.