Why America's Health Systems Need Dimensional Data Architecture

Despite spending $4.9 trillion on healthcare (17.6% of GDP)¹, America's health outcomes lag significantly behind other wealthy nations. Between 2010 and 2019, U.S. life expectancy increased by only 0.1 years compared to the 1.2-year average in developed countries². Perhaps most telling is that wealthier Americans with access to healthcare still experience mortality rates that are higher than Europeans of similar or lesser means³.

This paradox exists not because we lack understanding of what creates health—we have extensive knowledge about risk factors, social determinants, and effective interventions. Rather, the fundamental challenge lies in our health information architecture: systems optimized for documenting individual clinical encounters and processing insurance claims, not for population-level insights and action. The current state of health analytics resembles attempting to manage an entire enterprise while seeing only factory-floor operations; we excel at individualized clinical care but lack the systems-level visibility needed for population health management.

This article explores how a dimensional approach to data architecture can transform health information management. We'll examine how building a strong foundation for organizing and connecting health data helps bridge the critical gap between our extensive clinical knowledge and disappointing population health outcomes, enabling health systems to finally turn information into meaningful improvements across communities.

The Challenge of Health Data Transformation

We collect mountains of health data but lack the architectural framework to transform it into actionable intelligence. Limited public health capacity and infrastructure, which political and policy decisions have systematically deprioritized through insufficient investment, contribute significantly to this failure. Yet even in states with strong public health support and dedicated resources, the combination of data fragmentation and ineffective real-time data utilization continues to obscure patterns, impede analysis, and frustrate improvement efforts.

The future of public health requires systems that give large health datasets structure and meaning to both people and AI models. This foundational capability unlocks the true potential for data integration and enables AI-powered analytics. When health data is properly structured, it becomes inherently more valuable, not just as raw information, but as an organized knowledge base that both human analysts and machine learning algorithms can effectively leverage.

Dimensional Modeling: A Proven Approach from Business Intelligence

In the business world, dimensional data models serve as the architectural foundation for most data warehouses⁴, organizing information in a way that enables powerful analysis across multiple dimensions. Consider a typical sales scenario where the core quantitative values being tracked might include units sold, revenue, and cost of goods sold. These values derive their analytical significance when associated with contextual dimensions such as product categorization (including product line and specific names), geographic sales regions, and temporal elements tracking when each sale occurred.

This dimensional approach allows business analysts to "slice and dice" their data, examining performance from multiple angles simultaneously. The power of this analytical capability lies in its flexibility. Once the data model is properly constructed, it can answer a nearly infinite variety of business questions without requiring new data collection or reorganization efforts.

Despite its transformative impact in business, dimensional data modeling remains surprisingly underutilized in healthcare and public health, representing a missed opportunity to address our troubling health outcomes. One reason lies in the nature of health data, which tends to center around a single quantitative value (people) and doesn't always yield meaningful sums. To create valuable insights, populations need to be meaningfully grouped.

Adapting Dimensional Models for Healthcare and Public Health

Successful multi-dimensional data requires clearly defined population boundaries. By focusing on specific clinical cohorts, such as individuals diagnosed with diabetes, data modelers create groups with shared characteristics that support meaningful measurement comparisons. Within these populations, the dimensional structure can follow the natural care continuum from screening through diagnosis, treatment initiation, and ultimately to disease control.

This approach allows health systems to identify whether patients are falling out at screening (where losses can reach 65% in some populations), at diagnosis awareness, or at the treatment-to-control transition. By pinpointing these specific breakdown points, health systems can implement a more wholistic public health approach rather than doubling down on a more narrow set of clinical interventions.

The treatment cascade approach provides an elegant framework for dimensional modeling in the health context. Drawing from public health methodology, it maps patient journeys through sequential care stages⁵. In HIV management, for example, this tracks progression from testing to diagnosis, care linkage, treatment initiation, and viral suppression. The resulting "waterfall" visualization reveals where interventions would yield maximum impact.

This framework transforms conceptual understanding into actionable analytics by converting the cascade into a series of measurable dimensions. The specific dimensions incorporated into the model aren't predetermined through the modeling process itself. Rather, data modelers leverage established performance metrics, frameworks, and business processes, translating them into appropriate dimensional structures. This translation ensures alignment with operational priorities while enabling flexible, multi-dimensional analysis that can reveal previously unidentified patterns, relationships, and insights across different stages of the cascade.

From Retrospective Analysis to Operational Intelligence

The real power of health system dimensional data modeling isn't just in enabling retrospective research questions, but in creating actionable, real-time operational insights that healthcare and public health systems can use for immediate decision-making. When properly implemented, a dimensional data model transforms static data into a dynamic management tool. Rather than simply studying patterns retrospectively, health systems can identify and address issues as they emerge. A clinic manager could receive an alert when a particular demographic group shows a sudden drop in appointment attendance, allowing for immediate investigation and intervention rather than discovering the problem months later during a program evaluation.

Consider the parallel with financial management in business. We've known for centuries that revenue minus costs equals profit, yet businesses still build sophisticated dimensional models tracking products, channels, regions, and time periods. They do this not to discover the basic relationship between sales and expenses, but to operationalize that knowledge across complex organizations. The dimensional model transforms static knowledge into dynamic, actionable intelligence.

Similarly, a well-designed dimensional model for diabetes care creates a shared information architecture that connects research insights to operational data. When structured around the established cascade framework, this model allows organizations to examine performance from multiple perspectives simultaneously, conduct comparative analyses across different populations or time periods, and gain deeper understanding of the complex factors influencing outcomes. Rather than simply knowing that only 15-35% of patients with diabetes achieve control, a dimensional model enables health analysts to see a cohesive story across the entire care continuum, transforming abstract performance metrics into detailed, multi-dimensional insights.

Implementing Dimensional Modeling in Public Health

For technical leaders in public health, the fundamental challenge is twofold: integrating fragmented data across disparate systems and developing analytical frameworks that transform this data into operational insights. Dimensional data models addresses both aspects by creating coherent structures that facilitate data integration while establishing analytical relationships that enable real-time decision-making.

The current state of health analytics suffers from limited perspective. While individual clinical data points may be meticulously tracked, the broader system-level visibility required for population health management remains elusive. Many executives worry about implementation barriers such as aging IT infrastructure and complex regulatory requirements. However, my work integrating detailed health data across multiple countries and countless systems demonstrated that effective dimensional models can be developed that integrate health data without replacing existing systems or forced conformity with data standards.

Picture an analytical control room that connects and presents disparate datasets in a unified view. A dimensional data model adapted for health provides the architectural foundation that addresses twin challenges: integrating fragmented data across disparate systems, and transforming this unified data into operational insights. By creating coherent structures that facilitate integration while establishing meaningful analytical relationships, this approach enables real-time pattern identification, effective resource allocation, and systemic improvements across the entire public health continuum. This integration bridges the gap between successful individual interventions and collective health outcomes, providing the system-level visibility necessary to translate isolated successes into meaningful improvements for entire communities.