Balancing metrics and meaning: Rethinking data and models in healthcare

Data and models never fully capture the realities they represent. Scientist and philosopher Alfred Korzybski famously aphorized, “The map is not the territory,” and, “The word is not the thing.”¹ Data reflect the past or near-present moments, and models provide theoretical frameworks. Neither encompass the labyrinthine scope of reality.

The 2008 global financial crisis starkly illustrated the dangers of overreliance on mathematical models, such as those used to assess mortgage-backed securities, which failed to capture the full complexity and risk of real-world economic behavior.²

In healthcare, the risks of overreliance on data and models are similarly profound. Predictive algorithms built on biased data have misprioritized patients,³ as seen in early COVID-19 triage tools that underestimated risks for minority populations. Scheduling systems that lean too heavily on data can miss clinician workload nuances, contributing to burnout and reduced care quality.⁴ These are not rare exceptions. Biases and faulty generalizations are widespread in research and operational practice.

Still, when used wisely, data and models are indispensable. They bring structure to decision-making, enable predictive analytics for population health, and support evidence-based protocols that improve patient outcomes. For example, predictive models can flag at-risk patients for early intervention, helping reduce hospital readmissions.⁵ Data-driven insights also streamline operations, from staffing to supply chain logistics. The key is to treat these tools as decision support, not decision substitutes.

Buckingham and Goodall (2019) observed that solutions to complex problems lie in the “tangible and changing realities of the world as it really is,” not in abstract representations of the past.⁶ Models built on outdated data often misalign with present-day systems. In contrast, real-time, human-centered insights often outperform abstract models not designed for dynamic conditions or nuanced contexts. Strategically, overemphasizing data can mislead healthcare organizations, such as when hospitals prioritize short-term metrics like bed turnover rates at the expense of long-term community investments, ultimately undermining population health.⁷