Complex Systems in Practice: Modernizing Public Health Data Flows

Complexity starts when causality breaks down.

— Nigel Goldenfeld¹

The first step in any innovation project is understanding where the friction lies. Where does the work slow down? Where do risks emerge? And who bears the burden of inefficient systems?

In public health, many of these pain points are embedded in our data flows. Manual processes, fragmented systems, and unclear ownership are common features of legacy infrastructure. While often invisible to those outside the system, these challenges directly affect our ability to deliver timely, effective, and equitable public health services.

Public Health as a Complex System #

Public health challenges rarely exist in isolation. They occur within complex systems made up of community members, service providers, laboratories, reporting platforms, policies, and public institutions. These components interact in ways that are dynamic, non-linear, and often difficult to predict.

A small change in one part of the system, such as a new reporting requirement, data standard, or surveillance tool, can create ripple effects elsewhere. These effects may improve outcomes in one area while introducing new constraints or unintended consequences in another.

Understanding these interactions is essential if we want technological innovation to meaningfully support public health practice rather than add new layers of complexity.

Mapping Complex Systems #

One way to make sense of this complexity is through systems mapping. By visualizing how different actors, data sources, and processes connect, systems mapping identifies areas where change can have the greatest impact.

The opioid crisis is a well-recognized example of a complex public health challenge. It involves health care providers, emergency services, coroners offices, community organizations, laboratories, public health agencies, and people with lived experience, each contributing data, decisions, and actions that shape outcomes.

Within this system, data and analytics teams work alongside community partners who collect information on overdoses, toxicology results, and drug-related deaths. When this information is better managed, integrated, and governed, public health practitioners gain a more timely and complete picture of emerging trends, geographic hotspots, and service gaps.

The benefits extend beyond analytics. Improved data flows support more targeted interventions, better coordination across partners, and faster responses to changes in the local drug supply. Over time, they can also enable more informed decisions about where to allocate limited financial and programmatic resources, particularly in areas experiencing increased demand or chronic under-resourcing.

What This Blog Series Will Explore #

This post is the first in a series that explores public health through the lens of complex systems.

In upcoming posts, we will:

• Explore systems thinking as a practical approach to public health innovation
• Introduce data ecosystem mapping as a tool for understanding and navigating complex systems
• Share examples from our own work modernizing public health data and technology

When public health challenges are viewed holistically, technology can be positioned not as a solution in itself, but as a tool to reduce friction, support practitioners, and improve public health outcomes.

Stay tuned for the next post.