How Data Exposed $224 Million in Hidden Drug Markups

Ignoring the hidden costs in complex systems can drain millions, as Ohio discovered when data analysis revealed a staggering $224 million in undisclosed drug markups.

This isn’t about blaming individuals; it’s about understanding how opaque systems allow problems to fester. When Eric Pachman, a chemical engineer with a background running pharmacies, lost his mother to pancreatic cancer, he found himself confronting the bewildering world of drug pricing. That personal experience sparked a mission: to use data to expose what was really happening in the pharmaceutical supply chain. He teamed up with a friend, and together they started publishing free data visualizations.

Their work wasn’t just an academic exercise. It became a catalyst, contributing directly to a state audit and driving significant policy changes across more than a dozen states. It showed, powerfully, how making information accessible could force accountability in even the most complicated industries.

Unmasking the Markup: Data’s Role in Investigation

The journey to uncovering those hidden markups began with publicly available data. Many people assume crucial information is locked away, but often, government agencies and other organizations publish vast datasets. The trick isn’t just finding the data; it’s knowing how to ask the right questions of it. Eric and his team at Data 4 The People focused on the drug supply chain, a notoriously complex network involving manufacturers, wholesalers, pharmacies, and pharmacy benefit managers (PBMs).

Consider the sheer volume of transactions. Each prescription filled generates multiple data points: the drug’s initial price, what the wholesaler paid, what the pharmacy paid, and what was billed to the insurer or Medicaid. When you aggregate this data, patterns emerge that are invisible at the individual transaction level.

My own work in SAP implementations, particularly with IS-Retail, shows me daily how critical accurate master data is. Messy data, or data that’s intentionally obscured, creates blind spots.

The Ohio audit, directly influenced by Eric’s findings, pinpointed $224.8 million in hidden markups within the Medicaid drug supply chain. This wasn’t fraud in the traditional sense, but rather profits taken through complex pricing structures that were simply not transparent. Making this information visible was the first step toward fixing it. You can see more about how this played out in How Data Exposed $224 Million in Hidden Drug Markups.

From Raw Numbers to Clear Visualizations

Raw data is rarely compelling on its own. A spreadsheet full of numbers might be accurate, but it won’t drive policy change. This is where data visualization becomes essential.

Eric, with his engineering background, understood that presenting complex information simply was key. He took intricate pricing models and translated them into charts and graphs that anyone could understand.

This process isn’t just about making things look pretty; it’s about revealing relationships and anomalies. For example, visualizing the difference between what a PBM paid for a drug and what it charged Medicaid for the same drug, across thousands of transactions, clearly showed the markup. Without that visual comparison, the individual differences might seem minor. Aggregated, they represented a massive, systemic issue.

Of course, some argue that simplifying complex financial data risks oversimplification. They say the nuances are lost. While that’s a valid concern, the goal here wasn’t to create an academic paper, but to highlight a problem for public and legislative attention. A clear, impactful visualization often achieves more than a dense report.

Data Journalism Driving Policy

The impact of this data journalism was profound. The Ohio audit, a direct result of the public attention Eric’s work generated, led to concrete findings and recommendations. More importantly, it spurred action.

Over a dozen states have since enacted policy changes to address similar issues in their Medicaid drug programs. This shows the power of data to move beyond awareness and into tangible reform.

Data 4 The People also focuses on training others. They run fellowship programs, teaching individuals how to conduct similar investigative work. This isn’t just about specific findings; it’s about building capacity for informed oversight. Equipping more people with the skills to analyze and visualize complex data means more hidden problems can come to light.

The Role of AI in Data Investigation

The field of data analysis is constantly evolving, and AI-assisted tools are rapidly changing how investigations like Eric’s are conducted. AI can sift through massive datasets far quicker than any human, identifying patterns and anomalies that might take months for an analyst to find manually. It can automate the initial cleaning and structuring of data, which is often the most time-consuming part of any project.

However, AI is not a magic bullet. It can make mistakes, especially if the underlying data is flawed or biased. More critically, AI lacks human judgment and the ability to ask nuanced questions.

It can tell you “what” is happening based on patterns, but it can’t tell you “why” or what the ethical implications are. That still requires a human investigator, someone like Eric, to interpret the findings and build a compelling narrative.

AI speeds up the grunt work, but the insight, the critical thinking, and the investigative drive remain firmly in the human domain. It’s a powerful assistant, not a replacement for expertise.

How might better access to data and smarter AI tools help individuals better understand the complex systems that affect their daily lives, from healthcare to finance?