Hospitals in the United States spend over $25 billion in unnecessary supply chain costs every year. For a mid-sized health system, supply issues increase the cost of care by up to $3.5 million annually while tying up another $1 million in excess inventory.

Twenty-four percent of healthcare providers have seen or heard about expired products being used on patients. Roughly one percent of items sitting in hospital supply rooms are recalled products, many of them high-risk implants. Advanced inventory systems, despite their cost and complexity, routinely miss critical details like expiration dates and lot numbers.

The root cause isn't negligence. It's that healthcare supply chain data is inherently complex, and legacy systems were never designed to handle it.

The Endless Cycle

Vizient research shows that roughly 30 percent of the average hospital item master contains bad data. Duplicate records, conflicting manufacturer information, inconsistent naming, broken relationships between products and contracts. The mess is pervasive.

The standard fix: hire a third-party consulting firm to cleanse the data. These engagements can cost well over $100,000. The process takes months. Offshore teams manually check records, normalize entries, and export cleaned data back into the ERP.

What happens next will surprise absolutely no one. The data gets dirty again.

New items come in with inconsistent manufacturer names. Acquisitions change ownership relationships. Staff enter records differently across departments. Within months, you're back where you started, except now you've spent six figures and half a year.

So hospitals do it again. And again. Year after year, paying to clean the same data that their systems keep corrupting. That's not a fix. That's maintenance.

A Different Foundation

When we started Red Thread, we weren't trying to build a data cleansing tool. We were building an integration layer, and we kept hitting the same wall: the data we had to work with was a mess. So we solved that problem for ourselves, from first principles.

We made a deliberate architectural bet: our data layer would be a graph database. We chose Neo4j not because it's novel, but because healthcare supply chain data is fundamentally a network of relationships that change over time.

Relational databases expect clean, structured inputs. Healthcare supply chain data is neither. A graph lets us ingest manufacturer hierarchies, contract relationships, product documentation, and historical ownership changes without forcing them into rigid tables. And because relationships are native to the structure, we can identify duplicates by pattern, not just string matching, and enrich records by traversing connections automatically.

On that foundation, we built what became a patent-pending item data cleanse and enrichment engine. Only after it was working did we realize what we had: something faster, more accurate, and more complete than the consulting engagements we'd seen hospitals repeat year after year. So now we offer it as a service.

Our engine uses AI to cleanse and enrich at scale, with humans in the loop only for final quality checks. What takes months elsewhere happens in days.

And we go beyond cleaning. Your item master comes back enriched with critical product documentation, manufacturer relationships, and operational context that manual processes never touch. This is the connective tissue of our platform. The richer your data foundation, the more powerful every tool built on top of it becomes, from inventory optimization to the AI intelligence layer we're building now.

Why This Matters Beyond Clean Data

Dirty data doesn't just create operational headaches. It creates patient safety risk. It creates cost exposure. It creates the conditions where recalled products sit on shelves and expired items get used because nobody can trust what the system says.

A graph-based foundation changes what's possible. When your data layer treats relationships as first-class concepts, you're not just cleaning data. You're building infrastructure that can reason about that data. The result: an item master that stays clean because the architecture enforces it.

If you're tired of solving the same problem every year, we should talk -> Get in touch

Ryan Cheney, Co-Founder & CEO at Red Thread