The Hidden Costs of Manual Patient Matching: Why Ongoing Manual Review is Unsustainable

We often talk about data quality in terms of percentages. A “90% match rate” sounds successful. But when dealing with patient lives, percentages hide the dangerous reality of raw numbers. The gap between deterministic matching engines and a truly clean database creates a “Gray Zone” of unresolved records. The cost of this Gray Zone is immense, not just in operational dollars, but in time and patient safety risks. Let’s look at the math—and the consequences—of the status quo.

The 100,000 Record Reality Check

Consider a standard healthcare organization or HIE with a database of one million patient records. A typical deterministic MPI might successfully match 90%, leaving 10% of the records in the Gray Zone as “probable matches”.

That’s 100,000 records dumped onto the desks of your Data Stewards.

For illustration purposes, let’s say a human data steward can resolve about 60 records per day. Based on a standard 21-day working month, here is the reality of manual review for a typical team of eight stewards:

• The Manual Way: To clear that 100,000-record backlog, a team of eight working full-time would take roughly 10 months.
• The Automated Way: By using worklist automation to resolve 90% of those records instantly, that same team of eight could finish the remaining complex work in just one month.

The disparity between 10 months and one month isn’t just an operational inefficiency; it’s a massive financial drain on salary hours spent on solvable problems.

The Clinical Cost of “Waiting in Queue”

The most insidious cost of the manual backlog isn’t financial; it’s clinical. While those 100,000 records sit in the Gray Zone for months waiting for human review, they represent patients whose data is fragmented at the point of care. When a duplicate record exists, a clinician may not have a complete view of the patient’s history, allergies, or recent labs. The toll this takes on clinical decision-making is huge, and ultimately, the risk to patient safety is even bigger.

Closing the Gap

Hospitals and HIEs know that 90% accuracy isn’t good enough when patient safety is at risk. But throwing more humans at a computational problem is unsustainable.

Reducing a 10-month backlog to a one-month project isn’t just about efficiency. It’s about ensuring clinicians have accurate information faster, reducing risks, and allowing your data team to focus on high-value initiatives rather than drowning in busy work.