Every lab result is a decision point.

A troponin that comes back elevated sends a patient to cardiology. A low platelet count triggers a transfusion consult. A culture result changes an antibiotic. The laboratory is upstream of an enormous number of clinical decisions — and yet, it often sits outside the conversation when hospitals talk about outcomes data.

That gap is something I think about a lot.

In high throughput environments, we’re built for speed and accuracy. Turnaround time is king. But what happens after the result is released? Does the right clinician see it in time? Does a critical value get acted on? Is there a pattern in the patients who keep coming back with the same abnormal panel?

These are questions that live at the intersection of laboratory medicine and data analytics — and they’re largely unanswered, not because the data doesn’t exist, but because no one has connected the dots.

The data is there. Instrument logs, LIS records, QC runs, test utilization rates, reflex patterns — clinical labs are sitting on rich, largely untapped datasets. The challenge is building the analytical infrastructure and the clinical fluency to make sense of it.

That’s exactly what I’m working toward. My background in instrumentation gives me an understanding of where the data comes from and what it actually means. My work in analytics is giving me the tools to do something with it.

The goal isn’t just better lab operations — it’s better outcomes for the patients those labs serve. That’s the picture I want to help paint.