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Healthcare Case Study

Loomcraft Health

A sentiment analysis system that surfaced patterns across thousands of reviews.

-18%Admin Time Saved
Loomcraft Health digitallyscaled
ClientLoomcraft Health
IndustryHealthcare
Timeline5 weeks
ServicesNatural Language Processing (NLP)
The Challenge

Where things stood before

Loomcraft Health's biggest obstacle was administrative and clinical workflows that hadn't kept pace with the volume they were handling — something their team had tried to patch more than once without lasting success.

It was the kind of issue that showed up in day-to-day frustration long before it showed up in a report.

The Solution

What we built

We started by getting a clear picture of exactly where things were breaking down before proposing anything.

What we ultimately delivered was a sentiment analysis system that surfaced patterns across thousands of reviews, built specifically around what Loomcraft Health needed.

  • Benchmarked against clear, agreed-upon success metrics from day one
  • Built using Python and spaCy for a stable, maintainable foundation
  • Designed around Loomcraft Health's actual data and workflow, not a generic template
  • Delivered within a 5-week timeline without cutting scope
Results

The impact, by the numbers

-18%
Admin Time Saved
96.7%
Model Accuracy
-23%
Patient Wait Time
+38%
Staff Satisfaction
Tech Stack

Built with

PythonspaCyHugging FacePyTorch

“We'd talked about doing something with AI for a long time without knowing where to start. Having it scoped properly made all the difference — -18% admin time saved within a few months.”

QF
Quinlyn Falkenrath

Head of Data, Loomcraft Health

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