4x
Improvement in accurate HCC recapture
Find What Your Data Can Predict. Put It to Work.
A prediction solution for health systems and medtech companies. It finds what your data can predict and puts validated predictions to work.
Follow one risk score from the data behind it to the decision it informs.
Framing a prediction takes specialist judgment, and getting it used takes integration and the trust of the people who act on it. Once live, shifts in patient mix, coding, and devices erode accuracy quietly.
Your clinicians are handed risk scores they cannot interrogate, and a score nobody trusts goes unused.
Every score arrives with the drivers behind it and a model card, so clinicians see why and can disagree.
Your clinicians are handed risk scores they cannot interrogate, and a score nobody trusts goes unused.
Every score arrives with the drivers behind it and a model card, so clinicians see why and can disagree.
A Predictability Map shows what is predictable now, what one more data source unlocks, and what needs more data first.
Drivers or a visual heatmap sit beside each score, so clinicians see what pushed the risk up and what pulled it down.
Simulate a policy change on your own forecast and see which fix reduces risk the most before you commit.
Prototypes for catheter infection, cancer early risk, wound triage, infusion delivery, and tumor triage span structured and unstructured data.
Scores arrive as worklists, alerts, or embedded apps, designed to plug into existing workflows through standard interfaces.
Drift and calibration are monitored after go-live, with retraining and versioned releases whenever the data shifts.
Each problem starts from the clinical or financial decision, so the label, the timing, and the action are right before any build begins.
CarePredict runs in your cloud or on-premises, with PHI-minimized pipelines, and your data stays separate from every other client's.
Intended use, training data, performance on your population, subgroups, and limitations are written up and updated with each release.
We work alongside your data scientists and transfer what we build, so every solution leaves your team stronger.
4x
Improvement in accurate HCC recapture
42%
Increase in RAF score precision
50%
Reduction in post-encounter coding edits
HCC risk adjustment was manual and inconsistent, and missed diagnoses lowered RAF accuracy. We built an AI assistant trained on three years of EHR history that suggests diagnoses to recapture and shows RAF changes in real time. Providers accept, dismiss, or replace each suggestion.
Read Case Study





Bhupesh Nadkarni
Head, Healthcare
We build it. You own it.
