Key takeaways
- A heart disease risk model scores each patient from routine tests and history, and shows the factors that drove the score for that patient.
- Reason codes point your physician to the symptoms and tests that matter most, instead of a single unexplained number.
- Bias checks test the model across sociodemographic groups before it reaches a worklist.
- H2O makes no clinical efficacy claim: the model changes where your cardiology team starts, not who decides.
Risk hides in routine results
Heart conditions often go unrecognized until symptoms are hard to miss. The signals are frequently already in the chart, spread across lab results, imaging reports and history that nobody has time to read together for every patient.
Your cardiologists face a hard judgment: is a change in the heart a sign of disease or of age. A worklist in arrival order gives them no help deciding which patients to look at first, and any model you add has to explain itself to physicians, your quality team and your ethics review.