At its heart, the Geneia Data Intelligence Lab (GDI Lab) uses leading-edge data science to drive lower healthcare costs and improve health outcomes.
Our lab prioritizes projects that address major cost drivers and enable our clients – health plans, hospitals, physicians and their value-based partners – to identify, stratify and predict high-cost patients and conditions such as heart failure and diabetes.
Our models help clients to intervene earlier with patients whose risk is expected to rise and/or who are at risk for major, expensive conditions, allowing healthcare organizations to mitigate future costs while improving the health of those they serve.
Hypertension Complications Calculator: A Sneak Peek
For most of the 45.4% of Americans with hypertension, there are no obvious symptoms, which makes it even harder for healthcare professionals to get ahead of devastating complications such as heart attack, stroke and end-stage renal disease.
The Geneia Data Intelligence Lab created the personal hypertension complications calculator to illustrate how its Hypertension Complications Model* predicts near-term complications. The calculator simplifies the model and yields approximate results.
Our patent-pending, AI model helps healthcare organizations:
- Predict hypertensive patients/members most likely to experience complications in the next 12 months.
- Predict each person's risk for three stages of hypertension.
- Refine individual risk within each stage.
To illustrate how this model works, our data science team developed a personal hypertension calculator.
*Patent pending for the Hypertension Complications Model. Predictive models, by their very nature, contain certain assumptions. This is not an attempt to practice medicine or provide specific medical advice, and it should not be used to make a diagnosis or to replace or overrule a qualified healthcare provider's judgment. Certain data used in these studies were supplied by International Business Machines Corporation. Any analysis, interpretation, or conclusion based on these data is solely that of the authors and not International Business Machines Corporation.
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