Management

Predictive Analytics can help healthcare organizations predict and manage risk in patient populations, improving care and reducing costs. AI-powered predictive models can identify high-risk patients and help providers intervene earlier to prevent adverse events, such as hospital readmissions. Additionally, predictive analytics can help organizations optimize their care delivery processes and manage costs. By leveraging AI and machine learning, healthcare organizations can achieve greater accuracy in their risk management practices, resulting in improved health outcomes and quality of care.

How GenHealth Addresses Risk Management

Our model is a next token predictor. Those tokens can be any encoded event in a patient’s medical data. Specifically we work with codes like ICD (conditions), LOINC (labs), NDC/RxNorm (medications), CPT (procedures), NPI (doctors & facilities), etc. Therefore our model can be used to predict the likelihood or risk of any of these events happening in the future.

And because our model produces a sequence or chain of events you can use it to identify more complicated healthcare actions. For example: “How likely is this patient to leak out of network?” To do that you simply run a patient’s history as a prompt, get their predicted futures, and see how many times our model produced an NPI that is not in your network.

Another, “How likely is my population to be readmitted for their hip surgery or to the ER?” Run the futures for your patient panel or membership through our generative AI model and just check the patient futures for the CPT code 27130 (Hip Replacement) or 99281 (ER).

The future of Generative AI and Risk Management The use of AI in predictive analytics for risk management will continue to improve, enabling healthcare organizations to identify and manage risk more effectively. By leveraging real-time data and predictive modeling, providers will be able to intervene earlier and prevent adverse events, ultimately leading to better health outcomes and lower costs.