It’s estimated that 12.9% of all inpatient admissions could be avoided with early preventive care. But directing the right resources to the right patients at the right time can be a challenge, particularly for health systems with hundreds of thousands of patients to manage. Nevertheless, a Midwest health system with 2.4 million patients found success in reducing admissions by using clinical AI to target outreach to patients at risk. By leveraging data on socioeconomic and behavioral risk factors, the AI revealed patients at risk that care teams would otherwise miss. And unlike traditional predictive analytics, Jvion’s prescriptive clinical AI recommended the actions that would most effectively reduce patients’ risk for avoidable admissions.