Operationalizing AI-Based Risk Prediction to Improve Patient Outcomes

Operationalizing AI-Based Risk Prediction to Improve Patient Outcomes

The Challenge

Our client, a value-based care organization, had developed an advanced AI and machine learning-based risk prediction tool to stratify patients by risk level. The goal was to allocate physicians' limited time and resources toward the highest-risk patients. While the tool produced accurate risk predictions, the client struggled to integrate these insights into their operations. Initially, they tried embedding the predicted risk scores into the electronic health record (EHR) system for physicians to view. However, while physicians were aware of high-risk patients, they weren’t sure how to act on this information within their workflows. The organization lacked a clear mechanism to incorporate the risk data into the operational team's decision-making process.

Our Approach

We identified that the missing link was bridging the gap between the risk predictions and the operational actions required to support both physicians and patients. To address this, we developed a data-driven tool that surfaced key insights for the operational team, guiding them on which patients needed more attention from physicians and which panels required scheduling for follow-ups or additional visits. This tool allowed the operational team to take ownership of the process, ensuring that high-risk patients were not only flagged in the system but also received the necessary follow-up care. By providing clear, actionable insights, the operational team could better allocate resources, knowing when to prioritize conversations with physicians about high-risk patients or when to schedule necessary office visits for patients who had been overlooked.

The Outcome

With this tool in place, the client successfully integrated their AI-based risk prediction into their operational workflow. This enabled physicians to focus on high-risk patients while the operational team ensured that critical follow-ups were scheduled appropriately. As a result, the organization saw a significant reduction in their admission rates, measured as Admissions Per 1,000 patients. The integration of AI insights into daily operations allowed for a more targeted and efficient use of both physician and operational team resources, ultimately improving patient outcomes and reducing unnecessary costs.