In the ever-evolving landscape of healthcare, the integration of artificial intelligence (AI) has proven to be a game-changer in improving patient outcomes and reducing cost of care. Diagnostic Robotics recently conducted a cost analysis with Blue Cross Blue Shield of Rhode Island (BCBSRI) to measure the impact of Diagnostic Robotics' AI-Powered Population Health Management solution on the health plan's Chronic Heart Failure (CHF) program. 

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Comparing Results: Diagnostic Robotics vs. Traditional Approaches:

Instead of targeting members who were already expensive and utilizing healthcare services often, Diagnostic Robotics' Population Health Management solution utilized predictive analytics to analyze BCBSRI’s diverse data sources, including claims data, electronic medical records (EMR), and emergency department (ED) visits, and identify which members were at risk of future condition exacerbation and could be most significantly impacted by personalized interventions. 

The cost analysis showed that this method used by Diagnostic Robotics’ solution was more cost-effective.

Members flagged by Diagnostic Robotics generated a 4x cost savings per member compared to those identified solely through the health plan's transition of care (TOC) list. Additionally, the analysis demonstrated a substantial $1,600 Per Member Per Month (PMPM) reduction in cost of care for engaged members.

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Analysis from Blue Cross Blue Shield of Rhode Island:

When asked about the analysis during a recent webinar, Joe Cooper, Managing Director at Blue Cross Blue Shield of Rhode Island, stated, “These results are very promising” and that they show a “measurable benefit to engaging members using the method provided by Diagnostic Robotics Population Health solutions.”

Cooper added, “If you look at the results, you can see a clear advantage from understanding which high-risk members may benefit the most from interventions versus those who just happened to be engaged through some other course of action.”

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Expanding Usage and Maximizing Impact:

Following the successful cost analysis, Blue Cross Blue Shield of Rhode Island continues to leverage Diagnostic Robotics' solution to proactively identify at-risk CHF members. Furthermore, the health plan extends its utilization of the solution to design comprehensive care plans and effectively allocate staffing resources across its various program populations. This strategic approach optimizes both cost savings and member outcomes.

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Conclusion:

The cost analysis conducted by Diagnostic Robotics, in collaboration with Blue Cross Blue Shield of Rhode Island, has showcased the promising potential in improving healthcare outcomes and reducing costs. By harnessing the power of predictive analytics, the solution can identify high-risk individuals and help match members with targeted interventions that generate significant cost savings and a notable reduction in the cost of care.