Beyond Efficiency: Can AI Actually Change Patient Outcomes?

Much of healthcare’s early adoption of AI has focused on efficiency. From clinical documentation and administrative automation to workflow optimization and decision support, organizations are beginning to demonstrate that AI can save time and reduce operational burden.

The next question is harder: can AI actually improve patient outcomes?

Predictive and generative AI are increasingly being applied to identify deterioration earlier, support clinical decision-making, reduce avoidable readmissions, personalize care, and help clinicians intervene at the right time. As these technologies move from pilots into real clinical environments, organizations are beginning to look beyond productivity metrics towards measurable changes in care quality and outcomes.

Demonstrating that impact is not straightforward. Improvements in mortality, readmissions, length of stay, or disease progression are influenced by many factors, making it difficult to determine what can genuinely be attributed to AI. Successful implementation also depends on workflow integration, clinician adoption, data quality, governance, and what happens after an AI system identifies a patient who needs intervention.

This roundtable brings together leaders across healthcare, technology, pharma, and population health to explore where AI is beginning to demonstrate meaningful clinical impact, what separates successful deployments from promising pilots, and what evidence is needed to prove that AI is actually improving care.

Join us to discuss:

• Where is AI demonstrating the clearest measurable impact on patient outcomes today?

• What separates AI deployments that change clinical outcomes from those that primarily improve efficiency?

• How should organizations measure and attribute improvements in areas such as deterioration, mortality, readmissions, and length of stay?

• What needs to happen after AI identifies a risk or recommends an intervention for that insight to translate into better care?