Bill Siwicki

The CEO of Carta Healthcare discusses recent survey results that suggest why successful pilots often stall, why EHR integration has overtaken trust as the leading adoption barrier – and why more clinical leaders are taking ownership of AI strategy.

Dr. Ari Robicsek says digital quality measurement, interoperable clinical data and AI create an opportunity to reduce administrative burden while delivering more meaningful feedback to clinicians and better outcomes for patients.

As value-based care expands, health systems are using technology-dependent transitional care to support patients at home, reduce readmissions and improve outcomes.

CHAIRS aims to give health systems, payers and regulators evidence of how AI actually performs in live patient encounters, not just before implementation.

By embedding a predictive risk model into Epic and redesigning nursing workflows, the health system reduced hospital-onset MRSA bacteremia by about 45% while standardizing infection prevention across its hospitals.

After replacing a homegrown system with an AI-enabled practice management platform, the therapy provider improved revenue recovery while giving clinicians more time with patients.

Oracle Health chief architect Manu Agrawal explains why healthcare-specific AI must be grounded, auditable and tailored to the risk of each workflow.

Health systems need to move beyond virtual visits and ensure telehealth is integrated with the EHR, prioritizing patient triage and ongoing care, says Dr. Joseph Kvedar of Harvard Medical School and the American Telemedicine Association.

Artificial intelligence can take on time-consuming data work, helping healthcare providers work faster and reduce costs while keeping clinicians and other experts in control of critical decisions. Cancer registrars show how.

They should start with measurable, lower-risk use cases while also addressing broadband, workforce, data and governance gaps, says Julia Clark of Berkeley Research Group.