Bill Siwicki

As hospitals and health systems accelerate artificial intelligence adoption, industry leaders warn that inaccurate patient identity data threatens performance, patient experience and financial results.

As health systems face a new era of retail clinics, digital health companies and consumer expectations for convenience, some virtual care leaders say success depends on building clinical and technology teams around a shared operating model.

As health systems expand their artificial intelligence projects, tackling areas such as ambient charting and pharmacovigilance, cancer care is one area that's exposing data governance challenges that may determine success or failure for enterprise AI.

The health system integrates PatientGPT into its patient portal and clinical infrastructure, seeking to offer consumers AI-powered health guidance while maintaining physician oversight, data governance and continuity of care.

CommonSpirit Health's CMIO says its greatest value may be helping clinicians scale cancer screening – identifying overlooked findings and reducing administrative burden while preserving human oversight.

The health system's GRACE program combines genomics, imaging data and clinical workflows to make personalized risk assessment part of everyday practice.

High physician adoption, less after-hours charting and increased clinical capacity helped the health system scale AI-powered documentation across care settings.

Success with nursing AI will depend on designing tools around the realities of bedside care, governance and operational outcomes – rather than adapting physician documentation technologies.

As providers look beyond ambient AI documentation tools, AI orchestration could reshape prior authorizations, referrals, payer compliance, claims management and more.

A telemedicine program connects critical access hospitals with forensic nursing specialists, helping sexual assault survivors receive trauma-informed care locally while strengthening support for rural clinicians.