AI Enters Clinical Workflow at Sutter

Health system to integrate OpenEvidence into Epic records, bringing research summaries to doctors at the point of care

18 Feb 2026

Sutter Health plans to integrate OpenEvidence’s artificial intelligence medical search tool into its Epic electronic health record system, marking a further step in the use of AI in routine clinical care.

Once deployed, physicians will be able to submit clinical questions in plain language within a patient’s electronic chart and receive summaries of peer-reviewed studies and treatment guidelines. The system is designed to operate inside existing workflows, reducing the need for doctors to search external databases. Sutter has not said whether the rollout will take place across the entire network at once or in stages.

The move reflects a broader shift in parts of the US healthcare sector. Clinicians face a steady flow of new research, evolving standards of care and growing administrative demands. Embedding AI tools directly into electronic health records seeks to reduce the time required to retrieve information and allow more focus on patient care.

OpenEvidence describes its platform as a means of bringing evidence-based insights into physician workflows. The company says its system is intended to support, rather than replace, clinical judgement by accelerating access to validated research.

The timing comes as health systems face pressure to improve quality metrics, standardise care across large provider networks and manage costs. AI-enabled decision support tools may offer a way to promote more consistent, evidence-informed treatment while also improving operational efficiency. For organisations investing in digital infrastructure and analytics, such integrations form part of longer-term modernisation strategies.

However, deployment raises governance and oversight questions. Health systems must establish clear validation processes, transparency standards and accountability frameworks. US regulators continue to assess how AI systems influence clinical decision-making, and hospital leaders have cautioned against excessive reliance on automated outputs.

It remains early for broad conclusions about impact. But initiatives such as Sutter’s indicate growing interest in embedding AI tools into core clinical systems. Wider adoption is likely to depend on whether early implementations demonstrate measurable improvements in efficiency, quality of care and patient outcomes.