3 things healthcare leaders should know about AI-native EHRs
AI-native EHRs may improve clinical workflows, according to Healthcare Dive. This approach can help healthcare organizations scale AI more effectively.
Original reporting · healthcaredive.com
Read “3 things healthcare leaders should know about AI-native EHRs” at Healthcare Dive →
How this was written. Diagnosis Forward wrote this article with a language model from Healthcare Dive’s reporting (model: @cf/meta/llama-3.3-70b-instruct-fp8-fast); it describes that reporting rather than adding to it. The original, linked above, is the full story.
Key points from Healthcare Dive’s report
- AI-native EHRs may improve clinical workflows by providing seamless integration with existing data and workflows.
- AI-native EHRs can help healthcare organizations scale AI more effectively, creating continuity across the clinical and operational journey.
- Evaluating the architecture behind AI is crucial for healthcare organizations to ensure that individual capabilities can share data and workflows.
Healthcare organizations are shifting from experimenting with individual AI tools to deploying them more broadly, raising questions about how AI is integrated into electronic health records (EHRs).
The way AI is connected to EHRs has significant effects on how clinicians work, Healthcare Dive reported. When AI is built into the EHR platform itself, it can work seamlessly with existing data and workflows.
AI-native EHRs can synthesize patient history and provide relevant context to clinicians, making it easier for them to prepare for visits and document patient interactions.
For example, AI-native tools can automatically generate summaries of patient history and capture visit notes, reducing the time clinicians spend on documentation.
One healthcare organization using an AI-native EHR reduced chart preparation and documentation time by nearly six minutes per visit, and increased same-day chart completion to over 80%.
The value of AI-native EHRs lies not only in making individual tasks faster, but also in creating continuity across the clinical and operational journey, from preparation to documentation to coding and quality measurement.
As healthcare organizations scale AI, they need to evaluate the architecture behind it, ensuring that individual capabilities can share data and workflows across the organization.
Questions and answers
What is the difference between AI-connected and AI-native EHRs?
AI-native EHRs have AI built into the platform itself, allowing for seamless integration with existing data and workflows, while AI-connected EHRs may introduce new data boundaries and workflow interruptions.
How can AI-native EHRs improve clinical workflows?
AI-native EHRs can synthesize patient history, provide relevant context to clinicians, and automate tasks such as documentation and coding.
Why is evaluating the architecture behind AI important for healthcare organizations?
Evaluating the architecture behind AI is crucial to ensure that individual capabilities can share data and workflows across the organization, creating continuity across the clinical and operational journey.
