OpenEvidence Introduces Dotflows to Personalize AI Workflow for Clinicians

The new feature enables clinicians to generate personalized response styles, automate recurring clinical tasks, and execute structured workflow and documentation.

OpenEvidence, a leading AI-powered medical search and clinical decision-support platform, has announced the launch of Dotflows, a new AI-native feature designed to enable clinicians to customize workflows.

The new capabilities allow users to create commands and work based upon their clinical preferences, speciality requirements, and patient populations.

As per reports, Dotflows expands the concept of dot phrases into an advanced natural language-based customization layer suited for the AI-driven clinical network.

The feature can be activated through simple commands that enable clinicians to generate personalized response styles, automate recurring clinical tasks, and execute structured workflow and documentation.

These reusable workflows are intended to align the platform more closely with individual user behaviour and decision-making patterns over the period of time.

In addition, the upgrade also introduces a living library of community-created Dotflows. This repository is designed to enable clinicians to browse, adopt workflows, as well as clone Dotflows developed by peers from the library, and share their own Dotflows with the broader community.

Commenting on the new capability, Dr Mondira Ray, SVP of Clinical Informatics at OpenEvidence, said, “The value of any clinical tool depends on how well it fits into the way a clinician actually practices. Dotflows let users define that fit, encoding their personal styles and mental models into the platform itself."

Recently, an AI platform partnered with Mount Sinai Health System to integrate its AI system directly into the electronic health record (EHR) system used at Mount Sinai, aimed at enabling clinicians to access on-demand evidence and clinical insights seamlessly.

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