Microsoft has launched Microsoft Dragon Copilot, an AI assistant for clinical workflow, bringing together natural language voice dictation and ambient listening capabilities “with fine-tuned generative AI and healthcare-adapted safeguards”, said to support documentation, surfacing important information, and task automation.
Examples of functionality available to clinicians include the ability to create clinical documentation, with Copilot capturing patient-clinician conversations and orders, converting them into “high quality, comprehensive, specialty-specific notes”. Clinicians can also surface the information they need without leaving their workflow, offering details on questions such as whether a patient is taking a certain medication, or has any relevant family history. In terms of task automation, Microsoft points to Copilot’s capacity to provide clinicians with an “instant synopsis” of patient encounters, to offer diagnosis evidence from symptoms and lab or imaging reports, and to create referral letters using information gathered during an appointment.
Microsoft also highlights Dragon Copilot’s foundations in “responsible AI”, sharing that its capabilities are “built on a secure data estate” and incorporate “healthcare-specific clinical, chat, and compliance safeguards for accurate and safe AI outputs”. Data is also “grounded in privacy principles, backed by transparent policies, and protected by rigorous safeguards”, the announcement shares.
For partners, Dragon Copilot also reportedly offers opportunities to leverage a “comprehensive end-to-end toolchain” comprised of a suite of Microsoft solutions including Fabric, Copilot Studio, and Azure AI Foundry. Use cases shared include the embedding of Dragon Copilot into EHRs to improve clinical workflows, and its use for patient medical history summarisation and patient response drafting.
Cathy Turner, chief marketing and nurse executive for MEDITECH, referred to the launch of Dragon Copilot as “a significant step forward” in alleviating some of the challenges faced by clinicians, adding: “Integrating this innovative solution directly into Expanse streamlines documentation and ordering processes, reduces cognitive overload, and ultimately empowers providers to deliver superior, more patient-centered care.”
AI strategy and use cases across the health and care sector
A recent HTN Now webinar focused on the practicalities of AI technologies, exploring topics including implementation, adoption, the role of data, policy, regulation, evaluation and best practices. With the help of our expert panellists, we also took a closer look at examples of AI in health and care.
We caught up with Peter van Ooijen – professor of AI in Radiotherapy and coordinator of the Machine Learning Lab at University Medical Center Groningen, and former president of the European Society of Medical Imaging Informatics (EuSoMII) – for a recent interview, to talk about new technologies and future directions for medical imaging and radiotherapy.
HTN covered the publication of a new framework for implementing and monitoring AI in health and care across the London region, which focused on five key areas: partnership, infrastructure and data, use cases, AI delivery approach, and communication and workforce development. It spans governance, roles, responsibilities, delivery lifecycles, proof of concepts, pilots, data pipelines, business as usual, scaling, through to monitoring. Developed in collaboration with digital leaders from South East London NHS, AI experts, the AI Centre for Value Based Healthcare, and the Health Innovation Network, it represents, according to an executive summary, “an overview of the agreed way of implementing and monitoring artificial intelligence products in the London health and care system”.
