Clinical Decision Support Systems: Next big AI use case in Healthcare following Ambient Scribes?
- Lloyd Price
- Jun 20
- 4 min read
The next big AI use case in healthcare following ambient scribes is likely to be AI-powered clinical decision support systems (CDSS) or AI agents for workflow automation. These systems are poised to build on the success of ambient scribes by addressing clinician needs, integrating seamlessly into workflows, and maintaining a strong safety profile, as highlighted by Daniel Yang from Kaiser Permanente.
Why Clinical Decision Support Systems or AI Agents?
High Clinician Demand: Similar to ambient scribes, CDSS and AI agents address clinician burnout by automating complex tasks like synthesizing patient data, suggesting diagnoses, recommending treatments, or managing administrative workflows (e.g., referrals, orders, and coding). Clinicians are eager for tools that enhance decision-making and reduce cognitive load, much like scribes reduced documentation time.
Seamless Integration: Successful AI tools must integrate with electronic health records (EHRs) without disrupting workflows. CDSS can leverage existing EHR data and APIs (e.g., FHIR, HL7) to provide real-time insights, similar to how ambient scribes integrate with EHRs for note generation. AI agents can further automate tasks like scheduling, coding, or patient follow-up, streamlining operations.
Safety Profile: Like ambient scribes, which require clinician oversight to ensure accuracy, CDSS and AI agents can be designed to provide recommendations while keeping clinicians in the loop, minimizing risks to patient care. This human-in-the-loop approach aligns with regulatory and safety requirements, as seen with ambient scribes.
Emerging Traction: Industry leaders are already exploring CDSS and AI agents. For example, Microsoft’s Dragon Copilot automates clinical notes, referrals, and orders, while companies like Abridge and Ambience Healthcare are expanding into coding and workflow automation. Posts on X also highlight AI’s potential in medical coding and proactive revenue cycle management (RCM), indicating growing interest.
Specific Use Cases
Clinical Decision Support: AI can analyze patient data (e.g., EHRs, imaging, labs) to provide real-time diagnostic suggestions, risk predictions (e.g., early disease detection for cancer or stroke), or personalized treatment plans. This builds on ambient scribes by shifting from documentation to decision-making support.
Workflow Automation with AI Agents: AI agents can handle administrative tasks like automated medical coding (e.g., ICD-10, CPT), referral management, or patient visit summaries. For instance, Ambience Healthcare’s AI assigns codes instantly, reducing administrative burden, while Athelas Scribe automates labs, orders, and follow-up pathways.
Patient Engagement: AI could enhance patient-facing tools, such as generating patient-friendly summaries of visits or powering virtual assistants for scheduling and follow-up care, further reducing clinician workload and improving patient experience.
Challenges to Overcome
Adoption Barriers: Unlike ambient scribes, which have near-universal clinician appeal, other AI tools face fragmented interest. CDSS and AI agents must prove clear value to clinicians to achieve widespread adoption.
Financial ROI: Health systems are increasingly demanding measurable returns on investment. While ambient scribes reduce burnout, CDSS and AI agents need to demonstrate cost savings or revenue increases (e.g., through optimised coding or reduced errors).
Accuracy and Safety: AI tools must minimise errors like hallucinations (e.g., fabricating diagnoses) and ensure compliance with regulations like HIPAA. Robust clinician oversight and specialised medical LLMs (e.g., Med-Gemini, Med-PaLM) are critical.
While drug discovery and early disease detection are promising, they lack the immediate clinician-facing impact and ease of integration that made ambient scribes successful. These areas are more research-focused and face longer regulatory and adoption timelines. AI-powered clinical decision support systems and workflow automation agents are the most likely successors to ambient scribes due to their potential to reduce clinician burden, integrate with existing systems, and maintain safety with clinician oversight.
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