Agentic Clinical Intelligence Platform (ACIP)

1. Background and Context
Generative AI (GenAI) offers transformative potential in healthcare but faces significant challenges: hallucinations and unreliable outputs, data quality/bias issues, high costs and unclear ROI, integration difficulties, privacy/security risks, lack of transparency, governance gaps, and workforce adoption barriers. At the same time, wearable digital health technologies enable continuous real-world physiological monitoring; AI systems for ECG analysis can improve diagnostic accuracy and efficiency when designed for meaningful human–AI collaboration; and ambient GenAI tools can substantially reduce clinical documentation burden. These capabilities remain largely siloed. A unified, robust software architecture is needed to orchestrate them safely and effectively.

2. Project Vision
Develop and validate a service-oriented multi-agent software architecture that orchestrates wearable data streams, AI-assisted ECG interpretation (with strong human–AI collaboration), and GenAI-powered clinical documentation into a cohesive, clinician-centered clinical decision-support and workflow system. The platform will explicitly address GenAI challenges through human-in-the-loop design, governance, observability, and trustworthiness mechanisms.3. Primary Objectives

·       Design and implement a service-oriented multi-agent architecture (aligned with Agentic Service-Oriented Computing principles) in which specialized agents handle wearable data ingestion/processing, ECG analysis, documentation generation, clinical reasoning support, and governance/oversight.

·       Integrate continuous wearable physiological data with AI-driven ECG analysis that prioritizes human–AI collaboration (e.g., uncertainty-aware outputs, set-valued predictions, explanations, and clinician override/feedback loops).

·       Deploy ambient/GenAI clinical documentation capabilities that generate high-quality draft notes, summaries, and coding suggestions, with mandatory clinician review and clear accountability.

·       Embed technical and organizational safeguards that mitigate core GenAI risks (reliability, bias, privacy, cost control, transparency, and adoption).

Evaluate the integrated system in realistic clinical workflows for impact on diagnostic accuracy, documentation efficiency, clinician workload/burnout, patient safety, and operational productivity.

3**. Scope**
In scope

• Multi-agent service architecture (agents as independent, composable services with clear APIs, discovery, orchestration, and lifecycle management).

• Integration of selected wearable devices (ECG-capable patches/watches, continuous vital-sign sensors, etc.).

• Human–AI collaborative ECG analysis module (building on concepts such as SHAPE-style closed-loop systems).

• Ambient GenAI documentation pipeline with clinician review/edit workflow and EHR integration.

• Governance, observability, audit, and human-oversight layers.

• Pilot evaluation in one or more clinical settings (e.g., cardiology outpatient/inpatient or remote monitoring programs).

Out of scope (initial phase)

• Full autonomous clinical decision-making without human oversight.

• Broad multi-disease coverage beyond cardiovascular focus.

• Large-scale multi-center deployment or commercial productization.

4. Key Components / Work Packages

• WP1 – Architecture & Platform: Service-oriented multi-agent framework, agent orchestration, interoperability standards, security, and observability.

• WP2 – Wearable Integration: Secure data pipelines, signal processing, quality control, and real-time/near-real-time fusion.

• WP3 – Human–AI ECG Collaboration: Model development/adaptation, uncertainty quantification, explanation interfaces, feedback loops, and clinical workflow embedding.

• WP4 – GenAI Documentation: Ambient capture, structured note generation, coding support, version control, and mandatory human sign-off mechanisms.

• WP5 – Governance, Safety & Trust: Risk mitigation for hallucinations/bias, privacy-preserving techniques, audit trails, policy enforcement, and clinician training.

• WP6 – Evaluation & Iteration: Clinical pilot, quantitative/qualitative metrics, continuous improvement via real-world feedback.

5. Expected Outcomes and Deliverables

• Reference architecture and open (or controlled) implementation of the multi-agent platform.

• Working prototype integrating the three core clinical capabilities.

• Documented governance framework and risk-mitigation strategies specific to GenAI in clinical use.

• Pilot study results demonstrating measurable improvements in accuracy, efficiency, and clinician experience while maintaining or enhancing safety.

• Guidelines and lessons learned for broader adoption of agentic AI systems in digital health.