Real-Time Care Recommendations in Healthcare Using Agent-to-User Interaction (A2UI) on the Salesforce Ecosystem
Author(s): Brahmananda Naidu Dabbara
Publication #: 2606012
Date of Publication: 20.05.2026
Country: United States
Pages: 1-6
Published In: Volume 12 Issue 3 May-2026
DOI: https://doi.org/10.62970/IJIRCT.v12.i3.2606012
Abstract
The rapid digitization of healthcare systems, driven by the proliferation of Electronic Health Records (EHRs), Internet of Things (IoT) devices, and digital patient engagement platforms, has created an urgent need for real- time, intelligent, and actionable clinical decision support. Traditional Clinical Decision Support Systems (CDSS) are predominantly reactive and query- driven, limiting their effectiveness in time- sensitive care scenarios. This paper introduces a novel framework for real- time care recommendations using Agent- to- User Interaction (A 2 UI) within the Salesforce ecosystem, designed to bridge the gap between predictive analytics and clinical action.
The proposed architecture integrates event- driven data ingestion, unified patient intelligence via Salesforce Data Cloud, and advanced AI models, including predictive analytics and generative AI, to continuously monitor patient conditions and generate context- aware risk scores. These insights are transformed into actionable recommendations and delivered directly within clinician and patient interfaces via an A 2 UI layer, enabling proactive, workflow- integrated decision- making. The system supports multiple interaction modalities, including alert- based notifications, recommendation cards, guided workflows, and conversational AI interfaces.
A key contribution of this research is formalizing A 2 UI as a distinct interaction and architectural layer that enables seamless human- AI collaboration. The framework incorporates explainability mechanisms, confidence scoring, and human- in- the- loop controls, ensuring transparency, trust, and compliance with healthcare regulations. Additionally, the system leverages a closed- loop feedback mechanism to continuously improve model performance based on user actions and outcomes.
The proposed solution is evaluated using large- scale healthcare datasets, demonstrating significant improvements in clinical response time (up to a 60% reduction), patient engagement (approximately a 35% increase), and care coordination efficiency (over a 25% improvement), while achieving high predictive performance with ROC- AUC scores approaching 0. 0.9. These results highlight the effectiveness of combining real- time data processing, AI- driven intelligence, and interaction- centric design.
In conclusion, this paper establishes A 2 UI as a transformative paradigm for next- generation healthcare systems, enabling a shift from reactive analytics to proactive, intelligent, and patient- centric care delivery. The framework offers a scalable, enterprise- ready blueprint for integrating real- time AI capabilities into clinical workflows, positioning it as a foundational approach for modern digital health platforms.
Keywords: Agent-to-User Interaction (A2UI), Real-Time Healthcare Systems, Clinical Decision Support Systems (CDSS), AI-Driven Care Recommendations, Salesforce Data Cloud, Einstein AI, Agentforce, Predictive Analytics in Healthcare, Explainable AI (XAI), Event-Driven Architecture, Patient 360, Remote Patient Monitoring (RPM), Human-in-the-Loop AI, Next Best Action (NBA), Workflow Automation
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