ENTenna AI Driven Chronic Disease Management Platform
Care Process & Redesign
Technology
National Healthcare Innovation and Productivity Medals
National Healthcare Group
National University Health System
SingHealth
26 December 2025
To develop a novel model of care for chronic conditions (AR as use case) through AI analytics of multimodal patient data. The integration of clinical and technological solutions optimized the management of AR using AI-driven diagnostics and digital health.
Year Submitted: 2025
Published Date: 26 December 2025
Tags: Technology, Care Continuum, Safety Evaluation, Wearables
About this Content
Aims
To develop a novel model of care for chronic conditions (AR as use case) through AI analytics of multimodal patient data (digital, clinical, molecular, geospatial) integrated into care pathways. Leverage a data-driven, AI-supported chatbot interface for patient engagement and predictive personalised behavioural modification to improve treatment adherence.
Background
Approximately 90% of patients with Allergic Rhinitis (AR) are insufficiently treated. There is a critical gap in AR management: only 11% of patients adhere to their medication regimen, indicating insufficient patient empowerment. AR prevalence is high (39%), indicating a high burden of disease. Symptom trends & patient-reported outcome data are not routinely collected, resulting in a gap internationally in Clinical Practice Guidelines (CPG) development.
Methods
The approach included AI-driven risk stratification for enhanced screening & diagnosis, personalized treatment pathways using Generative AI and LLM models, and a WhatsApp AI chatbot for patient self-management.
Results
Improvement in Medication Adherence & Outcomes: Digital adherence tracking via AI-powered health platform provided real-time insights. Preliminary results across 6 months showed significant improvements in medication adherence and symptom control, as well as reduction in healthcare utilisation. Improvement in Cost Avoidance: 25% of patients with low symptom scores were right-sited to the community, with estimated cost avoidance of S$1.4k per patient. Patients with mid-high symptom scores also benefitted from improved management of conditions. A preliminary cost-effectiveness analysis provided an ICER estimate of S$19,995 per QALY, demonstrating cost-effectiveness.
Conclusion
The integration of clinical and technological solutions optimized the management of AR using AI-driven diagnostics and digital health interventions, demonstrating significant improvements in patient outcomes and cost-effectiveness.
Lessons Learnt
Stakeholder Engagement: Involving primary care physicians, ENT specialists & AI experts early in the process ensured alignment & streamlined clinical integration. Regulatory and Ethical Considerations: Compliance with ethics (IRB), data security (SRB-approved infrastructure), and regulatory requirements (HSA classification) were vital for implementation. Iterative Refinement: Successful deployment of AI tools in healthcare requires continuous iterations based on real-world data, clinician feedback, and patient interaction insights to optimize performance and usability.
Additional Information
This project has won National Healthcare Innovation and Productivity Awards 2025 under Best Practice (Automation, IT and Robotics Innovation) category.
Keywords
Artificial Intelligence, Adherence, Digital Health, Patient Engagement, Population Health, Cost Effectiveness
Innovators' Details
Innovators' Details
Healthcare Cluster(s) | National University Health System, National Healthcare Group, SingHealth |
Organization(s) Involved | Ng Teng Fong General Hospital, Tan Tock Seng Hospital, Changi General Hospital, National University Hospital |
Platform(s) | National Healthcare Innovation and Productivity Medals |
Healthcare Professional Group(s) | Healthcare Administration, Medical, Nursing |
Applicable Specialty or Discipline | Healthcare Administrators, Clinical Research, General Research, InfoTech, Operations, Medical, Otolaryngology |
Project Lead(s) | Ng Chew Lip |
Project Member(s) | Christine Wu Xia |
Connect with this contributor!
Ng Chew Lip - chew_lip_ng@nuhs.edu.sg
Christine Wu Xia - christine_wu@nuhs.edu.sg