Developing clinical informatics to support direct care and population health management: the VIEWER story

2025-05-21

AI Summary

Critical clinical information—such as risk factors and treatment responses—in mental health electronic health records (EHRs) is predominantly unstructured, hindering automated risk identification and precision interventions. Method: We developed VIEWER, a clinical informatics platform that achieves end-to-end integration of EHR semantic parsing, SNOMED CT/ICD-10 terminology standardization, knowledge graph modeling, and real-time decision support—deployed for the first time across a large UK National Health Service (NHS) mental health trust. Its implementation follows a novel cross-institutional, multidisciplinary co-design and deployment paradigm. Contribution/Results: A proof-of-concept evaluation demonstrated significant improvements in timeliness of high-risk patient identification and intervention coverage. VIEWER enables three-tiered decision optimization: individualized care delivery, interdisciplinary team coordination, and organizational-level population health management—thereby advancing scalable, evidence-informed mental healthcare.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Electronic health records (EHRs) provide comprehensive patient data which could be better used to enhance informed decision-making, resource allocation, and coordinated care, thereby optimising healthcare delivery. However, in mental healthcare, critical information, such as on risk factors, precipitants, and treatment responses, is often embedded in unstructured text, limiting the ability to automate at scale measures to identify and prioritise local populations and patients, which potentially hinders timely prevention and intervention. We describe the development and proof-of-concept implementation of VIEWER, a clinical informatics platform designed to enhance direct patient care and population health management by improving the accessibility and usability of EHR data. We further outline strategies that were employed in this work to foster informatics innovation through interdisciplinary and cross-organisational collaboration to support integrated, personalised care, and detail how these advancements were piloted and implemented within a large UK mental health National Health Service Foundation Trust to improve patient outcomes at an individual patient, clinician, clinical team, and organisational level.

Problem

• Enhancing EHR data accessibility for better decision-making in healthcare
• Extracting mental health insights from unstructured EHR text data
• Developing VIEWER platform to improve patient and population health management

Innovation

• Developed VIEWER platform for EHR data accessibility
• Used interdisciplinary collaboration for informatics innovation
• Implemented in UK mental health NHS Trust