Hybrid AI Decision Support in Healthcare: Insights into Usability and Trust

Clinical Snapshot

Key Findings

• Effect Size: Not applicable for qualitative study

• Primary Outcome: Good usability scores with moderate trust levels for hybrid AI decision support system

• Nnt Or Sensitivity: Highest acceptance for traceability, workflow integration, and autonomy features

Clinical Application

Demonstrates technical feasibility but requires attention to confidence indicators and contextual reasoning before widespread implementation Relevant to Australian digital health initiatives and TGA software as medical device regulations; aligns with RACGP emphasis on evidence-based preventive care Healthcare professionals using clinical decision support systems, particularly in preventive care settings

Abstract

Decision Support Systems (DSS) increasingly integrate Artificial Intelligence (AI) to enhance clinical reasoning, yet adoption depends on professionals' trust and usability. This study explored how hybrid AI decision support, combining rule-based guideline logic with generative reasoning, can be designed and evaluated in practice. Using a human-centred design approach within preventive child healthcare, a prototype integrating Large Language Models (LLMs) into an existing guideline-based DSS was developed and tested. Usability and trust were evaluated with nine healthcare professionals through task observations, usability scales, and interviews. The system showed good usability and moderate trust; highest scores were for traceability, workflow integration, and autonomy, while confidence indicators and contextual reasoning needed improvement. The study demonstrates the feasibility and acceptability of hybrid AI DSS in child healthcare and provides initial design requirements for usable, trustworthy, and human-centred AI integration across clinical domains.