Authors: (including presenting author): :
Cheng CF, Tong HY, Poon KH, Wong N
Affiliation: :
Department of Medicine, Haven of Hope Hospital
Keyword 1: :
Clinical Workflow
Keyword 2: :
Clinical Guideline
Introduction: :
In current high-pressure clinical environments, healthcare professionals face significant operational inefficiencies regarding time-consuming access to clinical guidelines. Traditional information retrieval methods are often cumbersome, creating bottlenecks that hinder timely decision-making and potentially compromise people-centered care. Consequently, a critical need exists for a scalable, intelligent solution to bridge the gap between extensive protocols and immediate bedside application, thereby better managing growing service demands.
Objectives: :
The primary objective is to drive operational excellence by deploying an advanced AI tool on existing hospital infrastructure. This initiative aims to:
1) Facilitate Lean management by utilizing AI to eliminate the non-value-added waste of searching for information, ensuring rapid access to guidelines.
2) Uphold professional service standards by empowering staff with instant evidence-based support.
3) Enhance resource optimization by leveraging current hardware to improve workflow without significant capital expenditure.
Methodology: :
Implemented starting June 2025, the project utilizes a structured rollout strategy:
1) Cost-Effective Deployment: An AI tool will be deployed on existing ward iPads, maximizing asset utilization to create a centralized knowledge base.
2) Data Synthesis: AI capabilities will classify complex data into accessible formats (mind maps, summaries) to streamline information consumption.
3) Chatbot Integration: An intuitive chatbot will enable natural language queries, facilitating instant analysis during patient care.
4) Change Management: Targeted training during clinical handovers will ensure staff proficiency, promoting smooth adoption and minimizing operational disruption.
5) Evaluation: Effectiveness will be validated through qualitative results derived from staff feedback regarding system intuitiveness and reliability, alongside objective quantitative performance metrics.
Result & Outcome: :
This initiative is expected to fundamentally improve nursing workflows, providing a model for sustainable service enhancement. Performance will be measured against pilot benchmarks:
1) Operational Efficiency: The platform targets an 80% accuracy rate in query resolution, significantly reducing protocol location time.
2) Staff Engagement (Qualitative): We anticipate 66.7% of nursing staff will rate the interface as 'highly intuitive,' validating the tool's role in mitigating cognitive load via positive user feedback.
3) Service Adoption (Qualitative): A primary success metric is integration; the project aims for 83.3% of staff to report the platform as an indispensable daily tool.
This project demonstrates how AI-driven Lean management can strip away administrative barriers to enhance patient safety and workforce well-being, ensuring rigorous standards are maintained amidst growing clinical demands.
