Reinventing Clinical decision support systems

Clinical Decision Systems

Software that supports clinical decision-making

• Aims to enhance quality, safety, and efficiency in healthcare

Promise of AI

• Faster analysis of large datasets

• Improved diagnostic accuracy

• Potential for personalized medicine

The limits of AI

• Risk of bias and misinformation if poorly trained

• Lack of transparency

• Requires high-quality, standardized data

ISO 42001

first international standard for AI management systems, guiding organizations on responsible AI deployment

OECD AI Principles

widely adopted global framework emphasizing human-centered AI, transparency, and accountab

Alerts

designed to prevent immediate harm; triggered in real time when a clinician is about to make a potentially unsafe decision

Reminders

support long-term care planning; prompt clinicians or patients about upcoming or overdue preventive or follow-up actions.

Patient-centered CDS

Goes beyond helping doctors → empowers patients

Centralized models

stores all patient data in one repository, making AI training easier and faster

Federate model

data stays in each hospital, while AI algorithms travel to the data

AI as Augmented intelligence

• AI to support, not replace clinicans

• Tools to reduce workload, improve decision-making

Oversight and Monitoring

• Continuous monitoring of CDS tools

• Strong regulatory oversight needed

• Prevent bias, errors, and unsafe recommendations

Balancing Innovation and trust

Technology should enhance clinician-patient interaction