Diagnostic excellence: turning to diagnostic performance improvement.

Area of Science:

• Healthcare Improvement
• Patient Safety
• Medical Diagnostics

Background:

• Diagnosis is a patient safety priority, with diagnostic errors causing significant harm.
• Diagnostic Excellence Programs are organizational initiatives to reduce errors and improve performance.
• Systematic surveillance is essential for identifying and addressing diagnostic errors.

Purpose of the Study:

• To outline the evolution and components of Diagnostic Excellence Programs.
• To highlight strategies for improving diagnostic processes at both system and individual levels.
• To emphasize the role of patient engagement and artificial intelligence in diagnostic excellence.

Main Methods:

• Review of advancements in diagnostic excellence and error reduction.
• Discussion of frameworks like the DEER Taxonomy and root/success cause analyses.
• Exploration of system factors (EHR, workload, communication) and individual clinician performance enhancement.

Main Results:

• Effective programs integrate system improvements, clinician support, and patient engagement.
• Artificial intelligence offers potential for enhanced surveillance, workflow optimization, and communication.
• Embedding diagnostic safety into standards of care is vital for sustained improvement.

Conclusions:

• Diagnostic Excellence Programs require a multi-faceted approach addressing systems, individuals, and patients.
• Strategic implementation of AI can significantly enhance diagnostic surveillance and improvement.
• Fostering a culture of diagnostic safety is paramount for reducing errors and improving patient outcomes.