From Joint Cognitive Systems to Human-AI Joint Cognitive Systems: A Theory Critique and Application to Obstetric...

Area of Science:

• Human-computer interaction
• Cognitive systems engineering
• Medical informatics

Background:

• The joint cognitive systems framework provides a basis for understanding human-machine collaboration.
• Evaluating this framework is crucial for advancing human-AI interaction in complex domains.
• Existing frameworks may not fully address the nuances of clinical decision-making.

Purpose of the Study:

• To examine the joint cognitive systems framework for human-machine collaboration.
• To critique the framework's strengths and limitations in healthcare settings.
• To propose an extension incorporating ethical and sociocultural factors for improved clinical decision-making.

Main Methods:

• Utilized Meleis's theory evaluation model to analyze the joint cognitive systems framework.
• Reviewed principles, origins, and applications of the framework.
• Applied the framework to obstetric anesthesia for risk assessment analysis.

Main Results:

• Identified challenges in trust, workload, and integration hindering effective healthcare application.
• Demonstrated potential for improved maternal risk assessment by combining provider expertise with machine learning.
• Highlighted the need for a modernized framework to address ethical and sociocultural dimensions.

Conclusions:

• The joint cognitive systems framework offers a foundation but requires enhancement for clinical practice.
• A modernized human-AI joint cognitive systems approach is proposed to integrate ethics and sociocultural factors.
• This extension aims to support safer and more effective clinical decision-making in healthcare.