Roadmap for the evolution of monitoring: developing and evaluating waveform-based variability-derived artificial...

Area of Science:

• Critical care medicine
• Biomedical engineering
• Health informatics

Background:

• Continuous waveform monitoring is standard for critically ill patients.
• Physiologic variability from waveforms offers diagnostic and prognostic insights.
• Machine learning can derive predictive indices from variability data.

Purpose of the Study:

• To outline the multidisciplinary steps for developing and evaluating predictive clinical decision support tools (CDSS) based on patient monitoring.
• To provide a roadmap for integrating advanced predictive models into clinical practice.

Main Methods:

• Review and analysis of the development and evaluation process for waveform-based predictive models.
• Involves data science, CDSS development, and clinical research phases.
• Addresses technical, analytical, psychological, regulatory, and commercial challenges.

Main Results:

• Development requires a multistep, multidisciplinary approach encompassing data science, CDSS evolution, and clinical research.
• The process includes data collection, waveform processing, machine learning, and various clinical study designs.
• Significant challenges exist across technical, analytical, regulatory, and commercial domains.

Conclusions:

• A proposed roadmap guides the development and evaluation of novel predictive CDSS tools.
• These tools have the potential to transform patient monitoring.
• The integration of predictive models can lead to improved patient care.