Considerations for the implementation of machine learning into acute care settings.

Area of Science:

• Utilizes artificial intelligence, specifically machine learning (ML), for cognitive augmentation in clinical settings.
• Focuses on the application of ML in acute care and intensive/critical care units.

Background:

• Acute care demands accurate diagnosis and rapid treatment, creating a need for technological support.
• Machine learning integration in healthcare promises to enhance clinical decision-making and patient care.

Purpose of the Study:

• To explore the role and implications of machine learning in acute care settings.
• To review the application of ML in intensive/critical care units using electronic health records.

Main Methods:

• Literature search of PubMed and Google Scholar using keywords: ML, intensive/critical care unit, EHR, anesthesia information management systems, clinical decision support.
• Analysis of different ML categories: supervised, unsupervised, and reinforcement learning, applied to clinical datasets.

Main Results:

• ML training utilizes large clinical datasets from electronic health records (EHRs).
• Different ML approaches (supervised, unsupervised, reinforcement learning) are applied to predict patient outcomes.
• Concerns regarding fairness, transparency, privacy, and accountability in ML applications are identified.

Conclusions:

• Well-resourced systems are poised to benefit most from ML, highlighting potential healthcare disparities.
• Urgent attention is needed to extend ML benefits to less-resourced healthcare systems to prevent widening healthcare gaps.