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Artificial intelligence and machine learning can identify care bundles for type 2 diabetes mellitus (T2DM) patients. Integrating diverse data sources improves AI/ML models for better inpatient care and reduced practice variance.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support

Background:

  • Electronic Health Records (EHR) data analysis is crucial for optimizing inpatient care.
  • Identifying effective care bundles can reduce practice pattern variance and improve patient outcomes.
  • Artificial Intelligence/Machine Learning (AI/ML) offers potential for analyzing complex health data.

Purpose of the Study:

  • To review the literature on AI/ML applications for identifying care bundles in inpatient settings.
  • To assess if AI/ML-identified care bundles impact practice patterns and patient outcomes for T2DM.
  • To explore the types of AI/ML models and data sources used in this domain.

Main Methods:

  • Scoping review of six databases (Jan 2000 - Jan 2024).
  • Inclusion criteria focused on AI/ML use in analyzing inpatient EHR data for care bundles.
  • Nine studies were selected and summarized based on predefined criteria.

Main Results:

  • Various AI/ML models were employed, utilizing diverse data sources beyond traditional EHR data.
  • Studies addressed therapeutic patterns, treatment pathways, clinical decision support dashboards, and medication optimization.
  • AI/ML shows potential but requires broader, multidisciplinary data, including nursing and ancillary data, for optimal effectiveness.

Conclusions:

  • AI/ML can identify appropriate interventions for diabetes management and support treatment adherence.
  • Incorporating diverse data sources beyond EHR is essential for developing robust AI/ML models.
  • Further research is needed on utilizing AI/ML with nursing and ancillary data for inpatient diabetes management.