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Innovations in chronic care delivery using data-driven clinical pathways.

Yiye Zhang1, Rema Padman

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This study used machine learning on electronic health records to identify chronic kidney disease patient subgroups and predict care pathways. The approach aids in reviewing practices and innovating healthcare delivery.

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Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Chronic Disease Management

Background:

  • Chronic diseases are complex and costly, necessitating innovative healthcare delivery.
  • Electronic Health Records (EHRs) offer rich data for improving care.
  • Information technology and analytics can enhance chronic disease management.

Purpose of the Study:

  • To propose a data-driven approach for developing clinical pathways from EHR data.
  • To apply this approach to chronic kidney disease (CKD) care delivery.
  • To identify patient subgroups and predict care trajectories.

Main Methods:

  • Analysis of structured, de-identified EHR data from 664 CKD patients (2009-2013).
  • Utilized machine learning to learn data-driven clinical pathways.
  • Modeled patient subgroups based on diagnoses, medications, and biochemical measurements.

Main Results:

  • Identified 7 distinct patient subgroups among CKD stage 3 hypertensive patients.
  • Achieved up to 44% accuracy in learning probable clinical pathways.
  • Demonstrated up to 75% accuracy in predicting future patient states.

Conclusions:

  • Data-driven pathway learning from EHRs can summarize complex patient journeys.
  • Identified patient clusters and sequences of care can inform practice review.
  • This method may reveal opportunities for innovation in healthcare delivery.