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Efficient goal attainment and engagement in a care manager system using unstructured notes.

Sara Rosenthal1, Subhro Das2, Pei-Yun Sabrina Hsueh1

  • 1IBM Research, Yorktown Heights, New York, USA.

Journal of the American Medical Informatics Association : JAMIA
|March 7, 2020
PubMed
Summary
This summary is machine-generated.

Analyzing unstructured care manager notes (CMNs) using machine learning improves patient goal attainment prediction. This helps identify patients needing extra support for self-management engagement.

Keywords:
evidence-based healthcare managementnatural language processingpatient engagementsupervised machine learning

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Care manager notes (CMNs) contain valuable unstructured text data.
  • Predicting patient self-management goal attainment is crucial for efficient care.
  • Manual annotation for engagement is time-consuming and data for goal attainment is often limited.

Purpose of the Study:

  • To analyze unstructured text in CMNs to improve patient goal attainment prediction.
  • To develop machine learning models for engagement and goal attainment classification.
  • To assess the impact of unstructured text features on model performance and interpretability.

Main Methods:

  • Utilized structured and unstructured CM data from phone interactions.
  • Developed two machine learning classifiers: an engagement model and a goal attainment model.
  • Applied domain adaptation and transfer learning to address data limitations for under-represented classes.

Main Results:

  • Successfully distinguished between patient engagement and lack of engagement automatically.
  • Incorporating engagement and unstructured text features significantly improved goal attainment classification accuracy.
  • Demonstrated the value of unstructured notes for enhancing model performance and interpretability.

Conclusions:

  • Unstructured CMNs enhance the accuracy of predicting patient self-management goal attainment.
  • Machine learning models can identify patients requiring additional care manager attention for self-management.
  • This approach aids in improving patient engagement and timely goal achievement.