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Related Concept Videos

Methods of Documentation VII: EMR01:30

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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Frailty Assessment in an Aging Mouse Model
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Developing and Validating a Primary Care EMR-based Frailty Definition using Machine Learning.

PhD Tyler Williamson1,2,3, Sylvia Aponte-Hao1, Bria Mele1

  • 1Department of Community Health Sciences, Cumming School of Medicine, University of Calgary.

International Journal of Population Data Science
|September 16, 2020
PubMed
Summary
This summary is machine-generated.

Developing a frailty case definition for electronic health records showed low sensitivity but high specificity. Further research is needed to improve frailty identification in primary care.

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Area of Science:

  • Gerontology
  • Primary Care Medicine
  • Health Informatics

Background:

  • Frailty is a state of vulnerability associated with adverse health events and increased healthcare costs.
  • Accurate identification of frail individuals is crucial for timely intervention and improved patient outcomes.

Purpose of the Study:

  • To develop and validate a machine learning-based case definition for frailty.
  • To enable frailty identification within primary care electronic medical record databases.

Main Methods:

  • A cross-sectional validation study was conducted using data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN).
  • A random sample of 875 patients aged over 65 were assessed using the Rockwood Clinical Frailty Scale.
  • Machine learning algorithms were employed to develop the frailty case definition based on CPCSSN records.

Main Results:

  • The developed case definition achieved a specificity of 0.94 (95% CI: 0.93-0.96) but a low sensitivity of 0.28 (95% CI: 0.21-0.36).
  • Positive Predictive Value (PPV) was 0.53 (95% CI: 0.42-0.64) and Negative Predictive Value (NPV) was 0.86 (95% CI: 0.83-0.88).

Conclusions:

  • The low sensitivity and specificity suggest that current case definitions may not fully capture the complexity of frailty.
  • Expert consensus and more sophisticated algorithms are likely necessary for successful frailty case definition development.
  • Further research is needed to refine frailty identification in primary care settings.