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Related Concept Videos

Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
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Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
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Classification of Illness01:17

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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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The Problem-Oriented Medical Record (POMR) revolutionized medical record-keeping by introducing a systematic approach focusing on the patient's problems rather than merely listing symptoms. Dr. Lawrence Weed's introduction of this method in the 1960s marked a significant advancement in medical documentation. The POMR framework consists of four key components: the database, problem list, plan of care, and progress notes.
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Related Experiment Video

Updated: Nov 22, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and

Thanh Thieu1, Jonathan Camacho Maldonado2, Pei-Shu Ho2

  • 1Oklahoma State University, Stillwater, OK, United States.

International Journal of Medical Informatics
|January 5, 2021
PubMed
Summary

This study introduces a novel method for extracting mobility functioning information from electronic health records. Our approach reliably captures patient function data, improving health status assessment.

Keywords:
Clinical notesFunctioning informationMobilityNamed entity recognitionNatural language processingText mining

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Rehabilitation Medicine

Background:

  • Secondary use of Electronic Health Records (EHRs) traditionally focuses on diseases and drugs, overlooking patient function.
  • Patient function is a critical health indicator, yet it remains underutilized in health status assessments.
  • The World Health Organization's International Classification of Functioning, Disability and Health (ICF) provides a standard for describing function, but its application in EHRs is limited.

Purpose of the Study:

  • To pioneer the first comprehensive analysis and identification of functioning concepts within the Mobility domain of the ICF.
  • To develop and evaluate a high-performance machine learning model for recognizing mobility-related entities in clinical notes.

Main Methods:

  • A hierarchical order of mobility-related entities (types, relations, attributes, values) was induced from physical therapy notes.
  • A gold standard corpus of 14,281 nested entity mentions was manually curated by domain experts from 400 clinical notes.
  • An Ensemble machine learning model for named entity recognition (NER) was trained and evaluated on the curated corpus.

Main Results:

  • High inter-annotator agreement (92.3% F1-score, 96.6% Cohen's kappa) was achieved in corpus curation.
  • The developed Ensemble NER model achieved an average F1-score of 84.90% for exact entity matching.
  • This performance surpassed popular NER methods like CRF, RNN, and BERT.

Conclusions:

  • Mobility functioning information can be reliably extracted from clinical notes using advanced sequence labeling methods.
  • The study demonstrates the feasibility of identifying ICF functioning concepts in EHR data.
  • This approach can be extended to identify functioning concepts in other ICF domains.