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Methods of Documentation V: CBE01:23

Methods of Documentation V: CBE

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Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...
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Methods of Documentation II: POMR01:26

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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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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Methods of Documentation III: PIE01:21

Methods of Documentation III: PIE

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Problem-intervention-evaluation (PIE) is a systematic approach to documentation used in healthcare settings for clinical decision-making and patient care planning. It is a structured approach to organizing patient data based on problems, interventions, and evaluations. Here's a breakdown of its key features and considerations:
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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.
For example, a patient with a chronic...
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Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

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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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Related Experiment Video

Updated: Jan 23, 2026

Synthesis of Keratin-based Nanofiber for Biomedical Engineering
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Synthesis of Keratin-based Nanofiber for Biomedical Engineering

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ESLMT: a new clustering method for biomedical document retrieval.

MohammadReza Keyvanpour1, Fatemeh Serpush2

  • 1Department of Computer Engineering, Alzahra University, Tehran, Iran.

Biomedizinische Technik. Biomedical Engineering
|June 15, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces the Expanding Statistical Language Modeling and Thesaurus (ESLMT) to improve biomedical document retrieval. The ESLMT method enhances clustering and information organization, leading to more relevant search results for researchers.

Keywords:
MEDLINEMeSH thesaurusbiomedical document retrievalclusteringstatistical language modeling

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Area of Science:

  • Biomedical Informatics
  • Information Retrieval
  • Computational Linguistics

Background:

  • MEDLINE database growth necessitates efficient biomedical document retrieval.
  • Current methods face challenges with compound words, polysemy, synonymy, and high costs for document labeling.
  • Existing retrieval strategies often yield unrelated results, highlighting the need for improved information organization.

Purpose of the Study:

  • To propose an efficient method for clustering and retrieving biomedical documents.
  • To enhance the performance of information retrieval systems in the biomedical domain.
  • To address limitations in current biomedical document retrieval (RBD) processes.

Main Methods:

  • Development and application of the Expanding Statistical Language Modeling and Thesaurus (ESLMT).
  • Utilizing Clustering with ESLM Similarity and Thesaurus (CESLMST) for document clustering.
  • Evaluating the Clusters' Retrieval Derived from ESLM Similarity-Query (CRDESLMS-QET) method using TREC datasets.

Main Results:

  • The CESLMST method demonstrated superior performance across all evaluated criteria compared to other methods.
  • The CRDESLMS-QET method showed significant improvement in mean average precision (MAP).
  • Well-defined clusters were found to be more effective than document-based retrieval.

Conclusions:

  • The ESLMT approach offers a more efficient and effective solution for clustering and retrieving biomedical documents.
  • The proposed methods significantly improve the precision and relevance of search results in MEDLINE.
  • This work contributes to advancing information retrieval systems in biomedical research.