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Related Concept Videos

Methods of Documentation II: POMR01:26

Methods of Documentation II: POMR

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.
Methods of Documentation I: Source-Oriented Records01:18

Methods of Documentation I: Source-Oriented Records

Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
Purpose of Health Records II01:19

Purpose of Health Records II

Health records serve various essential purposes in the healthcare system. Here are some key purposes:
Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

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 settings,...
Purpose of Health Records I01:11

Purpose of Health Records I

The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
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Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains for...

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Published on: September 20, 2018

Semantic relations for problem-oriented medical records.

Ozlem Uzuner1, Jonathan Mailoa, Russell Ryan

  • 1University at Albany, State University of New York, 135 Western Ave., Draper 114A, Albany, NY 12222, USA. ouzuner@albany.edu

Artificial Intelligence in Medicine
|July 22, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a semantic relation (SR) classifier for medical discharge summaries, achieving high accuracy in identifying patient problem relationships. Lexical features significantly improve the classification of medical concepts within patient records.

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Area of Science:

  • Natural Language Processing
  • Medical Informatics
  • Machine Learning

Background:

  • Medical discharge summaries contain crucial information about patient problems.
  • Organizing this information effectively is key for problem-oriented medical records.
  • Semantic relation (SR) classification can help structure this data.

Purpose of the Study:

  • To develop and evaluate a semantic relation (SR) classifier for medical discharge summaries.
  • To focus on relations involving patient medical problems, tests, and treatments.
  • To improve the creation of problem-oriented medical records.

Main Methods:

  • Representing patient problems using diseases and symptoms.
  • Analyzing relationships between problems, tests, and treatments within sentences.
  • Employing a support vector machine (SVM) classifier using surface, lexical, and syntactic features.
  • Applying the classifier to two distinct corpora of medical discharge summaries.

Main Results:

  • Achieved micro-averaged F-measures ranging from 74% to 95% on the BIDMC corpus.
  • Achieved micro-averaged F-measures ranging from 68% to 91% on the Partners corpus.
  • Demonstrated that inter-concept lexical tokens are highly informative, leading to 84% (BIDMC) and 72% (Partners) relation recognition.

Conclusions:

  • The developed SR classifier shows promising results for semantic indexing of medical records.
  • Lexical patterns within discharge summaries can be effectively leveraged for sentence-level relation classification.
  • This approach facilitates better organization and retrieval of patient information.