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Methods of Documentation I: Source-Oriented Records

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:

Methods of Documentation II: POMR

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 VII: EMR

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...

Methods of Documentation III: PIE

Methods of Documentation III: PIE

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:

Purpose of Health Records II

Purpose of Health Records II

Health records serve various essential purposes in the healthcare system. Here are some key purposes:

Purpose of Health Records I

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.
Here's a breakdown of how health records serve these purposes:

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Related Experiment Video

Updated: Apr 12, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

TagLine: Information Extraction for Semi-Structured Text in Medical Progress Notes.

Dezon K Finch¹, James A McCart², Stephen L Luther²

¹James A. Haley Veterans Hospital, Tampa, FL ; University of South Florida, Tampa, FL.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|May 9, 2015

Summary

TagLine, a new system, effectively extracts data from semi-structured medical text using machine learning and rules. It achieves high accuracy in identifying and annotating information within tables, slots, and fillers.

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Last Updated: Apr 12, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Area of Science:

Medical Informatics
Natural Language Processing
Machine Learning

Background:

Statistical text mining and natural language processing (NLP) are useful for medical documents but struggle with semi-structured text.
Extracting information from semi-structured text (e.g., tables, slots, fillers) in medical documents remains a challenge.

Purpose of the Study:

To develop and evaluate a prototype system, TagLine, for extracting information from semi-structured medical text.
To improve the accuracy and efficiency of information extraction from diverse text formats within medical records.

Main Methods:

Developed TagLine, a system combining machine learning and a rule-based annotator.
Utilized features from prior work and empirical text examination to train machine learning models.
Defined text line classes guided by an ontology from the VHA's Consortium for Health Informatics Research (CHIR).
Employed decision trees for text line classification.

Main Results:

TagLine achieved 98.5% overall accuracy in classifying 15,103 lines of text.
The system demonstrated high performance, with an F-measure exceeding 0.9 for annotating tables, slots, and fillers.
Successfully applied class labels for annotating semi-structured text elements.

Conclusions:

TagLine demonstrates a highly accurate and effective approach for extracting information from semi-structured medical text.
The integration of machine learning and rule-based methods, guided by an ontology, significantly enhances data extraction capabilities.
This system offers a promising solution for leveraging previously inaccessible information within medical documents.