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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.

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:

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

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:

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Related Experiment Video

Updated: Jun 28, 2025

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

Building large-scale registries from unstructured clinical notes using a low-resource natural language processing

Nazgol Tavabi¹, James Pruneski¹, Shahriar Golchin²

¹Department of Orthopaedic Surgery and Sports Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.

Artificial Intelligence in Medicine

|April 24, 2024

Summary

A new method, Sentence Extractor with Keywords (SE-K), efficiently extracts data from clinical notes for registries. SE-K outperforms complex models like BERT, offering speed and interpretability for improved patient care and research.

Keywords:

ACL Clinical notes Electronic health records Natural language processing Registry building

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Area of Science:

Medical Informatics
Natural Language Processing
Clinical Research

Background:

Clinical registries are vital for research and patient care quality.
Natural Language Processing (NLP) aids in extracting information from unstructured clinical notes.
Clinical notes present unique challenges for standard NLP models.

Purpose of the Study:

To introduce Sentence Extractor with Keywords (SE-K), an efficient and interpretable NLP approach.
To compare SE-K's performance against embedding-based NLP methods (SE-E, BERT).
To develop a comprehensive registry of anterior cruciate ligament surgeries using clinical data.

Main Methods:

Utilized SE-K, SE-E, and BERT for information extraction from clinical notes.
Applied methods to 20 years of unstructured clinical data from a children's hospital.
Evaluated performance using out-of-sample validation and AUROC metrics.

Main Results:

SE-K demonstrated superior performance (AUROC 0.94 ± 0.04) compared to SE-E (0.93 ± 0.04) and BERT (0.87 ± 0.09).
SE-K showed minimal performance drop in out-of-sample validation.
SE-K was at least six times faster than SE-E and BERT, offering interpretability.

Conclusions:

SE-K is an effective, efficient, and interpretable method for clinical note analysis.
SE-K facilitates large-scale registry building, quality improvement, and adverse event monitoring.
This approach offers a valuable alternative to resource-intensive NLP methods for clinical data extraction.