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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.

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Related Experiment Video

Updated: Jan 5, 2026

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

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Incrementally Transforming Electronic Medical Records into the Observational Medical Outcomes Partnership Common Data

Kristine E Lynch^1,2, Stephen A Deppen³, Scott L DuVall^1,2

¹VA Salt Lake City Health Care System, Salt Lake City, Utah, United States.

Applied Clinical Informatics

|October 24, 2019

Summary

Developing a quality assurance process for transforming health care data into common data models (CDMs) prevents errors during incremental updates, ensuring accurate clinical information capture.

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Area of Science:

Health Informatics
Data Management
Clinical Research

Background:

Health care common data models (CDMs) standardize data from disparate systems for research.
Transforming and maintaining enterprise-capable CDMs within data warehouses presents significant challenges.

Purpose of the Study:

To develop a quality assurance (QA) process and code base for the incremental transformation of the Department of Veterans Affairs Corporate Data Warehouse into the Observational Medical Outcomes Partnership (OMOP) CDM.
To prevent incremental load errors during this data transformation process.

Main Methods:

Implemented a multistage QA approach focusing on completeness, value conformance, and relational conformance data-quality elements.
Described key incremental load challenges, the extract, transform, and load (ETL) solutions, and the impacts of potential load failures.

Main Results:

Incremental changes to the Corporate Data Warehouse primarily affect completeness and value conformance.
Updates to source identifiers impact relational conformance, potentially leading to data fragmentation and inaccurate clinical concept capture.
ETL failures can result in incomplete or fragmented patient, provider, and location data.

Conclusions:

Robust QA processes for accurate CDM transformation are still evolving.
Opportunities exist to extend current QA frameworks and tools for incremental ETL processes.