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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...

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:

Health Information Technology and Healthcare Information System

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:

Integrated Healthcare System

Integrated Healthcare System

An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...

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: Mar 4, 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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Querying Archetype-Based Electronic Health Records Using Hadoop and Dewey Encoding of openEHR Models.

Erik Sundvall¹, Fang Wei-Kleiner¹, Sergio M Freire²

¹Linköping University, Linköping, Sweden.

Studies in Health Technology and Informatics

|April 21, 2017

Summary

Archetype-based Electronic Health Record (EHR) systems can be optimized for large-scale querying using Dewey encoding. This approach enables efficient data retrieval from millions of patient records with sub-minute response times.

Keywords:

Archetypes Computerized Database Management Systems Dewey encoding Epidemiology Hadoop Medical Record Systems XML openEHR

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Area of Science:

Health Informatics
Computer Science

Background:

Archetype-based Electronic Health Record (EHR) systems, utilizing models like openEHR, aim for flexibility in data model updates.
Ad-hoc, population-wide querying for research, such as epidemiology, presents a significant challenge in these adaptable EHR environments.

Purpose of the Study:

To implement and test an archetype-aware Dewey encoding optimization for EHR systems.
To enhance the efficiency of large-scale, ad-hoc querying within relational database management systems (RDBMS) and distributed frameworks like Hadoop.

Main Methods:

Developed and implemented an archetype-aware Dewey encoding optimization strategy.
Tested the optimization on a nine-node Hadoop cluster with over 4 million real patient EHR records.
Utilized relational operations within the Hadoop framework for data processing and querying.

Main Results:

Achieved sub-minute response times for complex queries on a large EHR dataset.
Demonstrated the feasibility of the Dewey encoding optimization in a distributed computing environment.
Validated the system's capability to handle large-scale, exploratory data analysis.

Conclusions:

The archetype-aware Dewey encoding optimization significantly improves query performance in EHR systems.
This method offers a practical solution for enabling large-scale epidemiological and exploratory research on EHR data.
The approach supports efficient data retrieval without extensive programming or database modifications.