A tool to improve the efficiency and reproducibility of research using electronic health record databases

Main Article Content

Mohammad Al Sallakh
Sarah Rodgers
Ronan Lyons
Aziz Sheikh
Gwyneth Davies
Published online: Jun 13, 2018


Background
Interrogation of electronic health record databases often involves time-consuming, manual, repetitive work in developing database queries. We developed a tool to automate this process.


Methods
We identified elementary approaches to query primary care data from the Secure Anonymised Information Linkage databank of Wales. We designed a web-based query builder that allows using combinations of these approaches as ‘building blocks’ to query complex variables. We created an R programme to automatically generate and execute the corresponding Structured Query Language queries.


Results
The tool allows data extraction using combinations of the following methods: event count (e.g., asthma prescriptions); code/date of earliest/latest event; code/date/value of the event of maximum/minimum value; and frequency of temporally constrained events. Query intervals could be fixed, dynamic, or individualised. The tool integrates with a codeset repository. Data extraction procedures and codesets are saved on a web server as versioned, shareable, and citable objects.


Conclusion
This versatile tool allows rapid and complex data extraction with minimal to no programming skills, reduces human errors, and improves research transparency and reproducibility.


Funding/Support
Health and Care Research Wales, ABMU Health Board, AUKCAR (AUK-AC-2012-01), Farr Institute of Health Informatics Research (MR/K006525/1-MR/K007017/1).


Background

Interrogation of electronic health record databases often involves time-consuming, manual, repetitive work in developing database queries. We developed a tool to automate this process.

Methods

We identified elementary approaches to query primary care data from the Secure Anonymised Information Linkage databank of Wales. We designed a web-based query builder that allows using combinations of these approaches as `building blocks' to query complex variables. We created an R programme to automatically generate and execute the corresponding Structured Query Language queries.

Results

The tool allows data extraction using combinations of the following methods: event count (e.g., asthma prescriptions); code/date of earliest/latest event; code/date/value of the event of maximum/minimum value; and frequency of temporally constrained events. Query intervals could be fixed, dynamic, or individualised. The tool integrates with a codeset repository. Data extraction procedures and codesets are saved on a web server as versioned, shareable, and citable objects.

Conclusion

This versatile tool allows rapid and complex data extraction with minimal to no programming skills, reduces human errors, and improves research transparency and reproducibility.

Funding/Support

Health and Care Research Wales, ABMU Health Board, AUKCAR (AUK-AC-2012-01), Farr Institute of Health Informatics Research (MR/K006525/1-MR/K007017/1).

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