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A Tool for Specifying Data Quality Checks for Clinical Data Management Systems – A Technical Case Report
Florian Ulbrich, Frank A. Meineke, Florian Rissner, Alfred Winter, Matthias Löbe
137 - 145
10.3233/SHTI230705
Research Article
Studies in Health Technology and Informatics
Volume 307: German Medical Data Sciences 2023 – Science. Close to People.
Abstract

Introduction:

Prospective data collection in clinical trials is considered the gold standard of clinical research. Validating data entered in input fields in case report forms is unavoidable to maintain good data quality. Data quality checks include both the conformance of individual inputs to the specification of the data element, the detection of missing values, and the plausibility of the values entered.

State-of-the-Art:

Besides Libre-/OpenClinica there are many applications for capturing clinical data. While most of them have a commercial approach, free and open-source solutions lack intuitive operation.

Concept:

Our ocRuleTool is made for the specific use case to write validation rules for Open-/LibreClinica, a clinical study management software for designing case report forms and managing medical data in clinical trials. It addresses parts of all three categories of data quality checks mentioned above.

Implementation:

The required rules and error messages are entered in the normative Excel specification and then converted to an XML document which can be uploaded to Open-/LibreClinica. The advantage of this intermediate step is a better readability as the complex XML elements are broken down into easy to fill out columns in Excel. The tool then generates the ready to use XML file by itself.

Lessons Learned:

This approach saves time, is less error-prone and allows collaboration with clinicians on improving data quality.

Conclusion:

Our ocRuleTool has proven useful in over a dozen studies. We hope to increase the user base by releasing it to open source on GitHub.

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Creative Commons License