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Published on: August 30, 2018
Using the German National Medication Plan for Clinical Studies in Practice-Based Research Networks.
Patrick Schmutz1, Arthur Krauss1, Sven Dörflinger1
1Reutlingen Research Institute, Reutlingen University, Reutlingen, Germany.
The German National Medication Plan (GNMP) offers valuable clinical study data but lacks interoperability. A new solution combines semi-automated data export and processing to improve digital integration for research.
Area of Science:
- Health Informatics
- Clinical Research Data Management
- Digital Health Records
Background:
- The German National Medication Plan (GNMP) is a digital, mandatory record for chronically ill patients.
- Seamless digital transfer of GNMP data from Patient Data Management Systems (PDMS) to electronic case report forms is crucial for clinical studies.
- Current limitations in standardized export and restricted access to pharmaceutical catalogs hinder direct data integration.
Purpose of the Study:
- To address the lack of digital interoperability of the GNMP for clinical study data evaluation.
- To propose a solution that facilitates efficient and accurate data capture in practice-based research networks (PBRNs).
- To reduce the burden of manual data entry in general practitioner (GP) practices during studies.
Main Methods:
- Development of a semi-automated data export process from PDMS at GP practices.
- Implementation of a manual database search at the study center to decode pharmaceutical information.
- Creation of a semi-automated processing pipeline to manage data flow and workload distribution.
Main Results:
- The proposed solution facilitates the integration of GNMP data into clinical studies.
- Workarounds were implemented to overcome current limitations in data export and accessibility.
- The approach aims to balance the workload between GP practices and study management.
Conclusions:
- The combined semi-automated approach enhances the digital interoperability of the GNMP for research purposes.
- This method improves data accuracy and reduces manual effort in clinical study data capturing.
- The solution supports efficient data evaluation in practice-based research settings.
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