A data management system for precision medicine

John J L Jacobs, Inés Beekers, Inge Verkouter, Levi B Richards, Alexandra Vegelien, Lizan D Bloemsma, Vera A M C Bongaerts, Jacqueline Cloos, Frederik Erkens, Patrycja Gradowska, Simon Hort, Michael Hudecek, Manel Juan, Anke H Maitland- van der Zee, Sergio Navarro-Velázquez, Lok Lam Ngai, Qasim A Rafiq, Carmen Sanges, Jesse Tettero, Hendrikus J A van Os, Rimke C Vos, Yolanda de Wit and Steven van Dijk

PLOS Digital Health, 2025, vol. 4, issue 1, 1-21

Abstract: Precision, or personalised medicine has advanced requirements for medical data management systems (MedDMSs). MedDMS for precision medicine should be able to process hundreds of parameters from multiple sites, be adaptable while remaining in sync at multiple locations, real-time syncing to analytics and be compliant with international privacy legislation. This paper describes the LogiqSuite software solution, aimed to support a precision medicine solution at the patient care (LogiqCare), research (LogiqScience) and data science (LogiqAnalytics) level. LogiqSuite is certified and compliant with international medical data and privacy legislations. This paper evaluates a MedDMS in five types of use cases for precision medicine, ranging from data collection to algorithm development and from implementation to integration with real-world data. The MedDMS is evaluated in seven precision medicine data science projects in prehospital triage, cardiovascular disease, pulmonology, and oncology. The P4O2 consortium uses the MedDMS as an electronic case report form (eCRF) that allows real-time data management and analytics in long covid and pulmonary diseases. In an acute myeloid leukaemia, study data from different sources were integrated to facilitate easy descriptive analytics for various research questions. In the AIDPATH project, LogiqCare is used to process patient data, while LogiqScience is used for pseudonymous CAR-T cell production for cancer treatment. In both these oncological projects the data in LogiqAnalytics is also used to facilitate machine learning to develop new prediction models for clinical-decision support (CDS). The MedDMS is also evaluated for real-time recording of CDS data from U-Prevent for cardiovascular risk management and from the Stroke Triage App for prehospital triage. The MedDMS is discussed in relation to other solutions for privacy-by-design, integrated data stewardship and real-time data analytics in precision medicine. LogiqSuite is used for multi-centre research study data registrations and monitoring, data analytics in interdisciplinary consortia, design of new machine learning / artificial intelligence (AI) algorithms, development of new or updated prediction models, integration of care with advanced therapy production, and real-world data monitoring in using CDS tools. The integrated MedDMS application supports data management for care and research in precision medicine.Author summary: Precision medicine promises more effective disease treatment by stratification and personalization of diagnosis and treatment. Further disease stratification requires more data from more patients and the use of Artificial Intelligence (AI). Traditional Medical Data Management Systems (MedDMSs) are not designed for implementation of precision medicine. We defined, built and applied a MedDMS for precision medicine. A MedDMS for precision medicine would (a) be compliant to the GDPR and other privacy protection guidelines, (b) facilitate multi-center data collaboration in research consortia, (c) allow sharing of existing and new data, (d) provide data ready for machine learning AI to develop predictive algorithms, and (e) allow the data to be used in care settings with clinical decision support tools. We developed LogiqSuite, a MedDMS compliant with these demands. We have selected use-cases from different biomedical fields, like oncology, pulmonology, cardiovascular risk management, and prehospital triage. We used LogiqSuite in multi-centre data registrations and monitoring, data analytics, for AI to develop algorithms for prediction models, to integrate care with advanced medicine production, and for real-world data monitoring.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0000464

DOI: 10.1371/journal.pdig.0000464

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