ArticleDigital health
Clinical data integration and processing challenges in healthcare caused by contemporary software design.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Electrochemical liquid biopsy across cancer care pathway.The journal of liquid biopsy · 2026Review
- A leakage-controlled and SHAP driven machine learning framework for paediatric respiratory disease classification using Indian hospital EHR data.BMC medical informatics and decision making · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The quantity of patient data in healthcare is exponentially increasing. While big data and artificial intelligence have emerged across the fields, in healthcare, such rapid development is hindered by numerous factors. Predominantly, health-care software developed decades ago cannot foresee the demands of modern data processing and analysis. We present the challenges, remedies, and steps of efficient patient data integration that have been co-developed with clinicians at Lenval Children's University Hospital in Nice, France. Methods: In collaboration with pediatricians, we created an integration framework that integrated a patient's germane historical data (from the past 10 years) for research purposes. The clinical data presented in this study were collected between 2012 and 2021 in the Lenval Children's University Hospital Pediatric Emergency Department. Results: We present the architecture of a clinical data warehouse (CDW) and demonstrate its use. CDW can also host doctoral notes, which is the key element for creating large language models that can help predict patient outcomes and provide critical information to health-care professionals. We also conducted several tests on the utilization of this new CDW, recorded multiple challenges on data integration, and gave three suggestions on software design. The CDW we created represents a solid foundation for future machine learning models of patient flow, hospital economics, and studies on rare diseases at CHU-Lenval. Conclusion: Although the integration framework is grounded in pediatrics, the challenges discussed, and the proposed remedies are relevant for software development across medical specializations. Our recommendations for software design can help with future secondary usage of Electronic Health Record.
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