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A clinician friendly data warehouse oriented toward narrative reports: Dr. Warehouse.
Nicolas Garcelon1, Antoine Neuraz2, Rémi Salomon3
1Institut Imagine, Paris Descartes Université Paris Descartes-Sorbonne Paris Cité, Paris, France; INSERM, Centre de Recherche des Cordeliers, UMR 1138 Equipe 22, Université Paris Descartes, Sorbonne Paris Cité, Paris, France.
Journal of Biomedical Informatics
|March 5, 2018
Summary
Dr. Warehouse is an open-source clinical data warehouse designed for narrative reports, enhancing translational research. It supports clinicians with advanced text exploration and cohort building, achieving high user satisfaction.
Area of Science:
- Biomedical Informatics
- Clinical Data Management
- Natural Language Processing
Background:
- Clinical data warehouses typically focus on coded data, limiting the use of crucial narrative reports in translational research.
- There is a need for data warehousing solutions that can effectively integrate and analyze unstructured clinical text data.
Purpose of the Study:
- To introduce Dr. Warehouse, an open-source data warehouse specifically designed for clinical narrative reports.
- To support clinicians' daily use and advance translational research through enhanced text exploration and cohort building.
Main Methods:
- Dr. Warehouse utilizes an original database model focusing on both documents and facts.
- It incorporates an advanced search engine and graphical user interfaces for text exploration.
- Natural Language Processing (NLP) techniques, including UMLS concept extraction, negation, and family history detection, are employed.
Main Results:
- A survey at Necker Children's Hospital showed 96.6% user satisfaction after 6 months.
- Users performed 2837 queries, accessed 4,267 patient records, and included 36,632 patients in 131 cohorts.
- The system demonstrates capabilities for cohort recruitment, high throughput phenotyping, and patient-centric views.
Conclusions:
- Dr. Warehouse is a valuable tool for translational research, effectively handling clinical narrative data.
- Its user-friendly interface and advanced NLP features contribute to high user satisfaction and efficient data utilization.
- The open-source availability facilitates broader adoption and further development in clinical informatics.
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