A Clinician Friendly Data Warehouse Oriented Toward Narrative Reports: Dr. Warehouse
Nicolas Garcelon, Antoine Neuraz, Rémi Salomon, Hassan Faour, Vincent Benoit, Arthur Delapalme, Arnold Munnich, Anita Burgun, Bastien Rance
Source :
Journal of biomedical informatics
2018 Mar 1
Pmid / DOI:
29501921
Abstract
Introduction: Clinical data warehouses are often oriented toward integration and exploration of coded data. However narrative reports are of crucial importance for translational research. This paper describes Dr. Warehouse®, an open source data warehouse oriented toward clinical narrative reports and designed to support clinicians' day-to-day use.
Method: Dr. Warehouse relies on an original database model to focus on documents in addition to facts. Besides classical querying functionalities, the system provides an advanced search engine and Graphical User Interfaces adapted to the exploration of text. Dr. Warehouse is dedicated to translational research with cohort recruitment capabilities, high throughput phenotyping and patient centric views (including similarity metrics among patients). These features leverage Natural Language Processing based on the extraction of UMLS® concepts, as well as negation and family history detection.
Results: A survey conducted after 6 months of use at the Necker Children's Hospital shows a high rate of satisfaction among the users (96.6%). During this period, 122 users performed 2837 queries, accessed 4,267 patients' records and included 36,632 patients in 131 cohorts. The source code is available at this github link https://github.com/imagine-bdd/DRWH.
Method: Dr. Warehouse relies on an original database model to focus on documents in addition to facts. Besides classical querying functionalities, the system provides an advanced search engine and Graphical User Interfaces adapted to the exploration of text. Dr. Warehouse is dedicated to translational research with cohort recruitment capabilities, high throughput phenotyping and patient centric views (including similarity metrics among patients). These features leverage Natural Language Processing based on the extraction of UMLS® concepts, as well as negation and family history detection.
Results: A survey conducted after 6 months of use at the Necker Children's Hospital shows a high rate of satisfaction among the users (96.6%). During this period, 122 users performed 2837 queries, accessed 4,267 patients' records and included 36,632 patients in 131 cohorts. The source code is available at this github link https://github.com/imagine-bdd/DRWH.