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Kortüm, Karsten U.; Müller, Michael; Kern, Christoph; Babenko, Alexander; Mayer, Wolfgang J.; Kampik, Anselm; Kreutzer, Thomas C.; Priglinger, Siegfried and Hirneiss, Christoph (2017): Using Electronic Health Records to Build an Ophthalmologic Data Warehouse and Visualize Patients' Data. In: American Journal of Ophthalmology, Vol. 178: pp. 84-93

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PURPOSE: To develop a near-real-time data warehouse (DW) in an academic ophthalmologic center to gain scientific use of increasing digital data from electronic medical records (EMR) and diagnostic devices. DESIGN: Database development. METHODS: Specific macular clinic user interfaces within the institutional hospital information system were created. Orders for imaging modalities were sent by an EMR-linked picture-archiving and communications system to the respective devices. All data of 325 767 patients since 2002 were gathered in a DW running on an SQL database. A data discovery tool was developed. An exemplary search for patients with age-related macular degeneration, performed cataract surgery, and at least 10 intravitreal (excluding bevacizumab) injections was conducted. RESULTS: Data related to those patients (3 142 204 diagnoses [including diagnoses from other fields of medicine], 720 721 procedures [eg, surgery], and 45 416 intravitreal injections) were stored, including 81 274 optical coherence tomography measurements. A web-based browsing tool was successfully developed for data visualization and filtering data by several linked criteria, for example, minimum number of intravitreal injections of a specific drug and visual acuity interval. The exemplary search identified 450 patients with 516 eyes meeting all criteria. CONCLUSIONS: A DW was successfully implemented in an ophthalmologic academic environment to support and facilitate research by using increasing EMR and measurement data. The identification of eligible patients for studies was simplified. In future, software for decision support can be developed based on the DW and its structured data. The improved classification of diseases and semiautomatic validation of data via machine learning are warranted.

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