HDVDB: a data warehouse for hepatitis delta virus

[Smart eye data : Development of a foundation for medical research using Smart Data applications]

BACKGROUND

Smart Data means intelligent data accumulation and the evaluation of large data sets. This is particularly important in ophthalmology as more and more data are being created. Increasing knowledge and personalized therapies are expected by combining clinical data from electronic health records (EHR) with measurement data.

OBJECTIVE

In this study we investigated the possibilities to consolidate data from measurement devices and clinical data in a data warehouse (DW).

MATERIAL AND METHODS

An EHR was adjusted to the needs of ophthalmology and the contents of referral letters were extracted. The data were imported into a DW overnight. Measuring devices were connected to the EHR by an HL7 standard interface and the use of a picture archiving and communications system (PACS). Data were exported from the review software using a self-developed software. For data analysis the software was modified to the specific requirements of ophthalmology.

RESULTS

In the EHR 12 graphical user interfaces were created and the data from 32,234 referral letters were extracted. A total of 23 diagnostic devices could be linked to the PACS and 85,114 optical coherence tomography (OCT) scans, 19,098 measurements from IOLMaster as well as 5,425 pentacam examinations were imported into the DW including over 300,000 patients. Data discovery software was modified providing filtering methods.

CONCLUSION

By building a DW a foundation for clinical and epidemiological studies could be implemented. In the future, decision support systems and strategies for personalized therapies can be based on such a database.

Using Electronic Health Records to Build an Ophthalmologic Data Warehouse and Visualize Patients' Data

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.