@article{DietrichKrebsLimanetal.2019,
  author    = {Dietrich, Georg and Krebs, Jonathan and Liman, Leon and Fette, Georg and Ertl, Maximilian and Kaspar, Mathias and St{\"o}rk, Stefan and Puppe, Frank},
  title     = {Replicating medication trend studies using ad hoc information extraction in a clinical data warehouse},
  series = {BMC Medical Informatics and Decision Making},
  volume    = {19},
  journal   = {BMC Medical Informatics and Decision Making},
  doi       = {10.1186/s12911-018-0729-0},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200409},
  pages     = {15},
  year      = {2019},
  abstract  = {Background Medication trend studies show the changes of medication over the years and may be replicated using a clinical Data Warehouse (CDW). Even nowadays, a lot of the patient information, like medication data, in the EHR is stored in the format of free text. As the conventional approach of information extraction (IE) demands a high developmental effort, we used ad hoc IE instead. This technique queries information and extracts it on the fly from texts contained in the CDW. Methods We present a generalizable approach of ad hoc IE for pharmacotherapy (medications and their daily dosage) presented in hospital discharge letters. We added import and query features to the CDW system, like error tolerant queries to deal with misspellings and proximity search for the extraction of the daily dosage. During the data integration process in the CDW, negated, historical and non-patient context data are filtered. For the replication studies, we used a drug list grouped by ATC (Anatomical Therapeutic Chemical Classification System) codes as input for queries to the CDW. Results We achieve an F1 score of 0.983 (precision 0.997, recall 0.970) for extracting medication from discharge letters and an F1 score of 0.974 (precision 0.977, recall 0.972) for extracting the dosage. We replicated three published medical trend studies for hypertension, atrial fibrillation and chronic kidney disease. Overall, 93\% of the main findings could be replicated, 68\% of sub-findings, and 75\% of all findings. One study could be completely replicated with all main and sub-findings. Conclusion A novel approach for ad hoc IE is presented. It is very suitable for basic medical texts like discharge letters and finding reports. Ad hoc IE is by definition more limited than conventional IE and does not claim to replace it, but it substantially exceeds the search capabilities of many CDWs and it is convenient to conduct replication studies fast and with high quality.},
  language  = {en}
}
@article{LimanMayFetteetal.2023,
  author    = {Liman, Leon and May, Bernd and Fette, Georg and Krebs, Jonathan and Puppe, Frank},
  title     = {Using a clinical data warehouse to calculate and present key metrics for the radiology department: implementation and performance evaluation},
  series = {JMIR Medical Informatics},
  volume    = {11},
  journal   = {JMIR Medical Informatics},
  issn      = {2291-9694},
  doi       = {10.2196/41808},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349411},
  year      = {2023},
  abstract  = {Background: Due to the importance of radiologic examinations, such as X-rays or computed tomography scans, for many clinical diagnoses, the optimal use of the radiology department is 1 of the primary goals of many hospitals. Objective: This study aims to calculate the key metrics of this use by creating a radiology data warehouse solution, where data from radiology information systems (RISs) can be imported and then queried using a query language as well as a graphical user interface (GUI). Methods: Using a simple configuration file, the developed system allowed for the processing of radiology data exported from any kind of RIS into a Microsoft Excel, comma-separated value (CSV), or JavaScript Object Notation (JSON) file. These data were then imported into a clinical data warehouse. Additional values based on the radiology data were calculated during this import process by implementing 1 of several provided interfaces. Afterward, the query language and GUI of the data warehouse were used to configure and calculate reports on these data. For the most common types of requested reports, a web interface was created to view their numbers as graphics. Results: The tool was successfully tested with the data of 4 different German hospitals from 2018 to 2021, with a total of 1,436,111 examinations. The user feedback was good, since all their queries could be answered if the available data were sufficient. The initial processing of the radiology data for using them with the clinical data warehouse took (depending on the amount of data provided by each hospital) between 7 minutes and 1 hour 11 minutes. Calculating 3 reports of different complexities on the data of each hospital was possible in 1-3 seconds for reports with up to 200 individual calculations and in up to 1.5 minutes for reports with up to 8200 individual calculations. Conclusions: A system was developed with the main advantage of being generic concerning the export of different RISs as well as concerning the configuration of queries for various reports. The queries could be configured easily using the GUI of the data warehouse, and their results could be exported into the standard formats Excel and CSV for further processing.},
  language  = {en}
}