Querying a Clinical Data Warehouse for Combinations of Clinical and Imaging Data.

This study aims to show the feasibility and benefit of single queries in a research data warehouse combining data from a hospital's clinical and imaging systems. We used a comprehensive integration of a production picture archiving and communication system (PACS) with a clinical data warehouse (CDW) for research to create a system that allows data from both domains to be queried jointly with a single query. To achieve this, we mapped the DICOM information model to the extended entity-attribute-value (EAV) data model of a CDW, which allows data linkage and query constraints on multiple levels: the patient, the encounter, a document, and a group level. Accordingly, we have integrated DICOM metadata directly into CDW and linked it to existing clinical data. We included data collected in 2016 and 2017 from the Department of Internal Medicine in this analysis for two query inquiries from researchers targeting research about a disease and in radiology. We obtained quantitative information about the current availability of combinations of clinical and imaging data using a single multilevel query compiled for each query inquiry. We compared these multilevel query results to results that linked data at a single level, resulting in a quantitative representation of results that was up to 112% and 573% higher. An EAV data model can be extended to store data from clinical systems and PACS on multiple levels to enable combined querying with a single query to quickly display actual frequency data.

Unlocking the PACS DICOM Domain for its Use in Clinical Research Data Warehouses.

Clinical Data Warehouses (DWHs) are used to provide researchers with simplified access to pseudonymized and homogenized clinical routine data from multiple primary systems. Experience with the integration of imaging and metadata from picture archiving and communication systems (PACS), however, is rare. Our goal was therefore to analyze the viability of integrating a production PACS with a research DWH to enable DWH queries combining clinical and medical imaging metadata and to enable the DWH to display and download images ad hoc. We developed an application interface that enables to query the production PACS of a large hospital from a clinical research DWH containing pseudonymized data. We evaluated the performance of bulk extracting metadata from the PACS to the DWH and the performance of retrieving images ad hoc from the PACS for display and download within the DWH. We integrated the system into the query interface of our DWH and used it successfully in four use cases. The bulk extraction of imaging metadata required a median (quartiles) time of 0.09 (0.03-2.25) to 12.52 (4.11-37.30) seconds for a median (quartiles) number of 10 (3-29) to 103 (8-693) images per patient, depending on the extraction approach. The ad hoc image retrieval from the PACS required a median (quartiles) of 2.57 (2.57-2.79) seconds per image for the download, but 5.55 (4.91-6.06) seconds to display the first and 40.77 (38.60-41.63) seconds to display all images using the pure web-based viewer. A full integration of a production PACS with a research DWH is viable and enables various use cases in research. While the extraction of basic metadata from all images can be done with reasonable effort, the extraction of all metadata seems to be more appropriate for subgroups.

Replicating medication trend studies using ad hoc information extraction in a clinical data warehouse.

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.

Using a Clinical Data Warehouse to Calculate and Present Key Metrics for the Radiology Department: Implementation and Performance Evaluation.

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.

Calculating Key Figures for Radiology Departments Using a Clinical Data Warehouse - A Technical Case Report.

Optimizing the utilization of radiology departments is one of the primary objectives for many hospitals. To support this, a solution has been developed, which at first transforms the export of different Radiological Information Systems (RIS) into the data format of a clinical data warehouse (CDW). Additional features, like for example the time between the creation of a radiologic request and the finalization of the diagnosis for the created images, can then be defined using a simple interface and are calculated and saved in the CDW as well. Finally, the query language of the CDW can be used to create custom reports with all the RIS data including the calculated features and export them into the standard formats Excel and CSV. The solution has been successfully tested with data from two German hospitals.

Implementation of a HL7-CQL Engine Using the Graph Database Neo4J.

The Clinical Quality Language (CQL) is a useful tool for defining search requests for data stores containing FHIR data. Unfortunately, there are only few execution engines that are able to evaluate CQL queries. As FHIR data represents a graph structure, the authors pursue the approach of storing all data contained in a FHIR server in the graph database Neo4J and to translate CQL queries into Neo4J's query language Cypher. The query results returned by the graph database are retranslated into their FHIR representation and returned to the querying user. The approach has been positively tested on publicly available FHIR servers with a handcrafted set of example CQL queries.

Query Translation Between openEHR and i2b2.

Secondary use of electronic health records using data warehouses (DW) has become an attractive approach to support clinical research. In order to increase the volume of underlying patient data DWs at different institutions can be connected to research networks. Two obstacles to connect a DW to such a network are the syntactical differences between the involved DW technologies and differences in the data models of the connected DWs. The current work presents an approach to tackle both problems by translating queries from the DW system openEHR into queries from the DW system i2b2 and vice versa. For the subset of queries expressible in the query languages of both systems, the presented approach is well feasible.

Query Translation Between AQL and CQL.

Secondary use of electronic health records using data aggregation systems (DAS) with standardized access interfaces (e.g. openEHR, i2b2, FHIR) have become an attractive approach to support clinical research. In order to increase the volume of underlying patient data, multiple DASs at different institutions can be connected to research networks. Two obstacles to connect a DAS to such a network are the syntactical differences between the involved DAS query interfaces and differences in the data models the DASs operate on. The current work presents an approach to tackle both problems by translating queries from a DAS using openEHR's query language AQL (Archetype Query Language) into queries using the query language CQL (Clinical Quality Language) and vice versa. For the subset of queries which are expressible in both query languages the presented approach is well feasible.

Information Extraction from Echocardiography Reports for a Clinical Follow-up Study-Comparison of Extracted Variables Intended for General Use in a Data Warehouse with Those Intended Specifically for the Study.

BACKGROUND: The interest in information extraction from clinical reports for secondary data use is increasing. But experience with the productive use of information extraction processes over time is scarce. A clinical data warehouse has been in use at our university hospital for several years, which also provides an information extraction of echocardiography reports developed for general use. OBJECTIVES: This study aims to illustrate the difficulties encountered, while using data from a preexisting information extraction process for a large clinical study. To compare the data from the preexisting process with the data obtained from a specially developed process designed to improve the quality and completeness of the study data. METHODS: We extracted the echocardiography variables for 440 patients from the general-use information extraction of the data warehouse (678 reports). Then we developed an information extraction process for the same variables but specifically for this study, with the aim to extract as much information as possible from the text. The extracted data of both processes were compared with a newly created gold standard defined by a cardiologist with long-standing experience in heart failure. RESULTS: Among 57 echocardiography variables considered relevant for the study, 50 were documented in the routine text reports and could be extracted. Twenty of the required variables were not provided by the general-use extraction process, some others were not provided correctly. The median macro F1-score (precision, recall) across the 30 variables for which values were extracted was 0.81 (0.94, 0.77). Across all 50 variables, as relevant for the study, median macro F1-score was only 0.49 (0.56, 0.46). Employing the study-specific approach considerably improved the quality and completeness of the variables, resulting in F1-scores of 0.97 (0.98, 0.96) across all variables. CONCLUSION: Data from information extractions can be used for large clinical studies. However, preexisting information extraction processes should be treated with caution, as the time and effort spent defining each variable in the information extraction process may not be clear.

Estimating a Bias in ICD Encodings for Billing Purposes.

ICD encoded diagnoses are a popular criterion for eligibility algorithms for study cohort recruitment. However, "official" ICD encoded diagnoses used for billing purposes are afflicted with a bias originating from legal issues. This work presents an approach to estimate the degree of the encoding bias for the complete ICD catalogue at a German university hospital. The free text diagnoses sections of discharge letters are automatically classified using a supervised machine learning algorithm. The automatic classifications are compared with the official, manually classified codes. For selected ICD codes the approach works sufficiently well.

Exporting Data from a Clinical Data Warehouse.

Data Warehouses (DW) are useful tools to support clinical studies as they can provide exports of routine care data for scientific reuse. Exported DW data is usually post-processed and integrated into study databases by study staff that is reasonably trained in specific tools like SPSS and Excel but which are no programmers or computer scientists. DW systems should therefore be configurable to satisfy export format desiderata as much as possible so that exports contain no unnecessary post-processing obstacles. In the presented work the authors analyze various existing DW systems in respect to a list of potential export formats.

Finding Needles in the Haystack: Identifying Patients with Rare Subtype of Multiple Myeloma Supported by a Data Warehouse and Information Extraction.

Finding patient cases with extremely rare pathologies is a laborious task. To decrease time spent on manually searching through thousands of discharge letters and reports, a data warehouse with a fast fulltext search index was queried. Our use case is to find "macrofocal myeloma", i.e. Multiple Myeloma patients with few large lesions. We guessed the number of those patients in the University Hospital Würzburg at about 20. Most criteria were available in the data warehouse in an unstructured form requiring information extraction. 8 patient cases were found by searching for different spellings of "macrofocal myeloma" in discharge letters directly. With an indirect search combining several criteria, we found additional 23 candidate patient cases, from which 10 were classified by a domain expert as correct. The most difficult criteria were determining the degree of bone marrow infiltration. We achieved an F1 score of 93.2 % for this task. The number of patient cases to be screened manually for this disease decreased from about 25000 to 23.