BACKGROUND: Data that needs to be documented for clinical studies has often been acquired and documented in clinical routine. Usually this data is manually transferred to Case Report Forms (CRF) and/or directly into an electronic data capture (EDC) system. OBJECTIVES: To enhance the documentation process of a large clinical follow-up study targeting patients admitted for acutely decompensated heart failure by accessing the data created during routine and study visits from a hospital information system (HIS) and by transferring it via a data warehouse (DWH) into the study's EDC system. METHODS: This project is based on the clinical DWH developed at the University of Würzburg. The DWH was extended by several new data domains including data created by the study team itself. An R user interface was developed for the DWH that allows to access its source data in all its detail, to transform data as comprehensively as possible by R into study-specific variables and to support the creation of data and catalog tables. RESULTS: A data flow was established that starts with labeling patients as study patients within the HIS and proceeds with updating the DWH with this label and further data domains at a daily rate. Several study-specific variables were defined using the implemented R user interface of the DWH. This system was then used to export these variables as data tables ready for import into our EDC system. The data tables were then used to initialize the first 296 patients within the EDC system by pseudonym, visit and data values. Afterwards, these records were filled with clinical data on heart failure, vital parameters and time spent on selected wards. CONCLUSIONS: This solution focuses on the comprehensive access and transformation of data for a DWH-EDC system linkage. Using this system in a large clinical study has demonstrated the feasibility of this approach for a study with a complex visit schedule.
BACKGROUND: Data that needs to be documented for clinical studies has often been acquired and documented in clinical routine. Usually this data is manually transferred to Case Report Forms (CRF) and/or directly into an electronic data capture (EDC) system. OBJECTIVES: To enhance the documentation process of a large clinical follow-up study targeting patients admitted for acutely decompensated heart failure by accessing the data created during routine and study visits from a hospital information system (HIS) and by transferring it via a data warehouse (DWH) into the study's EDC system. METHODS: This project is based on the clinical DWH developed at the University of Würzburg. The DWH was extended by several new data domains including data created by the study team itself. An R user interface was developed for the DWH that allows to access its source data in all its detail, to transform data as comprehensively as possible by R into study-specific variables and to support the creation of data and catalog tables. RESULTS: A data flow was established that starts with labeling patients as study patients within the HIS and proceeds with updating the DWH with this label and further data domains at a daily rate. Several study-specific variables were defined using the implemented R user interface of the DWH. This system was then used to export these variables as data tables ready for import into our EDC system. The data tables were then used to initialize the first 296 patients within the EDC system by pseudonym, visit and data values. Afterwards, these records were filled with clinical data on heart failure, vital parameters and time spent on selected wards. CONCLUSIONS: This solution focuses on the comprehensive access and transformation of data for a DWH-EDC system linkage. Using this system in a large clinical study has demonstrated the feasibility of this approach for a study with a complex visit schedule.
Entities:
Keywords:
Data warehouse; clinical study; electronic data capture; secondary data use
Authors: Caroline Morbach; Martin Wagner; Stefan Güntner; Carolin Malsch; Mehmet Oezkur; David Wood; Kornelia Kotseva; Rainer Leyh; Georg Ertl; Wolfgang Karmann; Peter U Heuschmann; Stefan Störk Journal: BMC Cardiovasc Disord Date: 2017-05-05 Impact factor: 2.298
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