Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting
45
Zitationen
9
Autoren
2016
Jahr
Abstract
BACKGROUND: Data capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems. METHODS: Case report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project. RESULTS: The analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records. CONCLUSIONS: Common data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.
Ähnliche Arbeiten
Machine Learning in Medicine
2019 · 3.794 Zit.
Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care
2006 · 3.174 Zit.
Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes
2005 · 2.971 Zit.
Studies in health technology and informatics
2008 · 2.903 Zit.
An overview of clinical decision support systems: benefits, risks, and strategies for success
2020 · 2.728 Zit.
Autoren
Institutionen
- University of Münster(DE)
- University of Dundee(GB)
- Assistance Publique – Hôpitaux de Paris(FR)
- Hôpital Européen Georges-Pompidou(FR)
- Hôpital Européen(FR)
- Département d'Informatique(FR)
- University College London(GB)
- Bayer (Germany)(DE)
- Novartis (Switzerland)(CH)
- Friedrich-Alexander-Universität Erlangen-Nürnberg(DE)