BACKGROUND: Clinical Trials (CTs) are indispensable instruments for evidence-based medicine and frequently necessitate the management, sharing and analysis of large amounts of data amongst partners in different locations. METHODS: To effectively satisfy these requirements, the proposed solution combines a web platform and a Clinical Data Management System (CDMS) to exploit the strengths of Electronic Health Records (EHR) and Electronic Data Capture (EDC) systems. The core of the proposal is a relational database which has high data structuring characteristics and utilises biomedical controlled vocabularies (e.g. LOINC and ICD). In addition, units and normality ranges were collected for data comparison through the application of the Z-score transformation. RESULTS: The obtained CDMS preserves the EDC's flexibility and user autonomy and permits the creation of patient cohorts, as in the EHR. Accordingly, clinical information, after the initial recording, is available for different simultaneous multicentre CTs. Furthermore, interface runtime controls guarantee high data quality during data entering processes. Currently, the proposed system has been developed in the HIV and eye diseases fields in Italy. CONCLUSIONS: The proposed solution is flexible and suitable to perform multicentre research within a varying range of medical domains. In the future, the automatic importation of information from hospitals has been planned through an HL7 standard interface which would improve both data quantity and quality.
BACKGROUND: Clinical Trials (CTs) are indispensable instruments for evidence-based medicine and frequently necessitate the management, sharing and analysis of large amounts of data amongst partners in different locations. METHODS: To effectively satisfy these requirements, the proposed solution combines a web platform and a Clinical Data Management System (CDMS) to exploit the strengths of Electronic Health Records (EHR) and Electronic Data Capture (EDC) systems. The core of the proposal is a relational database which has high data structuring characteristics and utilises biomedical controlled vocabularies (e.g. LOINC and ICD). In addition, units and normality ranges were collected for data comparison through the application of the Z-score transformation. RESULTS: The obtained CDMS preserves the EDC's flexibility and user autonomy and permits the creation of patient cohorts, as in the EHR. Accordingly, clinical information, after the initial recording, is available for different simultaneous multicentre CTs. Furthermore, interface runtime controls guarantee high data quality during data entering processes. Currently, the proposed system has been developed in the HIV and eye diseases fields in Italy. CONCLUSIONS: The proposed solution is flexible and suitable to perform multicentre research within a varying range of medical domains. In the future, the automatic importation of information from hospitals has been planned through an HL7 standard interface which would improve both data quantity and quality.
Authors: Monica Bonetto; Massimo Nicolò; Roberta Gazzarata; Paolo Fraccaro; Raffaella Rosa; Donatella Musetti; Maria Musolino; Carlo E Traverso; Mauro Giacomini Journal: IEEE J Transl Eng Health Med Date: 2015-12-28 Impact factor: 3.316
Authors: Paul Wicks; Matthew Hotopf; Vaibhav A Narayan; Ethan Basch; James Weatherall; Muir Gray Journal: BMC Med Date: 2016-11-07 Impact factor: 8.775
Authors: Giovanni Cenderello; Caterina Fanizza; Simona Marenco; Laura Ambra Nicolini; Stefania Artioli; Isabella Baldissarro; Chiara Dentone; Pasqualina De Leo; Antonio Di Biagio Journal: Clinicoecon Outcomes Res Date: 2017-05-22
Authors: Paolo Fraccaro; Valeria Pupella; Roberta Gazzarata; Chiara Dentone; Giovanni Cenderello; Pasqualina De Leo; Federica Bozzano; Giorgetta Casalino Finocchio; Andrea De Maria; Daniela Fenoglio; Gilberto Filaci; Michele Guerra; Antonio Di Biagio; Eugenio Mantia; Giancarlo Orofino; Giuseppe Ferrea; Claudio Viscoli; Mauro Giacomini Journal: Med 2 0 Date: 2013-08-13