To address these shortcomings, sites have historically worked with sponsors on point-to-point digital solutions that hasten dataset transfers 3. This process consumes valuable site and sponsor time and effort (T/E) and can introduce errors into the dataset from manual transcription processes 1, 2. Traditional industry-sponsored clinical trial manual data entry into Electronic Data Capture (EDC) systems from the site’s Electronic Health Record (EHR) is inefficient. Journal of the Society for Clinical Data Management. (2021) “Direct Data Extraction and Exchange of Local Labs for Clinical Research Protocols: A Partnership with Sites, Biopharmaceutical Firms, and Clinical Research Organizations”, It allows for the more efficient use of both sponsor, CRO, and site staff time and effort. A similar productivity analysis between NVS and NCCE showed a 99% reduction in traditional data review activities by NVS, and a 96% reduction in queries to the site.ĬONCLUSION: DDE increased the productivity of an existing clinical trial data transfer process by decreasing data latency, transcription errors, and queries. The NVS-MSK local lab DDE productivity analysis found that 20–24% of manually entered data were removed, and queries were reduced by approximately 50%. per patient, per study site queries by 2.5 queries per patient, per visit and monitoring activity by 3 hrs. RESULTS: Compared to manual transcription, the Lilly-MSK-Yale DDE PoC decreased: data latency from 20.4 to 3.5 days transcription errors from 6.7% to 0% site effort by 8 hrs. Novartis tracked similar efficiency gains when implementing DDE with MSK in 2012, and National Cancer Center Hospital East (NCCE) in 2014. Data entry timestamps were captured and analyzed for: 1) data latency, 2) transcription errors, 3) query rate, and 4) time and effort savings. Sites continued to manually transcribe data into Lilly’s EDC in parallel. METHODS: A DDE Proof of Concept (PoC) at Memorial Sloan Kettering Cancer Center (MSK) and Yale University compared manual EDC transcription and DDE. OBJECTIVES: The primary objective of this study was to show the efficiency gains and return on investment for implementing DDE compared to traditional manual data entry methods. Herein, we describe Direct Data Extraction (DDE) best practices identified by the Society for Clinical Data Management eSource Consortium that will enable other groups to implement DDE for their own clinical research efforts. INTRODUCTION: Manual transcription of site clinical trial data into sponsor Electronic Data Capture (EDC) systems is labor intensive and error prone. Original Research Direct Data Extraction and Exchange of Local Labs for Clinical Research Protocols: A Partnership with Sites, Biopharmaceutical Firms, and Clinical Research OrganizationsĪuthors: Michael Buckley (Memorial Sloan Kettering Cancer Center), Aruna Vattikola (Novartis Pharmaceuticals Corporation), Rakesh Maniar (Merck & Co., Inc., NJ), Hugh Dai (Eli Lilly and Company) Abstract |Journal of the Society for Clinical Data Management ![]() Vattikola | Direct Data Extraction and Exchange of Local Labs for Clinical Research Protocols: A Partnership with Sites, Biopharmaceutical Firms, and Clinical Research Organizations
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |