Abstract

INTRODUCTION: Manual transcription of site clinical trial data into sponsor Electronic Data Capture (EDC) systems is labor intensive and error prone. 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. 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. METHODS: A DDE Proof of Concept (PoC) at Memorial Sloan Kettering Cancer Center (MSK) and Yale University compared manual EDC transcription and DDE. Sites continued to manually transcribe data into Lilly’s EDC in parallel. Data entry timestamps were captured and analyzed for: 1) data latency, 2) transcription errors, 3) query rate, and 4) time and effort savings. Novartis tracked similar efficiency gains when implementing DDE with MSK in 2012, and National Cancer Center Hospital East (NCCE) in 2014. 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. per patient, per study; site queries by 2.5 queries per patient, per visit; and monitoring activity by 3 hrs. per patient per study. 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%. 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. CONCLUSION: DDE increased the productivity of an existing clinical trial data transfer process by decreasing data latency, transcription errors, and queries. It allows for the more efficient use of both sponsor, CRO, and site staff time and effort.

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