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Feasibility of Automated Laboratory Data Ascertainment and Transfer From Hospitals Into Medidata Rave Across
Tamara P Miller1,2, Richard Aplenc3, Charles Minard4
1Emory University School of Medicine, Atlanta, GA.
Purpose:
Electronic data capture (EDC) has the potential to improve trial data accuracy and efficiency. The Children's Oncology Group Pediatric Early Phase Clinical Trials Network (PEP-CTN) and Pediatric Brain Tumor Consortium (PBTC) conducted a joint pilot that aimed to assess feasibility of automated transfer of laboratory data from electronic health records to the Medidata Rave EDC (Rave). Second, we compared data from automated extraction (EXTRACTED) and manual ascertainment (MANUAL) during the original studies using two representative tests, bilirubin and platelet count (PLT).
Methods:
Data from 78 patients treated on seven completed PBTC or PEP-CTN trials at seven hospitals were included. Each consortium created a Rave instance to receive data. Sites elected to use local methods or the ExtractEHR R package. Postextraction data were uploaded to the Rave Batch Uploader (BU)-associated file transfer protocol client and BU uploaded data into Rave. After successful upload, discrepancies between EXTRACTED and MANUAL data were categorized.
Results:
All sites successfully extracted required laboratory data and had 1+ complete upload. PBTC/PEP-CTN operational effort and close collaboration with hospital site technical teams was required. Owing to study closure timing, only two sites checked EXTRACTED data from their first patient list; this identified incomplete mapping that was fixed before second patient list extraction. Beyond incomplete mapping, discrepancies for bilirubin and PLT were commonly due to mismatched values or units (Consortium 1 PLT: 3/10 (30%); Consortium 2 PLT: 152/176 (86%)).
Conclusion:
This study demonstrated that automated laboratory data ascertainment is feasible in pediatric cooperative oncology group trials. Although this required up-front central operational and local site-based effort, lessons learned from this pilot can inform future studies using automated data capture.
