Othr-10. Pilot Study to Evaluate Automated Laboratory Data Transfer Into Medidata Rave Across the National Cancer Institute (Nci)-Supported Pediatric Brain Tumor Consortium (Pbtc) and Pediatric Early Phase Clinical Trial Network (Pep-Ctn) Cooperative Groups

BACKGROUND Electronic data capture (EDC) has the potential to improve data accuracy and efficiency in oncology. The PBTC and PEP-CTN cooperative groups conducted a pilot project to 1) assess the feasibility of automated transfer of laboratory data from participating institutions’ electronic health records (EHR) to the Medidata Rave EDC housed within each consortium’s Uniform Resource Locator (URL); 2) evaluate discrepancies between retrospective automated laboratory data extraction from the EHR and manual data ascertainment during the original conduct of the study (MANUAL). METHODS Data from patients treated on 7 completed PBTC or PEP-CTN trials at 7 hospitals were included. Each consortium created a Rave instance to receive data. Sites elected to use local methods or the ExtractEHR R package to map and extract laboratory data for designated patients. Post-extraction data were uploaded to the Rave Batch Uploader (BU)-associated file transfer protocol (FTP) and BU uploaded data into Rave. After successful upload, discrepancies between EHR and MANUAL data were categorized. Two hospitals reviewed discrepancies at their sites to identify reasons for discrepancies. RESULTS Automated extraction and upload required significant PBTC/PEP-CTN operational effort and close work with hospital technical teams to map and extract data. All sites successfully extracted specified laboratory data and had ≥1 complete upload via BU to Rave; 2 sites had 2 uploads. Discrepancies were most commonly due to more data in EHR datasets (>1 result on a given day, data on additional study window days) or mismatched values or units. Differences in percentages of discrepant results between hospitals were likely due to mapping differences for EHR extraction. CONCLUSIONS This study demonstrated that in the pediatric cooperative oncology group setting automated laboratory data ascertainment can be performed. Accomplishing this required upfront central operational and hospital-based effort to prepare for data upload and transfer. This effort varied by site.