Automated data collection from an electronic medical record for a prospective real-world study in patients with retinal disease (VOYAGER).

Abstract

Background/AimsVOYAGER is a prospective, real-world study of treatment patterns and outcomes in retinal diseases. Data collection often requires double entry of routinely captured clinical data, into both site electronic medical records and VOYAGER electronic Case Report Forms (eCRFs), posing a significant time and resource burden and risk of transcription errors. To overcome these challenges, an electronic medical record-to-electronic data capture solution (EMR-to-EDC) was implemented to automate the direct transfer of electronic medical record data into the VOYAGER electronic data capture. This pilot study aimed to establish whether EMR-to-EDC could reduce data entry burden and improve data accuracy.MethodsEMR-to-EDC automatically retrieved study-specific data variables from patients in the mediSIGHT EMR (Medisoft) to pre-populate corresponding eCRF fields within the VOYAGER electronic data capture. Once pre-population of a visit was completed, site staff reviewed the eCRFs and, if required, edited erroneous fields and manually filled in fields that were not pre-populated. This study analyzed eCRF data from two UK VOYAGER sites, collected from patients for whom data were entered manually and patients for whom data were collected using EMR-to-EDC for ~6 months. Outcomes to assess the impact of EMR-to-EDC on data entry burden and accuracy were proportions of eCRF fields which were pre-populated and manually entered for pre-populated visits, and proportion of pre-populated fields overwritten by site staff. Site staff completed surveys to evaluate end-user satisfaction and acceptance of EMR-to-EDC.ResultsOverall, 49 baseline and 143 follow-up visits were registered, of which 146 (baseline: 39; follow-up: 107) were pre-populated by EMR-to-EDC, encompassing 5,017 baseline and 7,371 follow-up visit eCRF fields in total. Of these, 27.9% baseline and 20.5% follow-up visit fields were pre-populated by EMR-to-EDC. A low proportion of pre-populated baseline (8.1%) and follow-up (1.6%) fields were overwritten by site staff. Mean number of queries generated by the electronic data capture per visit was lower for pre-populated patients versus patients whose data were entered manually (baseline: 17.1 versus 22.0 (p = 0.22); follow-up: 4.1 versus 7.1 (p < 0.05)). Survey results demonstrated that site staff generally agreed that EMR-to-EDC helped reduce study data entry burden and collect high quality data. Most staff estimated that EMR-to-EDC saved 11-20 min and 0-10 min per patient for baseline and follow-up visit data entry, respectively, by the end of the study. Main reported benefits of EMR-to-EDC were time-saving and quality data collection; main challenges were high number of system queries generated and pull-through of study-irrelevant data.ConclusionThese results support EMR-to-EDC as an innovative tool to efficiently transfer large amounts of electronic medical record data into study databases while maintaining data quality, with potential to facilitate research in ophthalmology and other medical fields.