Use of EHRs data for clinical research: Historical progress and current applications.

The benefits of reusing EHR data for clinical research studies are numerous. They portend the opportunity to bring new therapies to patients sooner, potentially at a lower cost, and to accelerate learning health cycles-through faster data acquisition in clinical research studies. Metrics have proven that time can be saved, workflow and processes streamlined, and data quality increased significantly. Pilot projects and now actual investigational trials used for regulatory submissions have shown that these benefits support the transformation of clinical research by leveraging EHRs for research. Panelists at a recent collaborative focused on bridging clinical research and clinical care offered varying perspectives on how the latest standards and technologies could be leveraged to facilitate data transfer from EHR systems into clinical research databases, as well as the associated improvements in data quality. Panelists also discussed other avenues to leverage EHR in clinical research. Improvements and exciting possibilities notwithstanding, much work remains. Data ownership and access, attention to metadata and structured data for data sharing, and broader adoption of global standards are key areas for collaboration. With the steady increase in adoption of EHRs around the world, this is an excellent time for all stakeholders to work together and create an environment such that EHRs can be used more readily for research. The capacity for research can thus be increased to provide more high-quality information that will contribute to rapid continuous learning health systems from which all patients can benefit.

A comparative effectiveness study of eSource used for data capture for a clinical research registry.

OBJECTIVE: This pilot study compared eSource-enabled versus traditional manual data transcription (non-eSource methods) for the collection of clinical registry information. The primary study objective was to compare the time spent completing registry forms using eSource versus non-eSource methods The secondary objectives were to compare data quality associated with these two data capture methods and the flexibility of the workflows. This study directly addressed fundamental questions relating to eSource adoption: what time-savings can be realized, and to what extent does eSource improve data quality. MATERIALS AND METHODS: The study used time and motion methods to compare eSource versus non-eSource data capture workflows for a single center OB/GYN registry. Direct observation by industrial engineers using specialized computer software captured keystrokes, mouse clicks and video recordings of the study team in their normal work environment completing real-time data collection. RESULTS: The overall average data capture time was reduced with eSource versus non-eSource methods (difference, 151s per case; eSource, 1603s; non-eSource, 1754s; p=0.051). The average data capture time for the demographic data was reduced (difference, 79s per case; eSource, 133s; non-eSource, 213s; p<0.001). This represents a 37% time reduction (95% confidence interval 27% to 47%). eSourced data field transcription errors were also reduced (eSource, 0%; non-eSource, 9%). CONCLUSION: The use of eSource versus traditional data transcription was associated with a significant reduction in data entry time and data quality errors. Further studies in other settings are needed to validate these results.

Using Medical Informatics to Improve Clinical Trial Operations.

The continued escalation of clinical trial costs is becoming a public health concern. During the past decade, medical research funding peaked and there is growing concern that there may be insufficient resources to test many promising medical products. Recent changes in the regulatory environment create opportunities for the use of medical informatics to improve clinical trial operations and reduce costs. We report on a Medical Informatics Europe 2016 workshop conducted during the Health - Exploring Complexity (HEC) 2016 conference. We review presentation given on Secondary Data Use, eSource, and Data Quality in Clinical Trials and report on the workshop's discussions.