Automating Electronic Clinical Data Capture for Quality Improvement and Research: The CERTAIN Validation Project of Real World Evidence. Issue 1 (22nd May 2018)

• Record Type: Journal Article
• Authors: Devine, Emily Beth; Van Eaton, Erik; Zadworny, Megan E.; Symons, Rebecca; Devlin, Allison; Yanez, David; Yetisgen, Meliha; Keyloun, Katelyn R.; Capurro, Daniel; Alfonso-Cristancho, Rafael; Flum, David R.; Tarczy-Hornoch, Peter
• Is Part Of: EGEMS. Volume 6:Issue 1(2018)
• Publication Date: 2018-05-22
• Subjects: electronic health records -- validation studies -- comparative effectiveness research -- quality improvement; Medical records -- Data processing -- Periodicals; Medical care -- Data processing -- Periodicals; Medical Records; Automatic Data Processing; Medical care -- Data processing; Medical records -- Data processing; Periodicals; Periodicals; 651.504261
• DOI: 10.5334/egems.211
• Languages: English
• ISSNs: 2327-9214

Abstract

Background: The availability of high fidelity electronic health record (EHR) data is a hallmark of the learning health care system. Washington State's Surgical Care Outcomes and Assessment Program (SCOAP) is a network of hospitals participating in quality improvement (QI) registries wherein data are manually abstracted from EHRs. To create the Comparative Effectiveness Research and Translation Network (CERTAIN), we semi-automated SCOAP data abstraction using a centralized federated data model, created a central data repository (CDR), and assessed whether these data could be used as real world evidence for QI and research. Objectives: Describe the validation processes and complexities involved and lessons learned. Methods: Investigators installed a commercial CDR to retrieve and store data from disparate EHRs. Manual and automated abstraction systems were conducted in parallel (10/2012-7/2013) and validated in three phases using the EHR as the gold standard: 1) ingestion, 2) standardization, and 3) concordance of automated versus manually abstracted cases. Information retrieval statistics were calculated. Results: Four unaffiliated health systems provided data. Between 6 and 15 percent of data elements were abstracted: 51 to 86 percent from structured data; the remainder using natural language processing (NLP). In phase 1, data ingestion from 12 out of 20 feeds reached 95 percent accuracy. In phase 2, 55 percent of structured data elements performed with 96 to 100 percent accuracy; NLP with 89 to 91 percent accuracy. In phase 3, concordance ranged from 69 to 89 percent. Information retrieval statistics were consistently above 90 percent. Conclusions: Semi-automated data abstraction may be useful, although raw data collected as a byproduct of health care delivery is not immediately available for use as real world evidence. New approaches to gathering and analyzing extant data are required.