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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.
Highlights
Learning health care systems seek to deliver appropriate and effective health care by leveraging existing health care data [1, 2]
Our study was a grand experiment in that we used a unique approach to creating a clinical data repositories (CDRs), the use of a centralized federated model
The major limitation of our project was that finite resources prevented us from continuing to improve data quality and establishing a fully automated data abstraction system. This limitation, is the strength of the study, as it pointed up the fact that semi-automated data abstraction may eventually be useful, raw data collected as a byproduct of health care delivery is not immediately ready for use as real world evidence
Summary
Learning health care systems seek to deliver appropriate and effective health care by leveraging existing health care data [1, 2]. These include the Food and Drug Administration (FDA) Sentinel Initiative [4], Observational Health Data Sciences and Informatics (OHDSI) Program [5], and the National Patient-Centered Clinical Research Network (PCORnet) [6] Each of these initiatives employs a distributed federated model wherein each participating site standardizes and normalizes their data to network standards, and forwards those data to the central CDR [7]. These initiatives constitute real world evidence, that is, information on health care derived from multiple sources outside of typical research settings, including information from EHRs, registries, claims data, and wearables. New approaches to gathering and analyzing extant data are required
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