+ Site Statistics
References:
54,258,434
Abstracts:
29,560,870
PMIDs:
28,072,757
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Automating Electronic Clinical Data Capture for Quality Improvement and Research: The CERTAIN Validation Project of Real World Evidence



Automating Electronic Clinical Data Capture for Quality Improvement and Research: The CERTAIN Validation Project of Real World Evidence



Egems 6(1): 8



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. Describe the validation processes and complexities involved and lessons learned. 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. 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. 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.

(PDF emailed within 1 workday: $29.90)

Accession: 051719582

Download citation: RISBibTeXText

PMID: 29881766


Related references

Preparing Electronic Clinical Data for Quality Improvement and Comparative Effectiveness Research: The SCOAP CERTAIN Automation and Validation Project. Egems 1(1): 1025, 2013

Quality Improvement Project: Integration of Real-Time Clinical Data Collection and Validation to Standardize Initial Therapy for aGVHD. Biology of Blood and Marrow Transplantation 22(3): S282-S283, 2016

Integrating research data capture into the electronic health record workflow: real-world experience to advance innovation. Perspectives in Health Information Management 11: 1e, 2015

Assessing Real-World Data Quality: The Application of Patient Registry Quality Criteria to Real-World Data and Real-World Evidence. Therapeutic Innovation and Regulatory Science 2019: 2168479019837520, 2019

Electronic data capture. Impact on the quality of the clinical research. Medicina Clinica 122 Suppl 1: 11-15, 2004

A pragmatic method for transforming clinical research data from the research electronic data capture "REDCap" to Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM): Development and evaluation of REDCap2SDTM. Journal of Biomedical Informatics 70: 65-76, 2017

Real-world Evidence versus Randomized Controlled Trial: Clinical Research Based on Electronic Medical Records. Journal of Korean Medical Science 33(34): E213, 2018

Automated data extraction: merging clinical care with real-time cohort-specific research and quality improvement data. Journal of Pediatric Surgery 52(1): 149-152, 2016

The Use of Real-World Evidence and Data in Clinical Research and Postapproval Safety Studies. Therapeutic Innovation and Regulatory Science 52(6): 778-783, 2018

Secondary EMR data for quality improvement and research: A comparison of manual and electronic data collection from an integrated critical care electronic medical record system. Journal of Critical Care 47: 295-301, 2018

From Clinical Trial to Real-World Evidence: A Systematic Approach to Identifying Data Sources for Observational Research. Value in Health 16(7): A583-A584, 2013

Validation of undergraduate clinical data by electronic capture (barcode). Medical Teacher 24(2): 193-196, 2002

Online electronic data capture and research data repository system for clinical and translational research. Missouri Medicine 112(1): 46-52, 2015

Expressing observations from electronic medical record flowsheets in an i2b2 based clinical data repository to support research and quality improvement. AMIA ... Annual Symposium Proceedings. AMIA Symposium 2011: 1454-1463, 2013

Wireless real-time electronic data capture for self-assessment of motor function and quality of life in Parkinson's disease. Movement Disorders 19(4): 446-451, 2004