ArticleEGEMS (Washington, DC)2013
Preparing Electronic Clinical Data for Quality Improvement and Comparative Effectiveness Research: The SCOAP CERTAIN Automation and Validation Project.
Article in EGEMS (Washington, DC), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Challenges for Data Quality in the Clinical Data Life Cycle: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review.Journal of biomedical informatics · 2017Pooled it
- Colorectal Cancer Test Completion among Adults Aged 45 to 75 in an Integrated Healthcare System: A Retrospective Analysis.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- An analysis of multilevel factor contributions to breast cancer screening inequities in an academic health system.Cancer · 2025Observational
- Implementing an Organized Colorectal Cancer Screening Program: Lessons Learned From an Academic-Community Practice.AJPM focus · 2024Article
- Using A Standardized Nomenclature to Semantically Map Oncology-Related Concepts from Common Data Models to a Pediatric Cancer Data Model.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Effectiveness of a mailed fecal immunochemical test outreach: a Medicare Advantage pilot study.Therapeutic advances in gastroenterology · 2020Article
- Surgical Care and Outcomes Assessment Program (SCOAP): A Nuanced, Flexible Platform for Colorectal Surgical Research.Clinics in colon and rectal surgery · 2019Review
- Variability in invasive mediastinal staging for lung cancer: A multicenter regional study.The Journal of thoracic and cardiovascular surgery · 2018Article
- Automating Electronic Clinical Data Capture for Quality Improvement and Research: The CERTAIN Validation Project of Real World Evidence.EGEMS (Washington, DC) · 2018Article
- The Pediatric Emergency Care Applied Research Network Registry: A Multicenter Electronic Health Record Registry of Pediatric Emergency Care.Applied clinical informatics · 2018Article
- Surgeon attitudes and practice patterns in managing small bowel obstruction: a qualitative analysis.The Journal of surgical research · 2017Article
- Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading.BMC medical informatics and decision making · 2017Article
- Pragmatic (trial) informatics: a perspective from the NIH Health Care Systems Research Collaboratory.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.Journal of biomedical informatics · 2017Article
- Identification of Long Bone Fractures in Radiology Reports Using Natural Language Processing to support Healthcare Quality Improvement.Applied clinical informatics · 2016Article
- Using a Data Quality Framework to Clean Data Extracted from the Electronic Health Record: A Case Study.EGEMS (Washington, DC) · 2016Article
- Automating data abstraction in a quality improvement platform for surgical and interventional procedures.EGEMS (Washington, DC) · 2014Article
- Achieving and sustaining automated health data linkages for learning systems: barriers and solutions.EGEMS (Washington, DC) · 2014Article
- eGEMs' Early Adventures in Open Access Publishing.EGEMS (Washington, DC) · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe field of clinical research informatics includes creation of clinical data repositories (CDRs) used to conduct quality improvement (QI) activities and comparative effectiveness research (CER). Ideally, CDR data are accurately and directly abstracted from disparate electronic health records (EHRs), across diverse health-systems.
objectiveInvestigators from Washington State's Surgical Care Outcomes and Assessment Program (SCOAP) Comparative Effectiveness Research Translation Network (CERTAIN) are creating such a CDR. This manuscript describes the automation and validation methods used to create this digital infrastructure.
methodsSCOAP is a QI benchmarking initiative. Data are manually abstracted from EHRs and entered into a data management system. CERTAIN investigators are now deploying Caradigm's Amalga™ tool to facilitate automated abstraction of data from multiple, disparate EHRs. Concordance is calculated to compare data automatically to manually abstracted. Performance measures are calculated between Amalga and each parent EHR. Validation takes place in repeated loops, with improvements made over time. When automated abstraction reaches the current benchmark for abstraction accuracy - 95% - itwill 'go-live' at each site. PROGRESS TO DATE: A technical analysis was completed at 14 sites. Five sites are contributing; the remaining sites prioritized meeting Meaningful Use criteria. Participating sites are contributing 15-18 unique data feeds, totaling 13 surgical registry use cases. Common feeds are registration, laboratory, transcription/dictation, radiology, and medications. Approximately 50% of 1,320 designated data elements are being automatically abstracted-25% from structured data; 25% from text mining.
conclusionIn semi-automating data abstraction and conducting a rigorous validation, CERTAIN investigators will semi-automate data collection to conduct QI and CER, while advancing the Learning Healthcare System.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.