ArticleJournal of biomedical informatics2013
The Analytic Information Warehouse (AIW): a platform for analytics using electronic health record data.
Article in Journal of biomedical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed.
- Clinical Data Warehousing: A Scoping Review.Journal of the Society for Clinical Data Management · 2024Article
- How can technology support quality improvement? Lessons learned from the adoption of an analytics tool for advanced performance measurement in a hospital unit.BMC health services research · 2020Article
- What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions.JMIR formative research · 2020Review
- Deep Learning on Electronic Health Records to Improve Disease Coding Accuracy.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- A Method for EHR Phenotype Management in an i2b2 Data Warehouse.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining.Healthcare (Basel, Switzerland) · 2018Review
- Design and Implementation of a Secure Computing Environment for Analysis of Sensitive Data at an Academic Medical Center.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Urinary Angiogenin Reflects the Magnitude of Kidney Injury at the Infrahistologic Level.Journal of the American Society of Nephrology : JASN · 2017Article
- Architecting the Data Loading Process for an i2b2 Research Data Warehouse: Full Reload versus Incremental Updating.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data.PloS one · 2016Article
- Metadata-driven Clinical Data Loading into i2b2 for Clinical and Translational Science Institutes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016Article
- R-U policy frontiers for health data de-identification.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Clinical research informatics and electronic health record data.Yearbook of medical informatics · 2014Article
- Big data - smart health strategies. Findings from the yearbook 2014 special theme.Yearbook of medical informatics · 2014Review
- Locating relevant patient information in electronic health record data using representations of clinical concepts and database structures.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Urinary retinol binding protein is a marker of the extent of interstitial kidney fibrosis.PloS one · 2014Article
- Temporal abstraction-based clinical phenotyping with Eureka!AMIA ... Annual Symposium proceedings. AMIA Symposium · 2013Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
objectiveTo create an analytics platform for specifying and detecting clinical phenotypes and other derived variables in electronic health record (EHR) data for quality improvement investigations. MATERIALS AND
methodsWe have developed an architecture for an Analytic Information Warehouse (AIW). It supports transforming data represented in different physical schemas into a common data model, specifying derived variables in terms of the common model to enable their reuse, computing derived variables while enforcing invariants and ensuring correctness and consistency of data transformations, long-term curation of derived data, and export of derived data into standard analysis tools. It includes software that implements these features and a computing environment that enables secure high-performance access to and processing of large datasets extracted from EHRs.
resultsWe have implemented and deployed the architecture in production locally. The software is available as open source. We have used it as part of hospital operations in a project to reduce rates of hospital readmission within 30days. The project examined the association of over 100 derived variables representing disease and co-morbidity phenotypes with readmissions in 5years of data from our institution's clinical data warehouse and the UHC Clinical Database (CDB). The CDB contains administrative data from over 200 hospitals that are in academic medical centers or affiliated with such centers. DISCUSSION AND
conclusionA widely available platform for managing and detecting phenotypes in EHR data could accelerate the use of such data in quality improvement and comparative effectiveness studies.
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.