ArticleCurrent oncology reports2012
Leveraging EHR data for outcomes and comparative effectiveness research in oncology.
Article in Current oncology reports, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Exploring Technological Solutions for Interoperability Between Patient Electronic Medical Records and Clinical Registries: Scoping Review.Journal of medical Internet research · 2026Article
- Novel Electronic Health Record-Based Data Commons for Pancreatic Cancer.JCO clinical cancer informatics · 2026Article
- Article
- Cancer Patient Perspectives on Sharing of Medical Records and Mobile Device Data for Research Purposes.Journal of patient experience · 2020Article
- Nutrition Information in Oncology - Extending the Electronic Patient-Record Data Set.Journal of medical systems · 2020Article
- Relevant Word Order Vectorization for Improved Natural Language Processing in Electronic Health Records.Scientific reports · 2019Article
- Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes.JAMIA open · 2019Article
- The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- The stroke navigator: meaningful use of the electronic health record to efficiently report inpatient stroke care quality.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models.Annual review of biomedical data science · 2018Article
- The Camden & Islington Research Database: Using electronic mental health records for research.PloS one · 2018Article
- Mapping Multi-Site Clinic Workflows to Design Systems-Enabled Interventions.EGEMS (Washington, DC) · 2017Article
- Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers.Applied clinical informatics · 2016Article
- Linking electronic health records to better understand breast cancer patient pathways within and between two health systems.EGEMS (Washington, DC) · 2015Article
- Development of reusable logic for determination of statin exposure-time from electronic health records.Journal of biomedical informatics · 2014Article
- Community-level determinants of obesity: harnessing the power of electronic health records for retrospective data analysis.BMC medical informatics and decision making · 2014Article
- 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
6 authors.
Funding
Abstract
Along with the increasing adoption of electronic health records (EHRs) are expectations that data collected within EHRs will be readily available for outcomes and comparative effectiveness research. Yet the ability to effectively share and reuse data depends on implementing and configuring EHRs with these goals in mind from the beginning. Data sharing and integration must be planned both locally as well as nationally. The rich data transmission and semantic infrastructure developed by the National Cancer Institute (NCI) for research provides an excellent example of moving beyond paper-based paradigms and exploiting the power of semantically robust, network-based systems, and engaging both domain and informatics expertise. Similar efforts are required to address current challenges in sharing EHR data.
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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.