ArticleJCO clinical cancer informatics2021
Structured Data Capture for Oncology.
Article in JCO clinical cancer informatics, 2021. 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
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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.
- Exploring Technological Solutions for Interoperability Between Patient Electronic Medical Records and Clinical Registries: Scoping Review.Journal of medical Internet research · 2026Article
- Assessment of the adequacy of the Fast Healthcare Interoperability Resources (FHIR) Genomics standard for the representation of somatic testing reports.JAMIA open · 2026Article
- APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer.NPJ precision oncology · 2026Article
- Completeness of Common Data Elements for Breast Cancer Clinical Trials in Observational Databases.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Recommendations for Standardizing Nuclear Medicine Terminology and Data in the Era of Theranostics and Artificial Intelligence.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Real-time data in cancer registries: Validation of an automated data extraction system.iScience · 2025Article
- Making the case for an International Childhood Cancer Data Partnership.Journal of the National Cancer Institute · 2025Article
- A CDE-based data structure for radiotherapeutic decision-making in breast cancer.BMC medical informatics and decision making · 2025Article
- Obstacles and drivers in the adoption of Standardized Structured Reporting (SSR): insights from pathologists.Virchows Archiv : an international journal of pathology · 2025Article
- Implementation of a comprehensive clinical quality assurance system in radiation oncology.Radiation oncology (London, England) · 2025Article
- Article
- Challenges of data quality for the management of prostate cancer in Tanzania: A hurdle towards improving the quality of service.PLOS global public health · 2025Article
- Introducing HL7 FHIR Genomics Operations: a developer-friendly approach to genomics-EHR integration.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Article
- Clinical annotations for prostate cancer research: Defining data elements, creating a reproducible analytical pipeline, and assessing data quality.The Prostate · 2022Observational
- Supporting Structured Data Capture for Patients With Cancer: An Initiative of the University of Wisconsin Carbone Cancer Center Survivorship Program to Improve Capture of Malignant Diagnosis and Cancer Staging Data.JCO clinical cancer informatics · 2022Article
- Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality.JCO clinical cancer informatics · 2022Article
- College of American Pathologists Cancer Protocols: From Optimizing Cancer Patient Care to Facilitating Interoperable Reporting and Downstream Data Use.JCO clinical cancer informatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lack of interoperability is one of the greatest challenges facing healthcare informatics. Recent interoperability efforts have focused primarily on data transmission and generally ignore data capture standardization. Structured Data Capture (SDC) is an open-source technical framework that enables the capture and exchange of standardized and structured data in interoperable data entry forms (DEFs) at the point of care. Some of SDC's primary use cases concern complex oncology data such as anatomic pathology, biomarkers, and clinical oncology data collection and reporting. Its interoperability goals are the preservation of semantic, contextual, and structural integrity of the captured data throughout the data's lifespan. SDC documents are written in eXtensible Markup Language (XML) and are therefore computer readable, yet technology agnostic-SDC can be implemented by any EHR vendor or registry. Any SDC-capable system can render an SDC XML file into a DEF, receive and parse an SDC transmission, and regenerate the original SDC form as a DEF or synoptic report with the response data intact. SDC is therefore able to facilitate interoperable data capture and exchange for patient care, clinical trials, cancer surveillance and public health needs, clinical research, and computable care guidelines. The usability of SDC-captured oncology data is enhanced when the SDC data elements are mapped to standard terminologies. For example, an SDC map to Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) enables aggregation of SDC data with other related data sets and permits advanced queries and groupings on the basis of SNOMED CT concept attributes and description logic. SDC supports terminology maps using separate map files or as terminology codes embedded in an SDC document.
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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.