scholarly journals COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes

2020 ◽  
Vol 27 (9) ◽  
pp. 1437-1442 ◽  
Author(s):  
Xiao Dong ◽  
Jianfu Li ◽  
Ekin Soysal ◽  
Jiang Bian ◽  
Scott L DuVall ◽  
...  
Keyword(s):  
Web Application ◽  
Data Networks ◽  
Clinical Implementation ◽  
Health Records ◽  
Critical Data ◽  
Secondary Use ◽  
Independent Test ◽  
Data Elements ◽  
Online Web ◽  
Critical Resources

Abstract Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.

scholarly journals Development of a COVID-19 Application Ontology for the ACT Network

JAMIA Open ◽  
2021 ◽  
Author(s):  
Shyam Visweswaran ◽  
Malarkodi J Samayamuthu ◽  
Michele Morris ◽  
Griffin M Weber ◽  
Douglas MacFadden ◽  
...  
Keyword(s):  
Laboratory Tests ◽  
Research Community ◽  
Health Centers ◽  
Academic Health Centers ◽  
Data Networks ◽  
Health Records ◽  
Course Of Illness ◽  
Application Ontology ◽  
Value Sets ◽  
Data Elements

Abstract Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that that are critical to COVID-19 research. The ontology contains over 50,000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for SARS-CoV-2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of nine academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

scholarly journals Development of a COVID-19 Application Ontology for the ACT Network

2021 ◽  
Author(s):  
Shyam Visweswaran ◽  
Malarkodi J Samayamuthu ◽  
Michele Morris ◽  
Griffin M Weber ◽  
Douglas MacFadden ◽  
...  
Keyword(s):  
Laboratory Tests ◽  
Research Community ◽  
Health Centers ◽  
Academic Health Centers ◽  
Data Networks ◽  
Health Records ◽  
Course Of Illness ◽  
Application Ontology ◽  
Value Sets ◽  
Data Elements

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that that are critical to COVID-19 research. The ontology contains over 50,000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for SARS-CoV-2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of nine academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

scholarly journals Leveraging Real-World Data for the Selection of Relevant Eligibility Criteria for the Implementation of Electronic Recruitment Support in Clinical Trials

2021 ◽  
Vol 12 (01) ◽  
pp. 017-026
Author(s):  
Georg Melzer ◽  
Tim Maiwald ◽  
Hans-Ulrich Prokosch ◽  
Thomas Ganslandt
Keyword(s):  
Data Warehouse ◽  
Real World ◽  
Chart Review ◽  
Patient Recruitment ◽  
Real World Data ◽  
Eligibility Criteria ◽  
Health Records ◽  
World Data ◽  
Paper Chart ◽  
Data Elements

Abstract Background Even though clinical trials are indispensable for medical research, they are frequently impaired by delayed or incomplete patient recruitment, resulting in cost overruns or aborted studies. Study protocols based on real-world data with precisely expressed eligibility criteria and realistic cohort estimations are crucial for successful study execution. The increasing availability of routine clinical data in electronic health records (EHRs) provides the opportunity to also support patient recruitment during the prescreening phase. While solutions for electronic recruitment support have been published, to our knowledge, no method for the prioritization of eligibility criteria in this context has been explored. Methods In the context of the Electronic Health Records for Clinical Research (EHR4CR) project, we examined the eligibility criteria of the KATHERINE trial. Criteria were extracted from the study protocol, deduplicated, and decomposed. A paper chart review and data warehouse query were executed to retrieve clinical data for the resulting set of simplified criteria separately from both sources. Criteria were scored according to disease specificity, data availability, and discriminatory power based on their content and the clinical dataset. Results The study protocol contained 35 eligibility criteria, which after simplification yielded 70 atomic criteria. For a cohort of 106 patients with breast cancer and neoadjuvant treatment, 47.9% of data elements were captured through paper chart review, with the data warehouse query yielding 26.9% of data elements. Score application resulted in a prioritized subset of 17 criteria, which yielded a sensitivity of 1.00 and specificity 0.57 on EHR data (paper charts, 1.00 and 0.80) compared with actual recruitment in the trial. Conclusion It is possible to prioritize clinical trial eligibility criteria based on real-world data to optimize prescreening of patients on a selected subset of relevant and available criteria and reduce implementation efforts for recruitment support. The performance could be further improved by increasing EHR data coverage.

scholarly journals Secondary Use of Electronic Health Records for Building Large, Real-World ILD Cohorts

2021 ◽  
Author(s):  
E.D. Farrand ◽  
O. Gologorskaya ◽  
H. Mills ◽  
L. Radhakrishnan ◽  
H.R. Collard ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Real World ◽  
Health Records ◽  
Secondary Use ◽  
Electronic Health

scholarly journals The Universal Factory

2019 ◽  
Vol 16 (2) ◽  
pp. 18-31
Author(s):  
Kevin Rogan
Keyword(s):  
Urban Design ◽  
Smart Cities ◽  
Data Networks ◽  
The Political ◽  
Political Issues ◽  
Critical Data ◽  
The Third ◽  
Material Reality ◽  
Vast Network

Critical data studies have made great strides in bringing together data analysts and urban design, providing an extensible concept which is useful in visualizing the role of local and planetary data networks. But in the light of the experience of Sidewalk Labs, critical data studies need a further push. As smart cities, algorithmic urbanisms, and sensorial regimes inch closer and closer to reality, critical data studies remain woefully blind to economic and political issues. Data remains undertheorized for its economic content as a commodity, and the political ramifications of the data assemblages remain locked in a proto-political schema of good and bad uses of this vast network of data collection, analysis, research, and organization. This paper attempts to subject critical data studies to a rigorous critique by deepening its relationship to the history thus far of Sidewalk Labs’ project in Quayside, Toronto. It is broken into sections. The first section discusses the material reality of Kitchin and Lauriault’s (2014) data assemblages and data landscapes. The second section investigates data itself and what its ‘inherent’ value means in an economic sense. The third section looks at the way the understanding of data promoted by the data assemblage effects smart city design. The fourth section examines the role of the designer in shepherding this vision, and moreover the data assemblage, into existence.

scholarly journals The Efficacy and Usefulness of Online Web Application Based Logbook for Ophthalmology Residents

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Navapol Kanchanaranya ◽  
Chartchai Wibhusanawit ◽  
Tayakorn Kupakanchana
Keyword(s):  
Web Application ◽  
Online Web

scholarly journals Diabetes and the direct secondary use of electronic health records: Using routinely collected and stored data to drive research and understanding

2018 ◽  
Vol 4 ◽  
pp. 205520761880465 ◽  
Author(s):  
Tim Robbins ◽  
Sarah N Lim Choi Keung ◽  
Sailesh Sankar ◽  
Harpal Randeva ◽  
Theodoros N Arvanitis
Keyword(s):  
Electronic Health Records ◽  
Missing Values ◽  
Research Output ◽  
Healthcare Research ◽  
Healthcare Sector ◽  
Diabetes Research ◽  
Health Records ◽  
Diverse Range ◽  
Secondary Use ◽  
Electronic Health

Introduction Electronic health records provide an unparalleled opportunity for the use of patient data that is routinely collected and stored, in order to drive research and develop an epidemiological understanding of disease. Diabetes, in particular, stands to benefit, being a data-rich, chronic-disease state. This article aims to provide an understanding of the extent to which the healthcare sector is using routinely collected and stored data to inform research and epidemiological understanding of diabetes mellitus. Methods Narrative literature review of articles, published in both the medical- and engineering-based informatics literature. Results There has been a significant increase in the number of papers published, which utilise electronic health records as a direct data source for diabetes research. These articles consider a diverse range of research questions. Internationally, the secondary use of electronic health records, as a research tool, is most prominent in the USA. The barriers most commonly described in research studies include missing values and misclassification, alongside challenges of establishing the generalisability of results. Discussion Electronic health record research is an important and expanding area of healthcare research. Much of the research output remains in the form of conference abstracts and proceedings, rather than journal articles. There is enormous opportunity within the United Kingdom to develop these research methodologies, due to national patient identifiers. Such a healthcare context may enable UK researchers to overcome many of the barriers encountered elsewhere and thus to truly unlock the potential of electronic health records.

scholarly journals EPID-23. PURSUIT OF AN INTERNATIONAL LANGUAGE OF GLIOMA RESEARCH: COMMON DATA ELEMENTS FOR THE LONGITUDINAL STUDY OF ADULT MALIGNANT GLIOMA

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi79-vi79
Author(s):  
Laila Poisson ◽  
M C M Kouwenhoven ◽  
James Snyder ◽  
Kristin Alfaro-Munoz ◽  
Manpreet Kaur ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Malignant Glioma ◽  
Web Application ◽  
Data Capture ◽  
Data Repositories ◽  
Common Data Elements ◽  
Research Perspectives ◽  
Share Data ◽  
Data Elements ◽  
Meta Analyses

Abstract As an uncommon cancer, clinical and translational studies of glioma rely on multi-center collaborations, confirmatory studies, and meta-analyses. Unfortunately, interpretation of results across studies is hampered by the absence of uniformly coded clinical data. Common Data Elements (CDE) represent a set of clinical features for which the language has been standardized for consistent data capture across studies, institutions and registries. We constructed CDE for the longitudinal study of adult malignant glioma. To identify the minimum set of CDE needed to describe the clinical course of glioma, we surveyed clinical standards, ongoing trials, published studies, and data repositories for frequently used data elements. We harmonized the identified clinical variables, filled in gaps, and structured them in a modular schema, defining CDE for patient demographics, medical history, diagnosis, surgery, chemotherapy, radiotherapy, other treatments, and outcomes. Multidisciplinary experts from the Glioma Longitudinal AnalySiS (GLASS) consortium, representing clinical, molecular, and data research perspectives, were consulted regarding CDE. The validity and capture feasibility of the CDE were assessed through harmonization across published studies, then validated with single institution retrospective chart abstraction. The refined CDE library is implemented in the Research Electronic Data Capture (REDCap) System, a secure web application for building and managing online surveys and databases. The work was motivated by the GLASS consortium, which supports the aggregation and analysis of complex genetic datasets used to define molecular trajectories for glioma. The goal is that modular REDCap implementation of CDE allows broad adoption in glioma research. To accommodate novel aspects, the CDE sets can be expanded through additional modules. In contrast, for efficient initiation of focused studies, subsets of CDE can be selected. Broad adoption of CDE will improve the ability to compare results and share data between studies, thereby maximizing the value of existing data sources and small patient populations.

scholarly journals 66. Field Research in EMS: Who is Doing the Work?

1996 ◽  
Vol 11 (S2) ◽  
pp. S36-S36
Author(s):  
David R. Johnson ◽  
Mark B. Napier
Keyword(s):  
Educational Institution ◽  
Field Research ◽  
Field Studies ◽  
System Structure ◽  
Public And Private ◽  
Exact Test ◽  
Critical Data ◽  
And Performance ◽  
Data Elements ◽  
Meta Analyses

Purpose: To determine what types of EMS systems (public vs. private) are contributing to the peer reviewed field research in EMS and what type of research is being done by these agencies.Methods: A Medline literature search was conducted of all peer reviewed journals using the search terms: EMS, emergency medical services, EMT, paramedic, and ambulance. Studies published between 1976 and 1995 meeting these criteria were reviewed and classified as field or non-field studies. Studies were classified as field studies if they evaluated clinical outcomes or overall EMS system structure and performance. The type of EMS system in which the study was conducted was classified as: public (PB), private (PR), or a mixture of public and private agencies (PP). If the type of system was not evident in the paper, the primary author or EMS agency was contacted by phone. The primary affiliation of the first author was classified as being with: an educational institution, hospital, government agency, or EMS agency. Each study was also classified as being primarily clinical or evaluating EMS system structure. Review articles, editorials, and meta-analyses were excluded as were studies in which critical data elements could not be verified. Fischer's exact test was used for statistical analysis.Results: A total of 365 studies were evaluated with 66 non-field studies being excluded from analysis. 75 studies did not meet inclusionary criteria. This left 224 studies for analysis. PB systems accounted for 167 (74.5%) of field studies, with PP 44 (19.6%) and PR 13 (5.8%). Clinical studies were more commonly done by PB systems (72.5%) when compared to PR systems (38.5%), p = 0.02. System structure studies accounted for the majority of studies done by PR systems (61.5%). An affiliation with an educational institution such as a university occurred in 61.2% of the studies. The number of field studies done by PB systems has increased steadily over the last 10 years while field studies published by PR and PP systems has remained at a low level, with none published from 1992–1994.

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