Rebecca Kush

1.3k total citations
30 papers, 693 citations indexed

About

Rebecca Kush is a scholar working on Health Information Management, Public Health, Environmental and Occupational Health and Molecular Biology. According to data from OpenAlex, Rebecca Kush has authored 30 papers receiving a total of 693 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Health Information Management, 9 papers in Public Health, Environmental and Occupational Health and 8 papers in Molecular Biology. Recurrent topics in Rebecca Kush's work include Electronic Health Records Systems (9 papers), Biomedical Text Mining and Ontologies (8 papers) and Ethics in Clinical Research (6 papers). Rebecca Kush is often cited by papers focused on Electronic Health Records Systems (9 papers), Biomedical Text Mining and Ontologies (8 papers) and Ethics in Clinical Research (6 papers). Rebecca Kush collaborates with scholars based in United States, Russia and Japan. Rebecca Kush's co-authors include Lauren B. Becnel, Frank W. Rockhold, Edward D. Helton, Lynn D. Hudson, Mats Sundgren, Brecht Claerhout, Andreas Schmidt, Christian Ohmann, Bárbara Jaúregui and Danielle Dupont and has published in prestigious journals such as New England Journal of Medicine, Science Translational Medicine and Drug Discovery Today.

In The Last Decade

Rebecca Kush

27 papers receiving 667 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Rebecca Kush United States 14 205 204 174 136 88 30 693
Wolfgang Kuchinke Germany 13 174 0.8× 166 0.8× 153 0.9× 83 0.6× 89 1.0× 35 540
Brecht Claerhout Spain 10 188 0.9× 218 1.1× 168 1.0× 193 1.4× 59 0.7× 31 827
Georges De Moor Belgium 15 248 1.2× 309 1.5× 208 1.2× 279 2.1× 58 0.7× 51 927
M.S. Mendis Sri Lanka 6 223 1.1× 201 1.0× 98 0.6× 240 1.8× 86 1.0× 14 728
Daniel J. Nigrin United States 15 117 0.6× 212 1.0× 179 1.0× 114 0.8× 46 0.5× 22 797
Georg Duftschmid Austria 17 166 0.8× 268 1.3× 128 0.7× 109 0.8× 32 0.4× 61 653
David Kreda United States 9 180 0.9× 412 2.0× 185 1.1× 149 1.1× 36 0.4× 21 868
Éric Zapletal France 14 142 0.7× 163 0.8× 77 0.4× 144 1.1× 38 0.4× 34 639
Christel Daniel France 18 427 2.1× 340 1.7× 207 1.2× 289 2.1× 67 0.8× 69 1.3k
Meredith Nahm United States 11 132 0.6× 161 0.8× 128 0.7× 94 0.7× 47 0.5× 22 467

Countries citing papers authored by Rebecca Kush

Since Specialization
Citations

This map shows the geographic impact of Rebecca Kush's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rebecca Kush with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rebecca Kush more than expected).

Fields of papers citing papers by Rebecca Kush

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rebecca Kush. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rebecca Kush. The network helps show where Rebecca Kush may publish in the future.

Co-authorship network of co-authors of Rebecca Kush

This figure shows the co-authorship network connecting the top 25 collaborators of Rebecca Kush. A scholar is included among the top collaborators of Rebecca Kush based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rebecca Kush. Rebecca Kush is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chronaki, Catherine, et al.. (2025). Standards in sync: five principles to achieve semantic interoperability for TRUE research for healthcare. Frontiers in Digital Health. 7. 1567624–1567624.
2.
Ertürkmen, Gökçe Banu Laleci, Ali Anıl Sınacı, Karim Lekadir, et al.. (2024). An assessment of the European Patient Summary for clinical research: a case study in cardiology. Frontiers in Medicine. 11. 1481551–1481551.
3.
Ros, Francisco, et al.. (2020). Addressing the Covid‐19 pandemic and future public health challenges through global collaboration and a data‐driven systems approach. Learning Health Systems. 5(1). e10253–e10253. 27 indexed citations
4.
Kush, Rebecca, Denise Warzel, Alexander Sherman, et al.. (2020). FAIR data sharing: The roles of common data elements and harmonization. Journal of Biomedical Informatics. 107. 103421–103421. 99 indexed citations
5.
Hudson, Lynn D., Rebecca Kush, Bárbara Jaúregui, et al.. (2018). Global Standards to Expedite Learning From Medical Research Data. Clinical and Translational Science. 11(4). 342–344. 6 indexed citations
6.
Kush, Rebecca, et al.. (2015). An Examination of eClinical Technology Usage and CDISC Standards Adoption. Therapeutic Innovation & Regulatory Science. 49(6). 869–876. 8 indexed citations
7.
Moor, Georges De, Mats Sundgren, Dipak Kalra, et al.. (2014). Using electronic health records for clinical research: The case of the EHR4CR project. Journal of Biomedical Informatics. 53. 162–173. 133 indexed citations
8.
Mäurer, W, Lea Seeber, Gabriella Rundblad, et al.. (2014). Standardization and simplification of vaccination records. Expert Review of Vaccines. 13(4). 545–559. 13 indexed citations
9.
Evans, John, Thomas A. Oniki, Joanne Coyle, et al.. (2014). Harmonization of Detailed Clinical Models with Clinical Study Data Standards. Methods of Information in Medicine. 54(1). 65–74. 17 indexed citations
10.
Kush, Rebecca, et al.. (2014). Fostering Responsible Data Sharing through Standards. New England Journal of Medicine. 370(23). 2163–2165. 24 indexed citations
11.
Jiang, Guoqian, et al.. (2010). A Collaborative Framework for Representation and Harmonization of Clinical Study Data Elements Using Semantic MediaWiki.. PubMed. 2010. 11–5. 21 indexed citations
12.
Kush, Rebecca. (2009). What the Patient Should Order. Science Translational Medicine. 1(3). 3cm3–3cm3. 3 indexed citations
13.
Hammond, W. E., et al.. (2009). Healthcare standards development. The value of nurturing collaboration.. PubMed. 80(7). 44–50; quiz 51. 13 indexed citations
14.
Kush, Rebecca, et al.. (2008). Electronic Health Records, Medical Research, and the Tower of Babel. New England Journal of Medicine. 358(16). 1738–1740. 57 indexed citations
15.
Bleicher, Paul A., et al.. (2007). Special Section on Clinical Research Standards Introduction. Drug Information Journal. 41(3). 369–371.
16.
Speakman, John, et al.. (2007). A Standard Computable Clinical Trial Protocol: The Role of the BRIDG Model. Drug Information Journal. 41(3). 383–392. 4 indexed citations
17.
Kush, Rebecca, Liora Alschuler, Rosaria Maddalena Ruggeri, et al.. (2007). Implementing Single Source: The STARBRITE Proof-of-Concept Study. Journal of the American Medical Informatics Association. 14(5). 662–673. 52 indexed citations
18.
Kush, Rebecca, et al.. (2007). Global clinical data interchange standards are here!. Drug Discovery Today. 12(3-4). 174–181. 40 indexed citations
19.
Kush, Rebecca. (2007). Healthcare and clinical research: a critical link through standards. Community Oncology. 4(9). 553–557. 1 indexed citations
20.
Kush, Rebecca, Ota K, Akihiro C. Yamashita, et al.. (1990). Long-Term Continuous Ambulatory Peritoneal Dialysis. Blood Purification. 8(1). 1–13. 22 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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