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Hyeoneui Kim, PhD, MPH, RN

Associate Professor
Email: 
hyeoneui.kim@duke.edu
Phone: 
(919) 684-7534
Office: 
3148 Pearson Building

Dr. Kim is Associate Professor at the Duke University School of Nursing. Dr. Kim received a PhD in Health Informatics from the University of Minnesota at Twin Cities, completed a post-doctoral fellowship in the Decision Systems Group at the Brigham and Women’s Hospital in Boston. After the post-doctoral training, she joined the Clinical Informatics Research and Development group of Partner’s Healthcare as a nurse informatician, where she was heavily involved in analyzing and designing nursing documentation system.
Dr. Kim came to Duke from UC San Diego’s School of Medicine, Division of Biomedical Informatics where she was an associate professor. She was also associate director of the T15 training program on biomedical informatics funded through the National Library of Medicine. Her research interests in data standardization, consumer informatics, and nursing decision support complement the school’s research areas of excellence.
The overall goal of Dr. Kim’s research is to facilitate the reuse of biomedical data via standardization and effective representations. Data standardization encompasses standardized terminologies, metadata generation and information modeling, which are critical foundations for any informatics solutions that deal with biomedical data. Dr. Kim’s work has particular focuses on the reuse of clinical data to support patient care and she have demonstrated the impact of data reuse by developing prototype nursing clinical decision support tools and algorithms for acuity estimation, pressure ulcer risk assessment, and pain assessment. Dr. Kim is also experienced in developing infrastructure for data standardization to support big data analyses through two NIH funded projects. In these projects, Dr. Kim led development of a data standardization tool and standardization of the metadata extracted from various biomedical datasets.
Dr. Kim’s data standardization and representation work also expands to the area of consumer health informatics, Dr. Kim’s research in consumer focus data representation includes use of pictograms in health communication and assessing/improving the readability of various health texts. Dr. Kim’s latest consumer oriented informatics research aims to empower patients by providing a means to indicate their preference on sharing health data for research. Presenting a wide range of health data in such a way that help patients determine the level of data sharing is also critical component in this project.

Academic Program Affiliations

  • Master of Science in Nursing Program
  • PhD in Nursing Program

Education

  • PhD - University of Minnesota at Twin Cities
  • MPH - Seoul National University
  • BSN - Seoul National University

Research Interests

Data standardization
Nursing decision support system
Consumer informatics
Health disparity

Awards and Honors

  • 2017 || Daniel T. O’Connor Memorial Award, UC San Diego, CTRI
  • 2012 || Best poster award, Healthcare Informatics, Imaging and Systems Biology
  • 2010 || Harriet H. Werley Award, American Medical Informatics Association
  • 2007 || Working Group Student Award, American Medical Informatics Association Nursing Informatics

Areas of Expertise

  • Standardized Data Representation

Representative Publications

  • 2017 -- PubMed # : 28430977 Zong, N. and Kim, H. and Ngo, V. and Harismendy, O. Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations. Bioinformatics. 33(15); 2337-2344
  • 2017 -- PubMed # : 28585923 Sansone, S. and Gonzalez-Beltran, A. and Rocca-Serra, P. and Alter, G. and Grethe, J. S. and Xu, H. and Fore, I. M. and Lyle, J. and Gururaj, A. E. and Chen, X. and Kim, H. and Zong, N. and Li, Y. and Liu, R. and Ozyurt, I. B. and Ohno-Machado, L. DATS, the data tag suite to enable discoverability of datasets. Sci Data. 4 170059 PMC5460592
  • 2017 -- PubMed # : 28207198 Sitapati, A. and Kim, H. and Berkovich, B. and Marmor, R. and Singh, S. and El-Kareh, R. and Clay, B. and Ohno-Machado, L. Integrated precision medicine: the role of electronic health records in delivering personalized treatment. Wiley Interdiscip Rev Syst Biol Med. 9(3); PMC5400726
  • 2017 -- PubMed # : 28546571 Ohno-Machado, L. and Sansone, S. and Alter, G. and Fore, I. and Grethe, J. and Xu, H. and Gonzalez-Beltran, A. and Rocca-Serra, P. and Gururaj, A. E. and Bell, E. and Soysal, E. and Zong, N. and Kim, H. Finding useful data across multiple biomedical data repositories using DataMed. Nat Genet. 49(6); 816-819
  • 2017 -- PubMed # : 27589942 Kim, H. and Bell, E. and Kim, J. and Sitapati, A. and Ramsdell, J. and Farcas, C. and Friedman, D. and Feupe, S. F. and Ohno-Machado, L. iCONCUR: informed consent for clinical data and bio-sample use for research. J Am Med Inform Assoc. 24(2); 380-387 PMC5391727
  • 2017 -- PubMed # : 27688295 Nodora, J. N. and Komenaka, I. K. and Bouton, M. E. and Ohno-Machado, L. and Schwab, R. and Kim, H. and Farcas, C. and Perez, G. and Elena Martinez, M. Biospecimen Sharing Among Hispanic Women in a Safety-Net Clinic: Implications for the Precision Medicine Initiative. J Natl Cancer Inst. 109(2); PMC5040829
  • 2016 -- PubMed # : 27784835 Kim, H. and Jang, I. and Quach, J. and Richardson, A. and Kim, J. and Choi, J. Explorative Analyses of Nursing Research Data. West J Nurs Res.
  • 2016 -- PubMed # : 27454233 Yang, L. and Wang, S. and Jiang, X. and Cheng, S. and Kim, H. PATTERN: Pain Assessment for paTients who can't TEll using Restricted Boltzmann machiNe. BMC Med Inform Decis Mak. 16 Suppl 3 73 PMC4959350
  • 2016 -- PubMed # : 28269868 Kim, H. and Choi, J. and Jang, I. and Quach, J. and Ohno-Machado, L. Feasibility of Representing Data from Published Nursing Research Using the OMOP Common Data Model. AMIA Annu Symp Proc. 2016 715-723 PMC5333244
  • 2016 -- PubMed # : 27332230 Kim, J. and Fnu, V. and Bell, E. and Kim, H. Feasibility of the Rule-Based Approach to Creating Complex Pictograms. Stud Health Technol Inform. 225 397-401

Pages

Grant Funding (Selected)

  • Informed Consent for Clinical Data and Bio-sample Use for Research

    NIH/NHGRI
    R01HG008802-02
    09/2015 to 06/2018

    Project Goal: The goal of this project is to incorporate genetic test data produced at the laboratories outside UCSD Medical center to the clinical data warehouse at UCSD to promote reuse of the genetic test data for research and patient treatment.

  • bioCADDIE: biomedical and healthcare data discovery and indexing engine center

    NIH/NIAID
    U24AI117966-05
    09/2014 to 08/2017
    Role: Co-I

    Project Goal: The goal of this project is to promote the development of realistic, minimal, and user-friendly metadata specifications and annotation for biomedical and healthcare data and corresponding tools for automated indexing to facilitate reuse of biomedical big data.

  • Phenotype discovery NHLBI genomic studies (PhD)

    NIH/NHLBI
    UH3HL108785
    07/2011 to 09/2015

    Project Goal: The goal of this project is to develop a defined metadata model and build an integrated system that enables researchers to query and find genomic studies of interest in public repositories as well as upload new data into our database (sdGaP), in a standardized manner.

  • iDASH – integrating data for analysis, anonymization, and sharing

    NLH/NHLBI
    HL108460
    09/2010 to 06/2017
    Role: Co-I

    Project Goal: The goal of this project is to create IDASH, a national center for biomedical computing that will develop new algorithms, open-source tools, computational infrastructure and services that will enable biomedical and behavioral researchers nationwide to integrate Data for Analysis, Anonymization, and Sharing, IDASH will address fundamental challenges to research progress by providing a secure, privacy-preserving environment in which researchers can analyze genomic, transcriptomic and highly annotated phenotypic data.

  • SCANNER – scalable national network for effectiveness research

    AHRQ
    R01HS19913
    09/2010 to 09/2013
    Role: CO-I

    Project Goal: The goal of this project is to develop a distributed network infrastructure for comparative effectiveness research that provides flexibility to participant sites in the means for data sharing.

  • Global health knowledge collective – a collaborative toolkit for global health

    NIH/FIC, NIH/OD
    R24TW008805
    09/2010 to 08/2012
    Role: CO-I

    Project Goal: The goal of this project is to promote efficient health care services in resource-poor settings in low-to-middle income countries with knowledge collectives implemented on low cost computing device.

  • San Diego biomedical informatics education & research

    NIH/NLM
    T15LM11271
    07/2012 to 06/2021
    Role: Core faculty (associate director)

    Project Goal: The biomedical informatics training program at UC San Diego provides biomedical informatics training and research opportunities through pre-doctoral and post-doctoral levels.

Richesson, Kim and PhD Student Douthit Bring Honors at AMIA 2018 Informatics Summit

Wednesday, March 28, 2018

At the annual AMIA 2018 Informatics Summit in San Franscisco which focuses on informatics research and innovation that facilitates translational research, two DUSON faculty papers were recognized. Hyeoneui Kim's paper entitled "iCONCUR: informed consent for clinical data and bio-sample use for research" and Rachel Richesson's paper entitled "Pragmatic (trial) informatics: a perspective from the NIH Health Care Systems Researach Collaboratory." 

Walton, Kim and Yang to Participate in 2018 LEADER Program

Wednesday, January 24, 2018

AnnMarie Walton, Hyeoneui Kim and Qing Yang were recently selected to participate in the 2018 Duke course of Leadership Development for Researchers (LEADER). They will be among 40 outstanding scientists from fields in 13 different School of Medicine departments, the School of Nursing, the Pratt School of Engineering and Duke-NUS in Singapore.

Kim Publishes Articles in Two Publications

Wednesday, November 29, 2017

Hyeoneui Kim recently published an article entitled "Selecting Optimal Subset to release under Differentially Private M-estimators from Hybrid Datasets" in IEEE Transactions of Knowledge and Data Engineering. Co-authors include experts from Stanford University, University of California and Emory University.

Abstract:

Kim Receives Clinical Innovation/Precision Health Small Grant

Tuesday, October 17, 2017

Hyeoneui Kim recently received a Clinical Innovation/ Precision Health small grant for her proposal entitled "PLEDGE: Preparing Lifestyle and Living Environment Data to Guide Precision Health."