Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Vipina K Keloth¹, Juan M Banda², Michael Gurley³, Paul M Heider⁴, Georgina Kennedy⁵, Hongfang Liu⁶, Feifan Liu⁷, Timothy Miller⁸, Karthik Natarajan⁹, Olga V Patterson¹⁰, Yifan Peng¹¹, Kalpana Raja¹, Ruth M Reeves¹², Masoud Rouhizadeh¹³, Jianlin Shi¹⁴, Xiaoyan Wang¹⁵, Yanshan Wang¹⁶, Wei-Qi Wei¹⁷, Andrew E Williams¹⁸, Rui Zhang¹⁹, Rimma Belenkaya²⁰, Christian Reich²¹, Clair Blacketer²², Patrick Ryan²³, George Hripcsak⁹, Noémie Elhadad²⁴, Hua Xu²⁵

Affiliations

¹ Section of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA.
² Department of Computer Science, Georgia State University, Atlanta, GA, USA.
³ Lurie Cancer Center, Northwestern University, Chicago, Illinois, USA.
⁴ Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.
⁵ Ingham Institute for Applied Medical Research, Sydney, Australia.
⁶ Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
⁷ Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA.
⁸ Computational Health Informatics Program, Boston Children's Hospital, and Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
⁹ Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
¹⁰ VA Informatics and Computing Infrastructure, Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA; Division of Epidemiology, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA; Verily Life Sciences, Mountain View, CA, USA.
¹¹ Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
¹² TN Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
¹³ Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA; Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA.
¹⁴ VA Informatics and Computing Infrastructure, Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA; Division of Epidemiology, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA; Department of Biomedical Informatics, University of Utah, Salt Lake City, USA.
¹⁵ Sema4 Mount Sinai Genomics Incorporation, Stamford, CT, USA.
¹⁶ Department of Health Information Management, Department of Biomedical Informatics, and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
¹⁷ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁸ School of Medicine, Tufts University, Boston, MA, USA.
¹⁹ Institute for Health Informatics, and Department of Pharmaceutical Care & Health Systems, University of Minnesota, Minneapolis, MN, USA.
²⁰ Memorial Sloan Kettering Cancer Center, New York, NY, USA.
²¹ Real World Solutions, IQVIA, Durham, NC, USA.
²² Janssen Pharmaceutical Research and Development LLC, Titusville, NJ, USA; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.
²³ Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA; Janssen Pharmaceutical Research and Development LLC, Titusville, NJ, USA.
²⁴ Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA. Electronic address: noemie.elhadad@columbia.edu.
²⁵ Section of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA. Electronic address: hua.xu@yale.edu.

PMID: 36935011
PMCID: PMC10428170
DOI: 10.1016/j.jbi.2023.104343

Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Vipina K Keloth et al. J Biomed Inform. 2023 Jun.

. 2023 Jun:142:104343.

doi: 10.1016/j.jbi.2023.104343. Epub 2023 Mar 17.

Authors

Affiliations

¹ Section of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA.
² Department of Computer Science, Georgia State University, Atlanta, GA, USA.
³ Lurie Cancer Center, Northwestern University, Chicago, Illinois, USA.
⁴ Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.
⁵ Ingham Institute for Applied Medical Research, Sydney, Australia.
⁶ Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
⁷ Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA.
⁸ Computational Health Informatics Program, Boston Children's Hospital, and Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
⁹ Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
¹⁰ VA Informatics and Computing Infrastructure, Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA; Division of Epidemiology, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA; Verily Life Sciences, Mountain View, CA, USA.
¹¹ Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
¹² TN Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
¹³ Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA; Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA.
¹⁴ VA Informatics and Computing Infrastructure, Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA; Division of Epidemiology, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA; Department of Biomedical Informatics, University of Utah, Salt Lake City, USA.
¹⁵ Sema4 Mount Sinai Genomics Incorporation, Stamford, CT, USA.
¹⁶ Department of Health Information Management, Department of Biomedical Informatics, and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
¹⁷ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁸ School of Medicine, Tufts University, Boston, MA, USA.
¹⁹ Institute for Health Informatics, and Department of Pharmaceutical Care & Health Systems, University of Minnesota, Minneapolis, MN, USA.
²⁰ Memorial Sloan Kettering Cancer Center, New York, NY, USA.
²¹ Real World Solutions, IQVIA, Durham, NC, USA.
²² Janssen Pharmaceutical Research and Development LLC, Titusville, NJ, USA; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.
²³ Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA; Janssen Pharmaceutical Research and Development LLC, Titusville, NJ, USA.
²⁴ Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA. Electronic address: noemie.elhadad@columbia.edu.
²⁵ Section of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA. Electronic address: hua.xu@yale.edu.

PMID: 36935011
PMCID: PMC10428170
DOI: 10.1016/j.jbi.2023.104343

Abstract

Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this paper, we describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions with English textual data. Challenges faced and lessons learned during the process are also discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies.

Keywords: Electronic health records; Natural language processing; Real-world study.

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Conflict of interest statement

Declaration of Competing Interest Dr. Hua Xu and The University of Texas Health Science Center at Houston have research related financial interests at Melax Technologies Inc. Dr. Xiaoyan Wang has related financial interests at Sema4 Mount Sinai Genomics Inc.

Figures

**Figure 1:**
Schema of NOTE (left) and NOTE_NLP (right) tables in OMOP CDM 5.4 [30]

**Figure 2:**
An overview of the workflow for transforming clinical text in the NOTE table

See this image and copyright information in PMC

References

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Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Affiliations

Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Miscellaneous