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

Vipina K. Keloth; Juan M. Banda; Michael Gurley; Paul M. Heider; Georgina Kennedy; Hongfang Liu; Feifan Liu; Timothy Miller; Karthik Natarajan; Olga V Patterson; Yifan Peng; Kalpana Raja; Ruth M. Reeves; Masoud Rouhizadeh; Jianlin Shi; Xiaoyan Wang; Yanshan Wang; Wei Qi Wei; Andrew E. Williams; Rui Zhang; Rimma Belenkaya; Christian Reich; Clair Blacketer; Patrick Ryan; George Hripcsak; Noémie Elhadad; Hua Xu

doi:10.1016/j.jbi.2023.104343

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

Vipina K. Keloth, Juan M. Banda, Michael Gurley, Paul M. Heider, Georgina Kennedy, Hongfang Liu, Feifan Liu, Timothy Miller, Karthik Natarajan, Olga V Patterson, Yifan Peng, Kalpana Raja, Ruth M. Reeves, Masoud Rouhizadeh, Jianlin Shi, Xiaoyan Wang, Yanshan Wang, Wei Qi Wei, Andrew E. Williams, Rui ZhangRimma Belenkaya, Christian Reich, Clair Blacketer, Patrick Ryan, George Hripcsak, Noémie Elhadad^*, Hua Xu

^*Corresponding author for this work

Medical Informatics

Research output: Contribution to journal › Article › Academic › peer-review

5 Citations (Scopus)

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.

Original language	English
Article number	104343
Journal	Journal of Biomedical Informatics
Volume	142
DOIs	https://doi.org/10.1016/j.jbi.2023.104343
Publication status	Published - Jun 2023

Bibliographical note

Funding Information:
Dr. Hua Xu and Dr. Hongfang Liu were supported in part by NCATS 1U01TR002062. Dr. Yifan Peng was supported in part by the National Library of Medicine under Award No. 4R00LM013001. Dr. Rui Zhang was supported in part by NCCIH R01AT009457 and NCATS UL1TR002494. Dr. Paul M. Heider was partially supported through a Patient-Centered Outcomes Research Institute (PCORI) Award (ME-2018C3-14549) and the SmartState Endowment for Translational Biomedical Informatics. Dr. Juan M. Banda was supported in part by a grant from the National Institute on Aging (3P30AG059307-02S1). Dr. George Hripcsak was supported by the National Library of Medicine award R01 LM006910.

Publisher Copyright:
© 2023 Elsevier Inc.

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Keloth, V. K., Banda, J. M., Gurley, M., Heider, P. M., Kennedy, G., Liu, H., Liu, F., Miller, T., Natarajan, K., V Patterson, O., Peng, Y., Raja, K., Reeves, R. M., Rouhizadeh, M., Shi, J., Wang, X., Wang, Y., Wei, W. Q., Williams, A. E., ... Xu, H. (2023). Representing and utilizing clinical textual data for real world studies: An OHDSI approach. Journal of Biomedical Informatics, 142, Article 104343. https://doi.org/10.1016/j.jbi.2023.104343

@article{11dab0af8ff2412ca43aaf25fac1ab65,

title = "Representing and utilizing clinical textual data for real world studies: An OHDSI approach",

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.",

author = "Keloth, {Vipina K.} and Banda, {Juan M.} and Michael Gurley and Heider, {Paul M.} and Georgina Kennedy and Hongfang Liu and Feifan Liu and Timothy Miller and Karthik Natarajan and {V Patterson}, Olga and Yifan Peng and Kalpana Raja and Reeves, {Ruth M.} and Masoud Rouhizadeh and Jianlin Shi and Xiaoyan Wang and Yanshan Wang and Wei, {Wei Qi} and Williams, {Andrew E.} and Rui Zhang and Rimma Belenkaya and Christian Reich and Clair Blacketer and Patrick Ryan and George Hripcsak and No{\'e}mie Elhadad and Hua Xu",

note = "Funding Information: Dr. Hua Xu and Dr. Hongfang Liu were supported in part by NCATS 1U01TR002062. Dr. Yifan Peng was supported in part by the National Library of Medicine under Award No. 4R00LM013001. Dr. Rui Zhang was supported in part by NCCIH R01AT009457 and NCATS UL1TR002494. Dr. Paul M. Heider was partially supported through a Patient-Centered Outcomes Research Institute (PCORI) Award (ME-2018C3-14549) and the SmartState Endowment for Translational Biomedical Informatics. Dr. Juan M. Banda was supported in part by a grant from the National Institute on Aging (3P30AG059307-02S1). Dr. George Hripcsak was supported by the National Library of Medicine award R01 LM006910. Publisher Copyright: {\textcopyright} 2023 Elsevier Inc.",

year = "2023",

month = jun,

doi = "10.1016/j.jbi.2023.104343",

language = "English",

volume = "142",

journal = "Journal of Biomedical Informatics",

issn = "1532-0464",

publisher = "Academic Press",

}

Keloth, VK, Banda, JM, Gurley, M, Heider, PM, Kennedy, G, Liu, H, Liu, F, Miller, T, Natarajan, K, V Patterson, O, Peng, Y, Raja, K, Reeves, RM, Rouhizadeh, M, Shi, J, Wang, X, Wang, Y, Wei, WQ, Williams, AE, Zhang, R, Belenkaya, R, Reich, C, Blacketer, C, Ryan, P, Hripcsak, G, Elhadad, N & Xu, H 2023, 'Representing and utilizing clinical textual data for real world studies: An OHDSI approach', Journal of Biomedical Informatics, vol. 142, 104343. https://doi.org/10.1016/j.jbi.2023.104343

TY - JOUR

T1 - Representing and utilizing clinical textual data for real world studies

T2 - An OHDSI approach

AU - Keloth, Vipina K.

AU - Banda, Juan M.

AU - Gurley, Michael

AU - Heider, Paul M.

AU - Kennedy, Georgina

AU - Liu, Hongfang

AU - Liu, Feifan

AU - Miller, Timothy

AU - Natarajan, Karthik

AU - V Patterson, Olga

AU - Peng, Yifan

AU - Raja, Kalpana

AU - Reeves, Ruth M.

AU - Rouhizadeh, Masoud

AU - Shi, Jianlin

AU - Wang, Xiaoyan

AU - Wang, Yanshan

AU - Wei, Wei Qi

AU - Williams, Andrew E.

AU - Zhang, Rui

AU - Belenkaya, Rimma

AU - Reich, Christian

AU - Blacketer, Clair

AU - Ryan, Patrick

AU - Hripcsak, George

AU - Elhadad, Noémie

AU - Xu, Hua

N1 - Funding Information: Dr. Hua Xu and Dr. Hongfang Liu were supported in part by NCATS 1U01TR002062. Dr. Yifan Peng was supported in part by the National Library of Medicine under Award No. 4R00LM013001. Dr. Rui Zhang was supported in part by NCCIH R01AT009457 and NCATS UL1TR002494. Dr. Paul M. Heider was partially supported through a Patient-Centered Outcomes Research Institute (PCORI) Award (ME-2018C3-14549) and the SmartState Endowment for Translational Biomedical Informatics. Dr. Juan M. Banda was supported in part by a grant from the National Institute on Aging (3P30AG059307-02S1). Dr. George Hripcsak was supported by the National Library of Medicine award R01 LM006910. Publisher Copyright: © 2023 Elsevier Inc.

PY - 2023/6

Y1 - 2023/6

N2 - 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.

AB - 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.

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U2 - 10.1016/j.jbi.2023.104343

DO - 10.1016/j.jbi.2023.104343

M3 - Article

C2 - 36935011

AN - SCOPUS:85160214708

SN - 1532-0464

VL - 142

JO - Journal of Biomedical Informatics

JF - Journal of Biomedical Informatics

M1 - 104343

ER -

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

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