Detecting, Reporting and Alleviating Racial Biases in Standardized Medical Terminologies and Ontologies

James Geller; Navya Martin Kollapally

doi:10.1109/BIBM52615.2021.9669617

Detecting, Reporting and Alleviating Racial Biases in Standardized Medical Terminologies and Ontologies

James Geller
, Navya Martin Kollapally

Computer Information Systems

New Jersey Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Recently, the issue has been raised that personal and systemic biases in organizations, such as some police departments, have also been detected in healthcare organizations. Furthermore, victims of bias incidents often end up in the healthcare system for treatment. Providers use standardized terminologies to record the status of patients in EHRs. To accurately record patient data, these terminologies must contain all the terms that a healthcare provider needs, including terms that might be race-, ethnicity-, or gender-specific. Following reports about gaps in terminologies, we investigated the coverage with respect to such terms in major terminologies such as SNOMED CT, ICD-10, CPT, NCIt and MedDRA. To identify potentially missing terms, we drew on public databases and news articles describing incidents that resulted in minority members requiring medical attention after police interventions. We posit those terms should be added into medical terminologies to improve the ability to record incidents happening inside and outside of the healthcare system.

Original language	English
Title of host publication	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Editors	Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	663-667
Number of pages	5
ISBN (Electronic)	9781665401265
DOIs	https://doi.org/10.1109/BIBM52615.2021.9669617
State	Published - 2021
Event	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States Duration: 9 Dec 2021 → 12 Dec 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/Territory	United States
City	Virtual, Online
Period	9/12/21 → 12/12/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

HealthCare equity
Minority populations.
Police violence
Racial profiling in healthcare

Access to Document

10.1109/BIBM52615.2021.9669617

Cite this

Geller, J., & Kollapally, N. M. (2021). Detecting, Reporting and Alleviating Racial Biases in Standardized Medical Terminologies and Ontologies. In Y. Huang, L. Kurgan, F. Luo, X. T. Hu, Y. Chen, E. Dougherty, A. Kloczkowski, & Y. Li (Eds.), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 (pp. 663-667). (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM52615.2021.9669617

Geller, James ; Kollapally, Navya Martin. / Detecting, Reporting and Alleviating Racial Biases in Standardized Medical Terminologies and Ontologies. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. editor / Yufei Huang ; Lukasz Kurgan ; Feng Luo ; Xiaohua Tony Hu ; Yidong Chen ; Edward Dougherty ; Andrzej Kloczkowski ; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 663-667 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

@inproceedings{a2d6de4e203e4e8e9a5dc205e825b886,

title = "Detecting, Reporting and Alleviating Racial Biases in Standardized Medical Terminologies and Ontologies",

abstract = "Recently, the issue has been raised that personal and systemic biases in organizations, such as some police departments, have also been detected in healthcare organizations. Furthermore, victims of bias incidents often end up in the healthcare system for treatment. Providers use standardized terminologies to record the status of patients in EHRs. To accurately record patient data, these terminologies must contain all the terms that a healthcare provider needs, including terms that might be race-, ethnicity-, or gender-specific. Following reports about gaps in terminologies, we investigated the coverage with respect to such terms in major terminologies such as SNOMED CT, ICD-10, CPT, NCIt and MedDRA. To identify potentially missing terms, we drew on public databases and news articles describing incidents that resulted in minority members requiring medical attention after police interventions. We posit those terms should be added into medical terminologies to improve the ability to record incidents happening inside and outside of the healthcare system.",

keywords = "HealthCare equity, Minority populations., Police violence, Racial profiling in healthcare",

author = "James Geller and Kollapally, \{Navya Martin\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; Conference date: 09-12-2021 Through 12-12-2021",

year = "2021",

doi = "10.1109/BIBM52615.2021.9669617",

language = "English",

series = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "663--667",

editor = "Yufei Huang and Lukasz Kurgan and Feng Luo and Hu, \{Xiaohua Tony\} and Yidong Chen and Edward Dougherty and Andrzej Kloczkowski and Yaohang Li",

booktitle = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

}

Geller, J & Kollapally, NM 2021, Detecting, Reporting and Alleviating Racial Biases in Standardized Medical Terminologies and Ontologies. in Y Huang, L Kurgan, F Luo, XT Hu, Y Chen, E Dougherty, A Kloczkowski & Y Li (eds), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Institute of Electrical and Electronics Engineers Inc., pp. 663-667, 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Virtual, Online, United States, 9/12/21. https://doi.org/10.1109/BIBM52615.2021.9669617

Detecting, Reporting and Alleviating Racial Biases in Standardized Medical Terminologies and Ontologies. / Geller, James; Kollapally, Navya Martin.
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. ed. / Yufei Huang; Lukasz Kurgan; Feng Luo; Xiaohua Tony Hu; Yidong Chen; Edward Dougherty; Andrzej Kloczkowski; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. p. 663-667 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detecting, Reporting and Alleviating Racial Biases in Standardized Medical Terminologies and Ontologies

AU - Geller, James

AU - Kollapally, Navya Martin

PY - 2021

Y1 - 2021

N2 - Recently, the issue has been raised that personal and systemic biases in organizations, such as some police departments, have also been detected in healthcare organizations. Furthermore, victims of bias incidents often end up in the healthcare system for treatment. Providers use standardized terminologies to record the status of patients in EHRs. To accurately record patient data, these terminologies must contain all the terms that a healthcare provider needs, including terms that might be race-, ethnicity-, or gender-specific. Following reports about gaps in terminologies, we investigated the coverage with respect to such terms in major terminologies such as SNOMED CT, ICD-10, CPT, NCIt and MedDRA. To identify potentially missing terms, we drew on public databases and news articles describing incidents that resulted in minority members requiring medical attention after police interventions. We posit those terms should be added into medical terminologies to improve the ability to record incidents happening inside and outside of the healthcare system.

AB - Recently, the issue has been raised that personal and systemic biases in organizations, such as some police departments, have also been detected in healthcare organizations. Furthermore, victims of bias incidents often end up in the healthcare system for treatment. Providers use standardized terminologies to record the status of patients in EHRs. To accurately record patient data, these terminologies must contain all the terms that a healthcare provider needs, including terms that might be race-, ethnicity-, or gender-specific. Following reports about gaps in terminologies, we investigated the coverage with respect to such terms in major terminologies such as SNOMED CT, ICD-10, CPT, NCIt and MedDRA. To identify potentially missing terms, we drew on public databases and news articles describing incidents that resulted in minority members requiring medical attention after police interventions. We posit those terms should be added into medical terminologies to improve the ability to record incidents happening inside and outside of the healthcare system.

KW - HealthCare equity

KW - Minority populations.

KW - Police violence

KW - Racial profiling in healthcare

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DO - 10.1109/BIBM52615.2021.9669617

M3 - Conference contribution

AN - SCOPUS:85125197636

T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

SP - 663

EP - 667

BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

A2 - Huang, Yufei

A2 - Kurgan, Lukasz

A2 - Luo, Feng

A2 - Hu, Xiaohua Tony

A2 - Chen, Yidong

A2 - Dougherty, Edward

A2 - Kloczkowski, Andrzej

A2 - Li, Yaohang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Y2 - 9 December 2021 through 12 December 2021

ER -

Geller J, Kollapally NM. Detecting, Reporting and Alleviating Racial Biases in Standardized Medical Terminologies and Ontologies. In Huang Y, Kurgan L, Luo F, Hu XT, Chen Y, Dougherty E, Kloczkowski A, Li Y, editors, Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 663-667. (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). doi: 10.1109/BIBM52615.2021.9669617

Detecting, Reporting and Alleviating Racial Biases in Standardized Medical Terminologies and Ontologies

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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