TY - GEN
T1 - Detecting, Reporting and Alleviating Racial Biases in Standardized Medical Terminologies and Ontologies
AU - Geller, James
AU - Kollapally, Navya Martin
N1 - Publisher Copyright:
© 2021 IEEE.
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
UR - http://www.scopus.com/inward/record.url?scp=85125197636&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125197636&partnerID=8YFLogxK
U2 - 10.1109/BIBM52615.2021.9669617
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 -