One connector, four scholarly indexes, real DOIs and legal free PDFs. Works in Claude and ChatGPT.
Paste it into Claude or ChatGPT as a custom connector. 3-day free trial.
Michael M. C. Sun, Tomasz Oliwa, Monica E. Peek, Elizabeth L. Tung
Little is known about how racism and bias may be communicated in the medical record. This study used machine learning to analyze electronic health records (EHRs) from an urban academic medical center and to investigate whether providers' use of negative patient descriptors varied by patient race or ethnicity. We analyzed a sample of 40,113 history and physical notes (January 2019-October 2020) from 18,459 patients for sentences containing a negative descriptor (for example, resistant or noncompliant) of the patient or the patient's behavior. We used mixed effects logistic regression to determine the odds of finding at least one negative descriptor as a function of the patient's race or ethnicity, controlling for sociodemographic and health characteristics. Compared with White patients, Black patients had 2.54 times the odds of having at least one negative descriptor in the history and physical notes. Our findings raise concerns about stigmatizing language in the EHR and its potential to exacerbate racial and ethnic health care disparities.
Formatted from the source metadata, not typed by hand. For a whole reference list, use the citation generator or ask your assistant through Find Research.
Sun, M. M. C., Oliwa, T., Peek, M. E., & Tung, E. L. (2022). Negative Patient Descriptors: Documenting Racial Bias In The Electronic Health Record. Health Affairs, 41(2), 203–211. https://doi.org/10.1377/hlthaff.2021.01423
Sun, Michael M. C., et al. "Negative Patient Descriptors: Documenting Racial Bias In The Electronic Health Record." Health Affairs, vol. 41, no. 2, 2022, pp. 203–211, https://doi.org/10.1377/hlthaff.2021.01423.
Sun, Michael M. C., Tomasz Oliwa, Monica E. Peek, and Elizabeth L. Tung. "Negative Patient Descriptors: Documenting Racial Bias In The Electronic Health Record." Health Affairs 41, no. 2 (2022): 203–211. https://doi.org/10.1377/hlthaff.2021.01423.
Sun M. M. C., Oliwa T., Peek M. E. [и др.] Negative Patient Descriptors: Documenting Racial Bias In The Electronic Health Record // Health Affairs. — 2022. — Vol. 41, № 2. — P. 203–211. — URL: https://doi.org/10.1377/hlthaff.2021.01423
@article{sun2022negative,
title = {{Negative Patient Descriptors: Documenting Racial Bias In The Electronic Health Record}},
author = {Michael M. C. Sun and Tomasz Oliwa and Monica E. Peek and Elizabeth L. Tung},
journal = {Health Affairs},
year = {2022},
volume = {41},
number = {2},
pages = {203--211},
doi = {10.1377/hlthaff.2021.01423},
url = {https://doi.org/10.1377/hlthaff.2021.01423}
}TY - JOUR AU - Sun, Michael M. C. AU - Oliwa, Tomasz AU - Peek, Monica E. AU - Tung, Elizabeth L. TI - Negative Patient Descriptors: Documenting Racial Bias In The Electronic Health Record JO - Health Affairs VL - 41 IS - 2 SP - 203 EP - 211 PY - 2022 DA - 2022/01/19 DO - 10.1377/hlthaff.2021.01423 UR - https://doi.org/10.1377/hlthaff.2021.01423 ER -
Add Find Research to Claude or ChatGPT and ask in plain language. The assistant reads this record, finds the papers around it and cites only what it actually found:
Setup takes about a minute: add the connector, then start the free trial.
Record from OpenAlex; open-access location from Unpaywall and the indexes. Find Research links only to legal copies.