Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer.
<h4>Purpose</h4>Implicit, unconscious biases in medicine are personal attitudes about race, ethnicity, gender, and other characteristics that may lead to discriminatory patterns of care. However, there is no consensus on whether implicit bias represents a true predictor of differential c...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0314989 |
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| author | Ashwin Ramaswamy Michael Hung Joe Pelt Parsa Iranmahboub Lina P Calderon Ian S Scherr Gerald Wang David Green Neal Patel Timothy D McClure Christopher Barbieri Jim C Hu Charlotta Lindvall Douglas S Scherr |
| author_facet | Ashwin Ramaswamy Michael Hung Joe Pelt Parsa Iranmahboub Lina P Calderon Ian S Scherr Gerald Wang David Green Neal Patel Timothy D McClure Christopher Barbieri Jim C Hu Charlotta Lindvall Douglas S Scherr |
| author_sort | Ashwin Ramaswamy |
| collection | DOAJ |
| description | <h4>Purpose</h4>Implicit, unconscious biases in medicine are personal attitudes about race, ethnicity, gender, and other characteristics that may lead to discriminatory patterns of care. However, there is no consensus on whether implicit bias represents a true predictor of differential care given an absence of real-world studies. We conducted the first real-world pilot study of provider implicit bias by evaluating treatment parity in prostate cancer using unstructured data-the most common way providers document granular details of the patient encounter.<h4>Methods and findings</h4>Patients ≥18 years with a diagnosis of very-low to favorable intermediate-risk prostate cancer followed by 3 urologic oncologists from 2010 through 2021. The race Implicit Association Test was administered to all providers. Natural language processing screened human annotation using validated regex ontologies evaluated each provider's care on four prostate cancer quality indicators: (1) active surveillance utilization; (2) molecular biomarker discussion; (3) urinary function evaluation; and (4) sexual function evaluation. The chi-squared test and phi coefficient were utilized to respectively measure the statistical significance and the strength of association between race and four quality indicators. 1,094 patients were included. While Providers A and B demonstrated no preference on the race Implicit Association Test, Provider C showed preference for White patients. Provider C recommended active surveillance (p<0.01, φ = 0.175) and considered biomarkers (p = 0.047, φ = 0.127) more often in White men than expected, suggestive of treatment imparity. Provider A considered biomarkers (p<0.01, φ = 0.179) more often in White men than expected. Provider B demonstrated treatment parity in all evaluated quality indicators (p>0.05).<h4>Conclusions</h4>In this pilot study, providers' practice patterns were associated with both patient race and implicit racial preferences in prostate cancer. Alerting providers of existing implicit bias may restore parity, however future assessments are needed to validate this concept. |
| format | Article |
| id | doaj-art-16f3a3d0d3434966a43dbc0e3e786988 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-16f3a3d0d3434966a43dbc0e3e7869882025-01-17T05:31:56ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031498910.1371/journal.pone.0314989Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer.Ashwin RamaswamyMichael HungJoe PeltParsa IranmahboubLina P CalderonIan S ScherrGerald WangDavid GreenNeal PatelTimothy D McClureChristopher BarbieriJim C HuCharlotta LindvallDouglas S Scherr<h4>Purpose</h4>Implicit, unconscious biases in medicine are personal attitudes about race, ethnicity, gender, and other characteristics that may lead to discriminatory patterns of care. However, there is no consensus on whether implicit bias represents a true predictor of differential care given an absence of real-world studies. We conducted the first real-world pilot study of provider implicit bias by evaluating treatment parity in prostate cancer using unstructured data-the most common way providers document granular details of the patient encounter.<h4>Methods and findings</h4>Patients ≥18 years with a diagnosis of very-low to favorable intermediate-risk prostate cancer followed by 3 urologic oncologists from 2010 through 2021. The race Implicit Association Test was administered to all providers. Natural language processing screened human annotation using validated regex ontologies evaluated each provider's care on four prostate cancer quality indicators: (1) active surveillance utilization; (2) molecular biomarker discussion; (3) urinary function evaluation; and (4) sexual function evaluation. The chi-squared test and phi coefficient were utilized to respectively measure the statistical significance and the strength of association between race and four quality indicators. 1,094 patients were included. While Providers A and B demonstrated no preference on the race Implicit Association Test, Provider C showed preference for White patients. Provider C recommended active surveillance (p<0.01, φ = 0.175) and considered biomarkers (p = 0.047, φ = 0.127) more often in White men than expected, suggestive of treatment imparity. Provider A considered biomarkers (p<0.01, φ = 0.179) more often in White men than expected. Provider B demonstrated treatment parity in all evaluated quality indicators (p>0.05).<h4>Conclusions</h4>In this pilot study, providers' practice patterns were associated with both patient race and implicit racial preferences in prostate cancer. Alerting providers of existing implicit bias may restore parity, however future assessments are needed to validate this concept.https://doi.org/10.1371/journal.pone.0314989 |
| spellingShingle | Ashwin Ramaswamy Michael Hung Joe Pelt Parsa Iranmahboub Lina P Calderon Ian S Scherr Gerald Wang David Green Neal Patel Timothy D McClure Christopher Barbieri Jim C Hu Charlotta Lindvall Douglas S Scherr Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer. PLoS ONE |
| title | Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer. |
| title_full | Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer. |
| title_fullStr | Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer. |
| title_full_unstemmed | Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer. |
| title_short | Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer. |
| title_sort | ascertaining provider level implicit bias in electronic health records with rules based natural language processing a pilot study in the case of prostate cancer |
| url | https://doi.org/10.1371/journal.pone.0314989 |
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