An algorithmic approach to reducing unexplained pain disparities in underserved populations

Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients’ pain stems from factors extern...

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Vydáno v:	Nature medicine Ročník 27; číslo 1; s. 136 - 140
Hlavní autoři:	Pierson, Emma, Cutler, David M., Leskovec, Jure, Mullainathan, Sendhil, Obermeyer, Ziad
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Nature Publishing Group US 01.01.2021 Nature Publishing Group
Témata:	631/114/1305 706/689 Aged Algorithms Arthritis Biomedical and Life Sciences Biomedical materials Biomedicine Cancer Research Confidence intervals Deep Learning Diagnosis Female Health Status Disparities Humans Infectious Diseases Knee Learning algorithms Machine learning Male Medical imaging Metabolic Diseases Middle Aged Molecular Medicine Neurosciences Osteoarthritis Osteoarthritis, Knee - diagnostic imaging Osteoarthritis, Knee - physiopathology Pain Pain - physiopathology Pain Measurement Patients Physicians Populations Race factors Race Factors - statistics & numerical data Racial differences Severity of Illness Index Socioeconomic Factors Stems Underserved populations Vulnerable Populations - statistics & numerical data United States
ISSN:	1078-8956, 1546-170X, 1546-170X
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Abstract	Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients’ pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients’ experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3–16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2–11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients’ pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm’s ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients’ pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty. An algorithmic, machine-learning approach to measuring severe pain from osteoarthritis applied to X-ray images of knees suggests that reported disparities in knee pain in underserved populations can be reduced by comparison with use of standard radiographic measures of disease severity.
AbstractList	Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients' pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients' experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3-16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2-11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients' pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm's ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients' pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty. An algorithmic, machine-learning approach to measuring severe pain from osteoarthritis applied to X-ray images of knees suggests that reported disparities in knee pain in underserved populations can be reduced by comparison with use of standard radiographic measures of disease severity. Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients’ pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients’ experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3–16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2–11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients’ pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm’s ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients’ pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty. An algorithmic, machine-learning approach to measuring severe pain from osteoarthritis applied to X-ray images of knees suggests that reported disparities in knee pain in underserved populations can be reduced by comparison with use of standard radiographic measures of disease severity. Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients' pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients' experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3-16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2-11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients' pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm's ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients' pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty. Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients' pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients' experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3-16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2-11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients' pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm's ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients' pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients' pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients' experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3-16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2-11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients' pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm's ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients' pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.
Audience	Academic
Author	Pierson, Emma Obermeyer, Ziad Leskovec, Jure Cutler, David M. Mullainathan, Sendhil
Author_xml	– sequence: 1 givenname: Emma surname: Pierson fullname: Pierson, Emma organization: Department of Computer Science, Stanford University, Microsoft Research – sequence: 2 givenname: David M. surname: Cutler fullname: Cutler, David M. organization: Department of Economics, Harvard University – sequence: 3 givenname: Jure orcidid: 0000-0002-5411-923X surname: Leskovec fullname: Leskovec, Jure organization: Department of Computer Science, Stanford University – sequence: 4 givenname: Sendhil orcidid: 0000-0001-8508-4052 surname: Mullainathan fullname: Mullainathan, Sendhil email: sendhil.mullainathan@chicagobooth.edu organization: Booth School of Business, University of Chicago – sequence: 5 givenname: Ziad surname: Obermeyer fullname: Obermeyer, Ziad organization: School of Public Health, University of California at Berkeley
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/33442014$$D View this record in MEDLINE/PubMed
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Copyright	The Author(s), under exclusive licence to Springer Nature America, Inc. 2021 COPYRIGHT 2021 Nature Publishing Group The Author(s), under exclusive licence to Springer Nature America, Inc. 2021.
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Snippet	Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like...
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SubjectTerms	631/114/1305 706/689 Aged Algorithms Arthritis Biomedical and Life Sciences Biomedical materials Biomedicine Cancer Research Confidence intervals Deep Learning Diagnosis Female Health Status Disparities Humans Infectious Diseases Knee Learning algorithms Machine learning Male Medical imaging Metabolic Diseases Middle Aged Molecular Medicine Neurosciences Osteoarthritis Osteoarthritis, Knee - diagnostic imaging Osteoarthritis, Knee - physiopathology Pain Pain - physiopathology Pain Measurement Patients Physicians Populations Race factors Race Factors - statistics & numerical data Racial differences Severity of Illness Index Socioeconomic Factors Stems Underserved populations Vulnerable Populations - statistics & numerical data
Title	An algorithmic approach to reducing unexplained pain disparities in underserved populations
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