An algorithmic approach to reducing unexplained pain disparities in underserved populations.
Nat Med
; 27(1): 136-140, 2021 01.
Article
em En
| MEDLINE
| ID: mdl-33442014
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
1_ASSA2030
/
2_ODS3
Problema de saúde:
1_acesso_equitativo_servicos
/
2_cobertura_universal
Assunto principal:
Dor
/
Algoritmos
/
Populações Vulneráveis
Tipo de estudo:
Prognostic_studies
Aspecto:
Equity_inequality
/
Patient_preference
Limite:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Nat Med
Assunto da revista:
BIOLOGIA MOLECULAR
/
MEDICINA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos