National Healthcare Quality and Disparities Report
Latest available findings on quality of and access to health care
Data
- Data Infographics
- Data Visualizations
- Data Tools
- Data Innovations
- All-Payer Claims Database
- Healthcare Cost and Utilization Project (HCUP)
- Medical Expenditure Panel Survey (MEPS)
- AHRQ Quality Indicator Tools for Data Analytics
- State Snapshots
- United States Health Information Knowledgebase (USHIK)
- Data Sources Available from AHRQ
Search All Research Studies
AHRQ Research Studies Date
AHRQ Research Studies
Sign up: AHRQ Research Studies Email updates
Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 1 of 1 Research Studies DisplayedThompson HM, Sharma B, Bhalla S
Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups.
The objective of this study was to assess fairness and bias of a previously validated machine learning opioid misuse classifier. Two experiments were conducted with the classifier's original and external validation datasets from 2 health systems. Bias was assessed via testing for differences in type II error rates across racial/ethnic subgroups (Black, Hispanic/Latinx, White, Other) using bootstrapped 95% confidence intervals. The investigators concluded that standardized, transparent bias assessments were needed to improve trustworthiness in clinical machine learning models.
AHRQ-funded; HS026385.
Citation: Thompson HM, Sharma B, Bhalla S .
Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups.
J Am Med Inform Assoc 2021 Oct 12;28(11):2393-403. doi: 10.1093/jamia/ocab148..
Keywords: Opioids, Substance Abuse, Electronic Health Records (EHRs), Health Information Technology (HIT), Racial and Ethnic Minorities