The Relative Indirect Effects of Technology Bias and Implicit Bias on Racial Disparity in Service Delivery and Sepsis Mortality
Bibliographic record
Abstract
Racism at the individual and societal levels has been identified as a cause of disparity in healthcare outcomes in the United States but reducing disparity has been slow. This study disentangles the relative effects of two types of racism on inpatient service delivery and hospital mortality: technology bias and implicit bias. Drawing on clinical data from intensive care unit (ICU) patients with sepsis, we use propensity score matching to balance groups of White and nonwhite patients and run a causal mediation analysis to test our model, which links patient race to hospital mortality through two mediating variables related to service delivery: (1) discrepancies in blood oxygen saturation measurements due to technology bias embedded in the medical device (i.e., pulse oximeter) and (2) administration of supplemental oxygen, which could be impacted by clinicians’ implicit bias. We first replicate prior findings that (a) higher discrepancies between oximeter readings and laboratory tests for nonwhite patients compared to Whites and (b) a higher discrepancy lowers the likelihood of patients receiving supplemental oxygen during the ICU stay. We make a unique contribution by finding that nonwhite patients with sepsis have a 79% higher risk of hospital mortality in the ICU compared to Whites, with nearly half of the racial disparity in mortality stemming from technology bias and less than a fifth from clinicians’ implicit bias. Eliminating these two biases would help save thousands of lives annually among racial/ethnic minorities with sepsis in the United States. Our findings indicate that technology bias exerts a greater negative impact on mortality than does implicit bias, highlighting the importance of device approval standards and clinicians' ability to customize decision criteria for supplemental oxygen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".