The Role of Decision Support Systems in Attenuating Racial Biases in Healthcare Delivery
Bibliographic record
Abstract
Although significant research has examined how technology can intensify racial and other outgroup biases, limited work has investigated the role information systems can play in abating them. Racial biases are particularly worrisome in healthcare, where underrepresented minorities suffer disparities in access to care, quality of care, and clinical outcomes. In this paper, we examine the role clinical decision support systems (CDSS) play in attenuating systematic biases among black patients, relative to white patients, in rates of amputation and revascularization stemming from diabetes mellitus. Using a panel of inpatient data and a difference-in-difference approach, results suggest that CDSS adoption significantly shrinks disparities in amputation rates across white and black patients—with no evidence that this change is simply delaying eventual amputations. Results suggest that this effect is driven by changes in treatment care protocols that match patients to appropriate specialists, rather than altering within physician decision making. These findings highlight the role information systems and digitized patient care can play in promoting unbiased decision making by structuring and standardizing care procedures. This paper was accepted by Stefan Scholtes, healthcare management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".