Evaluating sociodemographic bias in a deployed machine-learned patient deterioration model
Bibliographic record
Abstract
Abstract Background Bias evaluations of machine learning (ML) models often focus on performance in research settings, with limited assessment of downstream bias following clinical deployment. The objective of this study was to evaluate whether CHARTwatch, a real-time ML early warning system for inpatient deterioration, demonstrated algorithmic bias in model performance, or produced disparities in care processes, and outcomes across patient sociodemographic groups. Methods We evaluated CHARTwatch implementation on the internal medicine service at a large academic hospital. Patient outcomes during the intervention period (November 1, 2020–June 1, 2022) were compared to the control period (November 1, 2016–December 31, 2019) using propensity score overlap weighting. We evaluated differences across key sociodemographic subgroups, including age, sex, homelessness, and neighborhood-level socioeconomic and racialized composition. Outcomes included model performance (sensitivity and specificity), processes of care, and patient outcomes (non-palliative in-hospital death). Results Among 12 877 patients (9079 control, 3798 intervention), 13.3% were experiencing homelessness and 36.9% lived in the quintile with the highest neighborhood racialized and newcomer populations. Model sensitivity was 70.1% overall, with no significant variation across subgroups. Model specificity varied by age, <60 years: 93% (95% Confidence Interval [CI] 91-95%), 60-80 years: 90% (95%CI 87-92%), and >80 years: 84% (95%CI 79-88%), P < .001, but not other subgroups. CHARTwatch implementation was associated with an increase in code status documentation among patients experiencing homelessness, without significant differences in other care processes or outcomes. Conclusion CHARTwatch model performance and impact were generally consistent across measured sociodemographic subgroups. ML-based clinical decision support tools, and associated standardization of care, may reduce existing inequities, as was observed for code status orders among patients experiencing homelessness. This evaluation provides a framework for future bias assessments of deployed ML-CDS tools.
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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 | 0.037 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".