When Clinical Risk Scores Compete for Limited Review Slots
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Abstract
Clinical risk scores can guide follow-up when care teams have fewer review slots than eligible patients. However, patients with noisier or systematically delayed inputs may be overrepresented or underrepresented at the top of a common ranking. We evaluated three policies in a paired Monte Carlo simulation: raw score ranking, ranking that accounts for group-specific measurement variance, and variance-aware ranking with a minimum allocation to the noisier group. Each of 160 synthetic cohorts included 4,000 candidates. Policies were evaluated under four measurement conditions at review capacities of 10%, 15%, and 25%. At 15% capacity, variance-aware ranking captured 1.42 additional events per 1,000 candidates under unequal but unbiased noise and 5.00 under upward bias. Under downward bias it captured 0.54 fewer events per 1,000 than raw ranking. Adding a 30% minimum allocation in the downward-bias scenario increased the noisier group’s mean event recall from 20.85% to 24.84% and recovered 0.32 events per 1,000 relative to variance-aware ranking alone. The results show how measurement variance and directional bias can change the consequences of allocating a fixed number of review slots. The experiment used synthetic outcomes and estimates policy behavior under specified assumptions.
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