The authorization lottery: contradictory AI prioritization patterns in healthcare resource allocation

Abstract

Abstract Healthcare systems increasingly deploy artificial intelligence to allocate resources, including procedure authorizations that impact patient access to care. While concerns about algorithmic bias typically focus on representation of protected attributes, how AI systems approach resource-constrained decisions remains understudied. We evaluated three large language models (LLMs), ChatGPT, Claude, and DeepSeek, on their handling of simulated surgical authorization request for an identical procedure. Each model assessed 6,500 surgeon profiles while implementing a mandated 30% denial rate, mirroring real-world authorization constraints. Multivariate regression analysis quantified how each model weighted 13 standardized attributes including professional qualifications and demographic characteristics. ChatGPT assigned significantly lower authorization scores to female surgeons (-9.55 points; 95% CI: -9.98, -9.11) while Claude (+ 2.01 points; 95% CI: + 1.85, + 2.17) and DeepSeek (+ 4.03 points; 95% CI: + 3.91, + 4.15) assigned higher scores to female surgeons. Geographic biases existed, with ChatGPT heavily favoring North American surgeons (+ 18.83 points; 95% CI: + 18.00, + 19.65) and DeepSeek penalizing them (-3.95 points; 95% CI: -4.18, -3.72). In ChatGPT, demographic factors frequently outweighed clinical qualifications; geographic location impacted authorization scores more than board certification. Though all models showed high internal consistency (R 2 values 0.822–0.929), variability in prioritization of attributes resulted in divergent approval thresholds despite identical denial rates (ChatGPT: 64.6 ± 21.1, Claude: 68.5 ± 9.1, DeepSeek: 89.4 ± 9.2). We describe a phenomenon in AI healthcare decision-making which we term "constrained-resource divergence." When forced to discriminate between identical cases under resource constraints, AI systems may apply arbitrary weights that can impact patient care without clinical justification. In practice, this means patients with identical presentations may receive different authorization decisions based on which AI model their insurer deployed. Our findings raise profound questions about AI reliability for consequential healthcare decisions.

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