Artificial Epidemiology: How self-evolving clinical AI manufactures disease prevalence from administrative coding artifacts

Self-evolving AI agents in clinical documentation may manufacture distorted disease prevalence at population scale. This paper formalizes the risk as a partially observable decision process (POMDP) where AI observes only administrative state (coded data) while clinical state remains hidden, creating a structural reward asymmetry: administrative rewards are immediately measurable while clinical outcomes are delayed and noisy. We introduce the artificial epidemiology divergence D(C) = P_a(C) - P_c(C), measuring the gap between administrative and clinical prevalence, and propose five experimentally testable markers for detecting population-level distortion. A computational pre-study on synthetic electronic health records (Synthea v3.x, n = 11,475 patients, 415,464 SNOMED-CT coded conditions) operationalizes D(C) for five sentinel conditions. Results show substantial baseline divergence: D(C) = +0.378 for diabetes (4,334 patients coded without supporting laboratory evidence), D(C) = -0.024 for hypertension (1,741 patients with clinical evidence but no code), and D(C) = +0.101 for obesity. Comorbidity co-occurrence ratios exceed expected values by 1.5x to 2.4x across all sentinel condition pairs. A documentation-action gap of 75.7% is observed for coded diabetes (diagnosed but pharmacologically untreated). A complementary governance simulation demonstrates that the reification feedback loop amplifies coding distortion by 17x to 21x over five iterative cycles under business-first and equilibrium governance scenarios. All data are synthetic; these results establish measurement methodology and construct operationalization, not clinical evidence. Validation on real-world EHR data with linked clinical registries is the necessary next step.