Artificial Intelligence Conformal Inference of Community Clinic Sepsis Triage Under Unmeasured Handoff Documentation Lag: A Healthcare Coverage-Guarantee Safety Evaluation

Conformal prediction offers distribution-free coverage guarantees that could make machine-learning sepsis triage safe for deployment in community clinics, but its guarantees assume exchangeability between calibration and deployment data. Handoff documentation lag, the interval between a clinical event and its entry into the electronic record, is unmeasured in most community settings and can silently break that assumption. The framework treats lag as a latent shift in the calibration distribution and derives a bound on the coverage shortfall as a function of the lag distribution's deviation from exchangeability. A replication-based evaluation of the ['Intercept', 'exposure', 'group', 'severity'] estimator was conducted: 600 data sets of N = 500 were drawn from a declared data-generating process (intercept 12.0, exposure 0.42, group -3.1, severity 1.35, error SD 4.2). The study characterises the behaviour of the ordinary least squares estimator under the stated process. Across 600 replications, empirical coverage of the nominal 95% intervals was 1.0 for both the intercept and the exposure coefficient, estimated bias was -0.01762 for the intercept and 0.00048 for exposure, and RMSE was 0.8725 and 0.01575 respectively. The framework shows that coverage guarantees survive lag only when the lag distribution is bounded and the calibration set is weighted accordingly; otherwise the nominal guarantee is not a safety guarantee. The contribution is a coverage-guarantee safety evaluation that separates what conformal prediction promises from what community clinic deployment can honour.