External Validation as Multi-Environment Falsification for Clinically Transportable Prediction under Adaptive Hospital Systems

Abstract

Hospitals now generate dense digital traces from perioperative care, ward monitoring, laboratory information systems, imaging workflows, discharge coding, and longitudinal follow-up platforms. These resources have made it possible to develop increasingly sophisticated models for diagnosis, prognosis, triage, and complication surveillance. At the same time, healthcare delivery has become more heterogeneous rather than less so. Institutions differ in admission pathways, documentation intensity, measurement cadence, intervention timing, escalation norms, and outcome ascertainment. This paper examines the consequences of that heterogeneity for translation. The central claim is that generalizability becomes a translational bottleneck because retrospective model development rarely identifies whether the learned risk surface reflects durable clinical regularity or context-bound structure generated by local care processes. The analysis develops a framework in which transportable prediction is treated as an identifiability problem under adaptive clinical environments. In this setting, internal validation is shown to be non-falsifying with respect to contextual overfitting, because repeated sampling within one environment does not test whether the score survives altered observation, altered treatment response, or altered labeling. External validation is therefore reinterpreted as multi-environment falsification rather than a routine add-on. The paper further introduces shadow deployment as an intermediate evidentiary stage between retrospective modeling and action-linked implementation, and it argues that translationally credible prediction requires environment-specific calibration analysis, threshold stress testing, and sequential monitoring under live data streams. The result is a stricter but more operationally meaningful account of what it takes for a clinical model to become usable outside the site that created it.