Probabilistic Service Measures and Numerical Planning Improvement
Abstract
Service-resource allocation and treatment-plan improvement probability both need explicit denominators and horizons. Their outcomes arise from different mechanisms and should not be pooled into one success percentage. Independent checks can validate the modeled comparisons. A broader service or clinical benefit requires corresponding field evidence beyond the numerical measure supplied by either model. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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