Artificial Intelligence Counterfactual Task-Shifting of Hypertension Medication Titration Under Variable Nurse Prescriber Competency Logging: A Doubly Robust Evaluation of Healthcare Workload Equity

Task-shifting hypertension medication titration to nurses is promoted as a remedy for physician shortages, but its workload consequences depend on how nurse prescriber competency is recorded. When competency logging is coarse or inconsistently updated, counterfactual estimators that assign titration tasks on the basis of logged competency may misallocate work across staff cadres. The design assumes a between-cluster standard deviation of 6.0, a residual standard deviation of 4.5 and an intraclass correlation of 0.64; power was estimated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis is methodological and uses no observational data. Under the declared variance structure, 54 clusters are required to detect a 5-percentage-point effect at 80% power. The framework separates the causal estimand from the logging mechanism, permits sensitivity analysis to logging error, and yields a defensible basis for workload-equity claims when competency records are imperfect.