An Agent-Based Modeling Framework for Healthcare AI Adoption: Application to Ambient Clinical Documentation

Objective: To develop and demonstrate an agent-based modeling framework for healthcare AI adoption, using ambient clinical documentation as the calibration case. Materials and Methods: We built an agent-based model with 50,000 clinician agents, 500 organization agents, and 4 vendor agents over 104 weeks. Modeled clinicians differed by psychotype, specialty, and friction/benefit thresholds; modeled organizations progressed through deployment phases with governance delays anchored to 8-28 weeks. All cited deployment values were independently re-verified against primary sources, and the model was validated against published data from seven health systems and three benchmarks using formal goodness-of-fit metrics (RMSE, 90% predictive-interval coverage) grouped by reference class. After correcting an organization-initialization artifact, we performed a formal six-parameter re-calibration to align both the early-time trajectory and the steady-state plateau with published data. Six intervention scenarios were compared in paired simulations (n=30 realizations per scenario) using effect sizes with bootstrap intervals, and both the full intervention comparison and the Sobol sensitivity screen were re-run natively under the re-calibrated model. Global sensitivity analysis used Sobol indices (64 base samples; 1,152 parameter sets) across eight parameters. Results: Baseline simulations produced S-curve adoption trajectories with wide variability. The re-calibrated model reproduced both early-time single-site trajectories and the cross-sectional adoption plateau, covering 86% of reference-class-matched anchors at the nominal 90% level, versus 29% for the original configuration. Most intervention scenarios increased adoption; in the original configuration the combined intervention outperformed individual levers, with significant interactions confirmed by 23 factorial analysis. Re-running the analyses natively under the calibrated model both confirmed and revised these conclusions: governance remained the largest single structural lever and non-success absorbing states remained prominent, but intervention effects attenuated sharply, the combined intervention no longer reliably exceeded the best single lever at operating scale, and the leading sensitivity driver shifted from governance delay to clinician friction/edit-rate tolerance. That calibration changes which levers appear influential is itself the central methodological finding. Organizational outcomes clustered into non-success absorbing states (pilot stagnation and failure) alongside success and scaling. Conclusions: Governance delay is an explicit upstream gate in the model, so its influence reflects model architecture and should not be interpreted as a universal real-world priority. The modeled pilot stagnation state is hypothesis-generating rather than an empirical category. Agent-based modeling provides a structured framework for understanding healthcare AI adoption dynamics. The approach supports hypothesis generation and comparative scenario exploration rather than point prediction.