Sequencing AI Automation and Data Interoperability in Oncology Using a Scenario-Planning Framework Coupled With Discrete-Event Simulation: Proof-of-Concept Study.

Peter May¹, Sabine D Brookman-May², Edward Garrahy³, Johannes von Büren⁴

¹Department of Medicine III, School of Medicine and Health, Technical University of Munich, Ismaninger Str. 22, Munich, Bavaria, 81675, Germany, 49 89-4400 ext 8753.

²Department of Urology, Ludwig-Maximilians-Universität München, Munich, Bavaria, Germany.

³Wellster Healthtech Group, Munich, Germany.

⁴Sidekick Health Germany GmbH, Hamburg, Germany.

PubMed PMC DOI PDF

Abstract

BACKGROUND: As oncology workflows integrate increasingly autonomous artificial intelligence (AI) agents, health systems face uncertainty regarding operational impacts. Traditional linear forecasting methods fail to capture second-order effects such as governance saturation, induced demand, and bottleneck migration. To navigate this complexity, the emerging field of medical futures studies requires…

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