Optimising antibiotic switching via forecasting of patient physiology

Abstract Timely transition from intravenous to oral antibiotic therapy shortens hospital stays, reduces catheter-related infections, and lowers healthcare costs, yet one in five patients in England remain on intravenous antibiotics despite meeting switching criteria. Clinical decision support systems can improve switching rates, but approaches that learn from historical decisions reproduce the delays and inconsistencies of routine practice. We propose using neural processes to model vital sign trajectories probabilistically, predicting switch-readiness by comparing forecasts against clinical guidelines rather than learning from past actions, and ranking patients to prioritise clinical review. The design yields interpretable outputs, adapts to updated guidelines without retraining, and preserves clinical judgement. Validated on datasets from US intensive care (6333 encounters) and a UK academic hospital group (10,584 encounters), the system selects 2.2–3.2 × more relevant patients than random. We show that forecasting patient physiology offers a principled foundation for decision support in antibiotic stewardship.