The Intensive Care Unit (ICU) represents the highest level of acuity within the hospital system. After discharge from the ICU, patients are sent to recover on “step-down wards” or “high-dependency units”. While on these acute wards, there is a substantial risk of avoidable deterioration in the patient’s condition. Adverse outcomes of this kind are approximately similar in number to the annual mortality due to road-traffic accidents, and represent one of the largest sources of avoidable death in modern healthcare systems.
Our on-going projects in acute care, in collaboration with world-leading intensive care clinicians at Oxford University Hospitals, aim to produce predictive AI-based systems using the huge quantities of data now routinely collected for each patient. The Hospital Alerting via Electronic Noticeboard (HAVEN) project, a collaboration between the CHI Lab, BSP-ML group and the Kadoorie Critical Care Research group at the John Radcliffe Hospital, has developed an algorithm for the early detection of reversible deterioration in patients at risk of ICU admission. The HAVEN algorithm has been shown to be substantially superior both to the national early warning score and alternative international algorithms.
Other outcomes from this theme of research include predictive systems to better improve the operation of the hospital, including predictively allocating beds and tests for patients.