During winter, hospital emergency departments are often overfull with very sick patients and teams may feel pressured to discharge people to create capacity for new admissions. This creates risk for patients, especially for frail and vulnerable people, and those who do not speak English.

A tool called National Early Warning Score 2 (NEWS2) is already used to help identify who needs further urgent care. It can also help clinicians understand who may be safe for outpatient care rather than admission, or who may be very sick and need intensive care.

However, NEWS2 accurately identifies only 1 in 4 people who need intensive care and is not fully checked for deciding who does NOT need a bed, even though it is widely used for this purpose. Other kinds of patient information, such as age, prescriptions and blood tests and diagnosis can be used to help decide about patient care but this requires too much time in an emergency situations.

This study will use artificial intelligence models, incorporating all this available data for each patient, to identify:

  • Whether certain groups of patients can be prioritised for admission to specific wards very early on after their arrival in the Emergency Department
  • Which patients are at risk of imminent deterioration that has not been picked up by NEWS2
  • Potentially unsafe discharges or outpatient care decisions in respect of early readmissions
  • The extent to which vulnerable patient groups are more significantly affected by winter pressures such as trolley waits, inappropriate discharges and undetected deterioration

This new information will help clinicians to more easily identify high risk patients that current approaches cannot.