“Patients may generally look well, but if their risk of death is high based on the AI model, then the clinician may reassess – should they be assigned a higher priority level and be seen within a shorter period of time,” Wong added.
Wong’s study offered an encouraging picture of how this approach could improve resource management and patient care in the emergency department. Her study, conducted as part of her third-year training, also showed that PAC+ performed better than PACS alone in identifying critically ill patients at high risk of death, and could safely identifying low risk patients who may be suitable for discharge and potentially be referred to primary or urgent care settings.
“In a retrospective study conducted using PAC+, almost half the number of patients could be safely down-triaged from higher (P1-P2) to lower priority levels (P3-P4). This could help to ease the emergency department’s caseload, allowing more resources to be diverted to looking after critically ill patients,” explained Wong.
The PAC+ team hopes that their model can help identify patients who may be suitable for discharge, referred to primary care, or admitted under Mobile Inpatient Care @ Home (MIC@Home), a programme that provides hospital-level care to patients in their homes.
Associate Professor Christopher Laing, Vice-Dean for Innovation and Entrepreneurship at Duke-NUS, said: “Artificial intelligence is the future of medicine, with immense potential to solve complex clinical problems. Our scientists and students are working with industry partners and entrepreneurs to bring new AI technologies to medical practice, where they can make a meaningful impact to patients.”
Duke-NUS commercialisation impact has included therapeutics, vaccines, diagnostics, medical devices, and digital health technologies, developed to combat our society’s greatest emerging health challenges.