New AI Tool Enables Early Identification of Patients at Risk of Prolonged Hospital Stays
The Accelerated Capability Environment (ACE) has collaborated with the NHS AI Lab to develop a groundbreaking artificial intelligence (AI) tool that can identify patients who are at risk of prolonged hospital stays. This tool utilizes AI technology to analyze hospital data and predict which patients are likely to experience extended stays.
Prolonged hospital stays can have negative consequences such as higher mortality rates, increased risks of readmission, and physical decline, especially among elderly patients. In fact, Gloucestershire Hospitals NHS Foundation Trust discovered that 4% of all admissions to their trust resulted in stays lasting 21 days or longer, constituting 34% of all bed stays.
By identifying these high-risk patients early on, healthcare professionals can adjust treatment plans and take proactive measures to prevent prolonged hospital stays. Professor Peter Brindle, strategic engagement lead at ACE, emphasized the importance of this tool, stating that it not only identifies individuals at risk but also identifies the specific factors contributing to this risk.
ACE collaborated with Polygeist to develop a long stay stratification tool. The AI model was trained on 460,000 anonymized records and utilized information available from initial patient data collection for its analysis. Remarkably, ACE and NHS AI Lab were able to deliver a proof of concept within just 12 weeks.
During trials conducted by Gloucestershire Hospitals NHS Foundation Trust, the tool successfully detected 66% of long stayers in the highest risk categories. Beyond improving patient outcomes, the tool also delivered significant financial savings. Even a mere reduction of one day in the average hospital stay can result in savings of £1.7 million for the trust.
Following the successful proof of concept, the tool was integrated with the trust’s electronic health record system using application programming interfaces (APIs). This integration allows for seamless implementation and real-time monitoring of high-risk patients.
The development of this AI tool represents a tremendous advancement in healthcare technology. Its early identification of patients at risk of prolonged hospital stays not only improves patient outcomes but also reduces the strain on healthcare facilities. By harnessing the power of AI, healthcare professionals can optimize patient care and allocate resources more effectively.
Sources:
– ACE (Accelerated Capability Environment)
– NHS AI Lab
– Gloucestershire Hospitals NHS Foundation Trust
– Polygeist
– NHS Cheshire and Merseyside integrated care board
– C2-Ai
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