Monday, November 20, 2023
The clinical applications platform by Philips, empowered with AI, expedites the diagnosis of bone fractures, delivering swift and efficient high-quality care to millions of patients across Norway.
With a persistent shortage of skilled radiologists and an increased level of staff burnout, especially in countries like Norway, prioritizing patients effectively and optimizing time has become crucial for clinicians. Addressing this challenge, clinicians are in need of workflow-enhancing solutions to manage growing workloads amid staffing shortages, ensuring consistent and rapid diagnoses and treatments to improve overall patient outcomes.
The extensive deployment encompasses an AI-based bone fracture radiology application, benefiting approximately half a million individuals across 22 Norwegian municipalities. The agreement, with the potential for a 4-year term extension, has the capacity to provide clinical AI solutions to around 3.8 million people, constituting 70% of Norway's population, across 30 hospitals under the country's major regional healthcare authorities. This marks a substantial and all-encompassing commitment by Philips to implement enterprise-wide AI solutions throughout a healthcare system in Europe.
The initial deployment of the AI application by Philips at Vestre Viken Health Trust hospitals involves the automatic identification of radiography scans lacking immediate evidence of a fracture. This empowers radiologists to focus on more intricate, challenging, and urgent cases. Radiologists retain the authority to accept or reject the results before transmitting them to the hospital's PACS (Picture Archiving and Communication System).
Feedback suggests that the application has exceeded expectations by not only enhancing patient flows and the quality of care but also by identifying fractures that might have been overlooked by doctors. Philips AI Manager extends the range of clinical applications for radiology, encompassing image processing, intelligent algorithms, advanced visualization, and AI. These collectively assist radiologists in overcoming challenges associated with high-volume workloads, facilitating the efficient and precise analysis of both routine and complex cases.