日本語 
Osaka Acute Care and General Medical Center, a local independent administrative agency of the Osaka Prefectural Hospital Organization, will introduce “MeDiCU-AI,” developed by MeDiCU Co., Ltd., which assists in determining whether or not ICU patients can be discharged, to its Advanced Emergency and Critical Care Center, ICU, and Pediatric HCU.
Background of the implementation
Osaka Acute Care and General Medical Center has been designated a “core disaster base hospital” by Osaka Prefecture, and in the event of a large-scale disaster, it plays a central role in the concentrated acceptance of critically ill patients and the coordination of wide-area transportation. In particular, in the event of a Nankai Trough mega-earthquake, which is feared to occur in the future, it is anticipated that thousands of critically ill patients will occur in Osaka Prefecture alone. How quickly and appropriately the approximately 50 beds in the intensive care unit can be utilized within limited time and resources will be an extremely important issue that directly impacts the survival rate of the entire region.
Furthermore, not only in the event of a massive disaster like the Nankai Trough earthquake, but also in situations where medical demand has rapidly increased due to emerging or re-emerging infectious disease pandemics, it is necessary to establish a system that efficiently utilizes limited critical care resources and continues to provide them appropriately to patients who truly need intensive care. To achieve this, it is essential to maintain the capacity of intensive care unit beds by monitoring changes in the condition of critically ill patients in real time and safely transferring patients whose condition has stabilized to general wards.
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However, traditionally, understanding bed occupancy and patient conditions in intensive care units has largely relied on the experience and verbal communication between doctors and nurses. There has been a lack of a system to visualize and predict in real time “which beds will become available and when” and “which patients are nearing the point where they can be transferred.” This issue not only affects bed control and operational efficiency during normal times, but also hinders the rapid assessment of the number of patients that can be accepted during emergencies, potentially leading to delays in coordinating inter-ward transfers.
The MeDiCU-AI system being introduced supports decision-making regarding critical care patient management and hospital bed utilization by visualizing risk scores in real time based on statistical information of patients who have received treatment in the past. In addition to improving bed control for critically ill patients in daily practice, it is expected to contribute to building a system that can “accept as many critically ill patients as possible” by making the most effective use of limited critical care bed resources during large-scale disasters and pandemics.
By utilizing an AI system that comprehensively visualizes the condition of ICU patients, we aim to build a seamless critical care support system from peacetime to emergencies, and contribute to realizing a medical system that can save as many critically ill patients as possible by making the most effective use of limited critical care resources even during large-scale disasters.
SOURCE: PRTimes
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