Chi Mei Medical Center
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2024 National Healthcare Quality Award
Highlights of Workload Reduction
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By minimizing unnecessary tests, this system is expected to reduce blood glucose testing costs by approximately 65.8%.
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By utilizing this predictive system, neonates at high risk for hypoglycemia can be identified more rapidly, enabling early preparation and timely intervention. This reduces both the workload and psychological stress of healthcare providers.
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The predictive system helps improve the quality of neonatal care by reducing the incidence of neonatal hypoglycemia, which in turn enhances clinical outcomes and increases family satisfaction.
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By supporting physicians in making timely and accurate decisions, the system reduces unnecessary blood glucose testing, thereby lowering the frequency of invasive procedures and the risk of needlestick-related infections in neonates.
Early identification of neonates at risk for hypoglycemia can optimize clinical management strategies and improve overall quality of neonatal care. This product is designed to develop a machine learning predictive model and transform it into a clinical prediction application to assist clinicians in accurately predicting the risk of neonatal hypoglycemia within the first four hours after birth.
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Source: 2025 Application of Digital Healthecare