Artificial intelligence algorithms can predict patient death accuracy by up to 90%

A team of researchers at Stanford University has developed an AI system capable of predicting patient mortality with up to 90% accuracy. The goal is to enhance the timing of hospice care for critically ill patients, ensuring they receive appropriate support at the right moment. While the system has shown impressive results in tests, it still lacks transparency—doctors are unable to fully understand how the AI arrives at its predictions. Predicting death is a complex process that involves numerous factors, including a patient’s age, medical history, response to treatment, and the nature of their illness. Additionally, doctors must navigate personal biases and emotional challenges when assessing a patient’s condition. Sometimes, predictions can be too early or too late, which can lead to missed opportunities for hospice care or unnecessary strain on the healthcare system. Hospice care aims to provide comfort and dignity during a patient’s final days, managing pain, offering psychological support, and respecting cultural and spiritual needs. However, if patients are referred to hospice too late, they may miss out on critical care, while early referrals can overburden the system. Ken Jung, a researcher at Stanford and co-author of the study, highlighted that even though the AI helps improve prediction, it's not meant to replace doctors but to assist them. “The goal is to have meaningful conversations with patients before a crisis occurs,” he explained. These discussions help patients express their preferences and ensure their wishes are respected, regardless of whether they pass away soon or later. Jung noted that many people want to die at home, yet only a fraction actually do. Although progress has been made, there’s still a long way to go. In China, approximately 7 million people reach the end of life each year, but hospice care meets only about 15% of the demand. According to a report by the Economist Intelligence Unit, China ranks 10th out of 80 countries in the Death Quality Index, highlighting the growing gap between aging populations and available services. To address this challenge, Anand Avati and his team at Stanford created an AI system designed to improve the timing of hospice referrals. Using deep learning algorithms trained on electronic health records from adult and pediatric patients, the system analyzes vast amounts of data, including diagnoses, procedures, medications, and hospital stays. It remains neutral regarding the type or stage of illness, focusing instead on patterns that could indicate a patient’s likelihood of passing. This AI tool is not meant to replace human judgment but to support doctors in making more accurate and timely decisions. By reducing the burden of manual prediction, it allows healthcare professionals to focus on what matters most: providing compassionate, patient-centered care.

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