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

A research team at Stanford University has developed an artificial intelligence system designed to predict patient mortality, aiming to improve the timing of hospice care for critically ill patients. During testing, the AI demonstrated impressive accuracy, correctly predicting death in 90% of cases. However, despite its predictive power, the system lacks transparency—it cannot explain how it reaches its conclusions, which is a critical limitation for medical professionals. Predicting death is a complex task that involves evaluating numerous factors, such as a patient’s age, family medical history, drug responses, and the nature of their illness. Moreover, doctors must also manage their own biases and unconscious assumptions when assessing a patient's condition. While some predictions may be accurate, others can be off by months or even years, making it difficult to determine the right time for hospice care. Both premature and delayed decisions can negatively impact the quality of end-of-life care. This challenge complicates the precise scheduling of hospice services. Typically, when a patient is expected to live less than a year, they are transitioned to hospice care, where the focus shifts from curative treatment to comfort and support. Hospice teams work to manage pain, nausea, and confusion, while offering psychological and spiritual assistance, respecting the cultural and personal needs of both the patient and their family. Yet, if a patient moves to hospice too late, they may miss out on essential care during the final stages of life. Conversely, moving too early could strain the healthcare system unnecessarily. Ken Jung, a medical research scientist at Stanford and co-author of the study, explained to Gizmodo that one of the goals of hospice care is to have meaningful conversations with patients before a crisis occurs. These discussions help patients express their preferences and make informed decisions about their care, even if they don’t pass away immediately. The aim is to ensure that patients receive the best possible support throughout their journey. Jung noted that this unmet need has been recognized for decades. A survey revealed that 80% of Americans wish to die at home, but only 35% actually do so. While progress has been made, he said, “we have a long way to go.” In China, approximately 7 million people reach the end of life each year, yet only 15% of them receive hospice care. According to a report by the Economist Intelligence Unit, China ranks 10th out of 80 countries in the 2015 Death Quality Index, highlighting the growing gap between demand and available resources. To address these challenges, Stanford’s Anand Avati and his team developed an AI-based system to improve the timing of hospice care. Rather than replacing doctors, the algorithm aims to enhance their decision-making by providing more accurate predictions. It also helps reduce the burden on medical staff, who often spend significant time analyzing patient outcomes. The system uses deep learning, a type of AI that trains neural networks on large datasets. In this case, the model was built using electronic health records from adult and pediatric patients at Stanford Hospital and Lucille Packard Children’s Hospital. After analyzing 2 million records, the researchers identified 200,000 patients for the program. The system remains neutral regarding the type of disease, stage, or level of care (e.g., ICU or non-ICU), focusing instead on clinical data such as diagnoses, imaging orders, procedures, length of stay, and medications.

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