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#15 - When Algorithms Know Your End-Of-Life Wishes Better Than Loved Ones
What if the person who knows you best isn’t the best person to speak for you when it matters most?
We explore a study that tested just that—comparing the CPR preferences predicted by loved ones with those predicted by machine learning. The result? Algorithms got it right more often. That surprising outcome raises tough, important questions: Why do partners misjudge? And could AI really support life-and-death decisions when seconds count?
We unpack the study’s approach in everyday terms: who was surveyed, what data fueled the models, and how three algorithms were trained using demographics, clinical records, and stated values. The twist? Basic details like age and sex turned out to be stronger predictors than deeply personal values or medical history. That finding sparks a deeper conversation about autonomy, identity, and the tension between individual dignity and data-driven generalizations.
We also dig into the practical side: advance directives, POLST forms, and the true role of a healthcare proxy. Rather than replacing human decision-makers, we imagine a partner-in-the-loop model—where AI offers guidance, not verdicts, and transparency is key. Because when emergencies hit, it's not just about having a plan—it's about making sure your voice is heard.
If this resonates, take one step today: name your proxy, talk to your doctor, and share your wishes. Then subscribe, send this episode to someone who needs it, and leave a review to help keep these critical conversations alive.
Reference:
Machine Learning–Based Patient Preference Prediction: A Proof of Concept
Georg Starke, et al.
NEJM AI (2025)
Credits:
Theme music: Nowhere Land, Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0
https://creativecommons.org/licenses/by/4.0/
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By Vasanth Sarathy & Laura HagopianWhat if the person who knows you best isn’t the best person to speak for you when it matters most?
We explore a study that tested just that—comparing the CPR preferences predicted by loved ones with those predicted by machine learning. The result? Algorithms got it right more often. That surprising outcome raises tough, important questions: Why do partners misjudge? And could AI really support life-and-death decisions when seconds count?
We unpack the study’s approach in everyday terms: who was surveyed, what data fueled the models, and how three algorithms were trained using demographics, clinical records, and stated values. The twist? Basic details like age and sex turned out to be stronger predictors than deeply personal values or medical history. That finding sparks a deeper conversation about autonomy, identity, and the tension between individual dignity and data-driven generalizations.
We also dig into the practical side: advance directives, POLST forms, and the true role of a healthcare proxy. Rather than replacing human decision-makers, we imagine a partner-in-the-loop model—where AI offers guidance, not verdicts, and transparency is key. Because when emergencies hit, it's not just about having a plan—it's about making sure your voice is heard.
If this resonates, take one step today: name your proxy, talk to your doctor, and share your wishes. Then subscribe, send this episode to someone who needs it, and leave a review to help keep these critical conversations alive.
Reference:
Machine Learning–Based Patient Preference Prediction: A Proof of Concept
Georg Starke, et al.
NEJM AI (2025)
Credits:
Theme music: Nowhere Land, Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0
https://creativecommons.org/licenses/by/4.0/