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paihc [2019/02/26 18:19] nigam [Our efforts] |
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- | ====== The idea ====== | + | ~~REDIRECT> |
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- | Consider the case of Vera, a 60 year old woman of Asian descent with a history of hypertension and asthma, entering the doctor' | + | |
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- | Imagine how Vera’s experience would change if we could predict risks of specific events and take proactive action. | + | |
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- | ====== Our efforts ====== | + | |
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- | We are working on a set of efforts, which are collectively referred to as the Stanford Medicine Program for Artificial Intelligence in Healthcare, with the mission of bringing AI technologies to the clinic, safely, cost-effectively and ethically. {{ :: | + | |
- | - Training: We pair informatics experts with clinician domain experts who provide guidance on the clinical workflow, and input on “what would they do differently” if they had a prediction in hand. | + | |
- | - Human in the loop implementations: | + | |
- | - Utility assessment: Clinical utility of making a prediction, and taking actions depends on factors beyond model accuracy, such as lead time offered by the prediction, the existence of a mitigating action, the cost and ease of intervening, | + | |
- | - Safety, ethics, and health system effects: We map the multiple groups involved in executing a prediction-action loop and study their varying perspectives, | + | |
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- | If done right, the adoption of efficacious prediction-action loops can massively improve the ability of a health system to find patients at risk and act early. As part of this effort, we search for clinical situations where the application of risk-stratification and proactive action can provide cost-effective, | + | |
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