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paihc [2020/07/01 16:42]
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paihc [2020/09/21 10:53]
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| - **Implementation**: We partner with analytics and IT teams at Stanford Hospital to deploy predictive models in care delivery workflows. See our [[http://stanmed.stanford.edu/2018summer/artificial-intelligence-puts-humanity-health-care.html|first effort]] in improving palliative care, and recent [[https://www.statnews.com/2020/07/01/end-of-life-artificial-intelligence/|coverage in Statnews]]. | - **Implementation**: We partner with analytics and IT teams at Stanford Hospital to deploy predictive models in care delivery workflows. See our [[http://stanmed.stanford.edu/2018summer/artificial-intelligence-puts-humanity-health-care.html|first effort]] in improving palliative care, and recent [[https://www.statnews.com/2020/07/01/end-of-life-artificial-intelligence/|coverage in Statnews]]. |
| - **Rethinking Utility**: Typically, we evaluate models, quantify potential net-benefit, and realize some of it with real world operational constraints. 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, the logistics of the intervention, and incentives of the providers. Check out our viewpoints on [[https://jamanetwork.com/journals/jama/fullarticle/2748179?guestAccessKey=8cef0271-616d-4e8e-852a-0fddaa0e5101|making machine learning models clinical useful]], [[ https://www.nature.com/articles/s41591-019-0651-8| estimating the hidden deployment cost of predictive models]], and [[https://innovations.bmj.com/content/6/2/45 | bridging the implementation gap of machine learning in healthcare]]. | - **Rethinking Utility**: Typically, we evaluate models, quantify potential net-benefit, and realize some of it with real world operational constraints. 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, the logistics of the intervention, and incentives of the providers. Check out our viewpoints on [[https://jamanetwork.com/journals/jama/fullarticle/2748179?guestAccessKey=8cef0271-616d-4e8e-852a-0fddaa0e5101|making machine learning models clinical useful]], [[https://www.nature.com/articles/s41591-019-0651-8| estimating the hidden deployment cost of predictive models]], and [[https://innovations.bmj.com/content/6/2/45| bridging the implementation gap of machine learning in healthcare]]. |
| - **Safety, ethics, and health system effects**: We map the multiple groups involved in executing a prediction-action pair and study their varying perspectives, positions, stakes, and commitments to pre-empt ethical challenges. We believe that the use of AI can lead to good decisions if we keep human intelligence in the loop. Read our perspective on [[https://www.nejm.org/doi/full/10.1056/NEJMp1714229|addressing ethical challenges]]. | - **Safety, ethics, and health system effects**: We map the multiple groups involved in executing a prediction-action pair and study their varying perspectives, positions, stakes, and commitments to pre-empt ethical challenges. We believe that the use of AI can lead to good decisions if we keep human intelligence in the loop. Read our perspective on [[https://www.nejm.org/doi/full/10.1056/NEJMp1714229|addressing ethical challenges]]. |
| - **Training and Partnerships**: We partner with multiple groups to figure out “what would we do differently” if we had a prediction from a model, and to investigate the pros and cons of using AI to guide care. For example, we [[https://www.researchgate.net/publication/341829909_The_accuracy_vs_coverage_trade-off_in_patient-facing_diagnosis_models | examined the accuracy vs coverage trade off in patient facing diagnosis models]], and partnered with Google on efforts to enable [[https://www.nature.com/articles/s41746-018-0029-1| scalable and accurate deep learning with electronic health records]]. | - **Training and Partnerships**: We partner with multiple groups to figure out “what would we do differently” if we had a prediction from a model, and to investigate the pros and cons of using AI to guide care. For example, we [[https://www.researchgate.net/publication/341829909_The_accuracy_vs_coverage_trade-off_in_patient-facing_diagnosis_models|examined the accuracy vs coverage trade off in patient facing diagnosis models]], and partnered with Google on efforts to enable [[https://www.nature.com/articles/s41746-018-0029-1| scalable and accurate deep learning with electronic health records]]. |
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| If done right, the adoption of efficacious prediction-action pairs 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, health system level benefits. | If done right, the adoption of efficacious prediction-action pairs 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, health system level benefits. |
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