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 ====== Stanford Medicine Program for Artificial Intelligence in Healthcare ====== ====== Stanford Medicine Program for Artificial Intelligence in Healthcare ======
  
-Consider the case of Vera, a 60 year old woman of Asian descent with a history of hypertension and asthma, entering the doctor'​s office with symptoms consistent with a diagnosis of pneumonia. Her primary care physician must diagnose and treat the acute illness, but also manage risk for chronic diseases such as heart attack, stroke, renal failure, and osteoporosis. Ideally, treatment decisions for Vera are guided by risk stratification to decide if to treat, and evidence based selection of how to treat. However, without accurate risk-stratification to decide who to treat, and when, Vera's care remains reactive and suboptimal. Imagine how Vera’s experience would change if we could predict risks of specific events and take proactive action. +We are working on a set of efforts collectively referred to as the [[https://​stanfordhealthcare.org/​stanford-health-now/​ceo-report/​advancing-precision-health-takes-real-smarts.html|Stanford Medicine Program for Artificial Intelligence in Healthcare]],​ with the mission of bringing AI technologies to the clinic, safely, cost-effectively and ethically. {{:​shc_program_for_ai_in_healthcare.pdf|See brochure}}. The four key components are:
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-We are working on a set of efforts, which are collectively referred to as the [[https://​stanfordhealthcare.org/​stanford-health-now/​ceo-report/​advancing-precision-health-takes-real-smarts.html|Stanford Medicine Program for Artificial Intelligence in Healthcare]],​ with the mission of bringing AI technologies to the clinic, safely, cost-effectively and ethically. {{:​shc_program_for_ai_in_healthcare.pdf|See brochure}}. The four key components are:+
  
   - **Implementation**:​ We partner with the Technology and Digital Solutions team 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|effort]] in improving palliative care and its [[https://​www.statnews.com/​2020/​07/​01/​end-of-life-artificial-intelligence/​|coverage in Statnews]].   - **Implementation**:​ We partner with the Technology and Digital Solutions team 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|effort]] in improving palliative care and its [[https://​www.statnews.com/​2020/​07/​01/​end-of-life-artificial-intelligence/​|coverage in Statnews]].
   - **Ensuring that models are useful**: The 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 making the intervention.   - **Ensuring that models are useful**: The 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 making the intervention.
       - Check out our ideas 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]], [[https://​innovations.bmj.com/​content/​6/​2/​45|bridging the implementation gap of machine learning in healthcare]],​ and developing a [[https://​www.nature.com/​articles/​s41746-020-00318-y|delivery science for AI in healthcare]].       - Check out our ideas 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]], [[https://​innovations.bmj.com/​content/​6/​2/​45|bridging the implementation gap of machine learning in healthcare]],​ and developing a [[https://​www.nature.com/​articles/​s41746-020-00318-y|delivery science for AI in healthcare]].
-      - Read about our [[https://​academic.oup.com/​jamia/​article/​28/​6/​1149/​6045012|framework for quantifying the impact of healthcare delivery factors and work capacity constraints on achieved benefit]].+      - Read about our framework for quantifying the [[http://​academic.oup.com/​jamia/​article/​28/​6/​1149/​6045012|impact of work capacity constraints]] on achieved benefit, estimating [[https://​www.sciencedirect.com/​science/​article/​pii/​S1532046421001544|individualized utility]], and learning [[https://​pubmed.ncbi.nlm.nih.gov/​34350942/​|optimal decision thresholds]] from aggregate clinician behavior.
   - **Safety, ethics, and health system effects**: We map the multiple groups involved in taking action in response to a prediction and study their varying perspectives,​ positions, stakes, and commitments to pre-empt ethical challenges. Read our perspective on [[https://​www.nejm.org/​doi/​full/​10.1056/​NEJMp1714229|addressing ethical challenges]]. We believe that the use of AI can lead to good decisions if we keep [[https://​hai.stanford.edu/​news/​when-algorithmic-fairness-fixes-fail-case-keeping-humans-loop|human intelligence in the loop]].   - **Safety, ethics, and health system effects**: We map the multiple groups involved in taking action in response to a prediction and study their varying perspectives,​ positions, stakes, and commitments to pre-empt ethical challenges. Read our perspective on [[https://​www.nejm.org/​doi/​full/​10.1056/​NEJMp1714229|addressing ethical challenges]]. We believe that the use of AI can lead to good decisions if we keep [[https://​hai.stanford.edu/​news/​when-algorithmic-fairness-fixes-fail-case-keeping-humans-loop|human intelligence in the loop]].
   - **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]]. Stanford students, check out the [[https://​stanfordmlgroup.github.io/​programs/​aihc-bootcamp/​|AI for Healthcare Bootcamp]].   - **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]]. Stanford students, check out the [[https://​stanfordmlgroup.github.io/​programs/​aihc-bootcamp/​|AI for Healthcare Bootcamp]].
aihc.1634170600.txt.gz · Last modified: 2021/10/13 17:16 by nigam