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 (perhaps learning from similar patients).
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, 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. See brochure. The four key components are:
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.
Russ Altman and Nigam Shah taking an in-depth look at the growing influence of “data-driven medicine.”
Keeping the Human in the Loop for Equitable and Fair Use of ML in Healthcare, at AIMiE 2018
Building a Machine Learning Healthcare System, at XLDB, April 30 2018