Whether a classifier or prediction model is usefulness in guiding care depends on the interplay between the model's output, the intervention it triggers, and the intervention’s benefits and harms.

We study this interplay for bringing AI to the clinic, safely, cost-effectively and ethically to inform the work of the Data Science Team at Stanford Healthcare. Our work stemmed from the effort in improving palliative care using machine learning. Blog posts at HAI summarize our work in easily accessible manner. Ensuring that machine learning models are clinically useful requires quantifying the impact of work capacity constraints on achievable benefit, estimating individualized utility, and learning optimal decision thresholds.

Whether a classifier or prediction model is usefulness in guiding care depends on the interplay between the model's output, the intervention it triggers, and the intervention’s benefits and harms.

We study this interplay for bringing AI to the clinic, safely, cost-effectively and ethically and to inform the work of the Data Science Team at Stanford Healthcare in performing assessments to ensure that we are creating Fair, Useful, Reliable Models (FURM). Blog posts at HAI summarize our work in easily accessible manner. Our research stemmed from the effort in improving palliative care using machine learning. Ensuring that machine learning models are clinically useful requires estimating the hidden deployment cost of predictive models as well as quantifying the impact of work capacity constraints on achievable benefit, estimating individualized utility, and learning optimal decision thresholds. Pre-empting ethical challenges often requires keeping humans in the loop and focus on examining the consequences of model-guided decision making in the presence of clinical care guidelines.