Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems
<|reference_start|>Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems: The impact of using artificial intelligence (AI) to guide patient care or operational processes is an interplay of the AI model's output, the decision-making protocol based on that output, and the capacity of the stakeholders involved to take the necessary subsequent action. Estimating the effects of this interplay before deployment, and studying it in real time afterwards, are essential to bridge the chasm between AI model development and achievable benefit. To accomplish this, the Data Science team at Stanford Health Care has developed a Testing and Evaluation (T&E) mechanism to identify fair, useful and reliable AI models (FURM) by conducting an ethical review to identify potential value mismatches, simulations to estimate usefulness, financial projections to assess sustainability, as well as analyses to determine IT feasibility, design a deployment strategy, and recommend a prospective monitoring and evaluation plan. We report on FURM assessments done to evaluate six AI guided solutions for potential adoption, spanning clinical and operational settings, each with the potential to impact from several dozen to tens of thousands of patients each year. We describe the assessment process, summarize the six assessments, and share our framework to enable others to conduct similar assessments. Of the six solutions we assessed, two have moved into a planning and implementation phase. Our novel contributions - usefulness estimates by simulation, financial projections to quantify sustainability, and a process to do ethical assessments - as well as their underlying methods and open source tools, are available for other healthcare systems to conduct actionable evaluations of candidate AI solutions.<|reference_end|>
@article{callahan2024standing,
    title={Standing on FURM ground -- A framework for evaluating Fair, Useful, and
  Reliable AI Models in healthcare systems},
    author={Alison Callahan, Duncan McElfresh, Juan M. Banda, Gabrielle Bunney,
  Danton Char, Jonathan Chen, Conor K. Corbin, Debadutta Dash, Norman L.
  Downing, Sneha S. Jain, Nikesh Kotecha, Jonathan Masterson, Michelle M.
  Mello, Keith Morse, Srikar Nallan, Abby Pandya, Anurang Revri, Aditya Sharma,
  Christopher Sharp, Rahul Thapa, Michael Wornow, Alaa Youssef, Michael A.
  Pfeffer, Nigam H. Shah},
    journal={arXiv preprint arXiv:2403.07911},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07911},
    primaryClass={cs.CY cs.AI}
}