Evaluating Fair, Useful, and Reliable AI Models in Healthcare Systems

Belangrijkste concepten

The authors developed a comprehensive framework to assess the fairness, usefulness, and reliability of AI models in healthcare systems. Their approach involves ethical reviews, simulations for usefulness estimates, financial projections for sustainability, and prospective monitoring plans.

Samenvatting

The article presents a framework developed by the Data Science team at Stanford Health Care to evaluate AI models in healthcare settings. It emphasizes the importance of assessing the impact of AI model deployment before and after implementation. The process includes identifying fair, useful, and reliable AI models through ethical reviews, simulations for estimating usefulness, financial projections for sustainability, and prospective monitoring plans. The study conducted FURM assessments on six use cases with potential impacts on various patient populations. Two use cases have advanced to planning and implementation phases based on the assessment results.

The FURM assessment process consists of three stages: What & Why (assessing potential usefulness), How (identifying deployment requirements), and Impact (evaluating observed outcomes). Each stage includes multiple components such as problem definition, simulation-based utility estimates, financial projections, ethical considerations, model formulation recommendations, training methods evaluation protocols for model testing deployment infrastructure requirements organizational integration plans prospective evaluation strategies and monitoring plans.

Key findings include completed assessments on six use cases spanning clinical and operational settings with varying patient impacts. Financial projections were crucial in determining project sustainability while ethical considerations played a significant role in decision-making processes. The FURM assessment process evolved over time to enhance efficiency and consistency in evaluating AI solutions for healthcare systems.

Statistieken

Estimate the hidden deployment cost of predictive models to improve patient care - Nat Med 2020 Jan;26(1):18-19.
Implementing Machine Learning in Health Care — Addressing Ethical Challenges - New Eng J Med 2018 Mar 15;378(11):981-983.
Evaluating Ethical Concerns with Machine Learning to Guide Advance Care Planning - Journal of Investigative Medicine 2021;69:152

Citaten

"Effective decision-making by leadership requires a rapid, repeatable and flexible assessment process whose results can be distilled into an executive summary."
"We contribute three novel elements to existing testing and evaluation regimens: pre-deployment ethical review for identification of stakeholder value mismatches."
"Our goal is to identify additional costs that may be incurred and have the corresponding need for external support acknowledged."

Belangrijkste Inzichten Gedestilleerd Uit

Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems

by Alison Calla... om arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.07911.pdf

Diepere vragen

How can other healthcare systems adopt similar frameworks like FURM assessments?

Other healthcare systems can adopt similar frameworks like FURM assessments by following a structured approach that focuses on evaluating the fair, useful, and reliable aspects of AI models before deployment. Key steps to adopting such frameworks include:

Establishing a Clear Process: Define a step-by-step process for assessing AI models, including stages for problem definition, usefulness estimation, financial projections, ethical considerations, model formulation, training/testing, deployment infrastructure requirements, and organizational integration.

Engaging Stakeholders: Involve key stakeholders from various departments (clinical, IT, finance) in the assessment process to ensure comprehensive evaluations that consider all relevant perspectives.

Utilizing Simulation Tools: Use simulation tools like APLUS to estimate the achievable utility of proposed AI workflows based on technical performance and capacity constraints specific to each use case.

Conducting Financial Projections: Evaluate the potential financial impact of deploying AI models compared with existing practices to determine sustainability and cost-effectiveness.

Addressing Ethical Considerations: Conduct thorough ethical reviews using key informant interviews and ethical analysis to identify potential value mismatches or conflicts among stakeholders related to model design or deployment characteristics.

Monitoring Post-Deployment Impact: Develop plans for prospective evaluation and continuous monitoring post-deployment to assess real-world impacts on patient outcomes and operational processes.

By following these guidelines and customizing them according to their specific needs and resources, other healthcare systems can successfully implement frameworks similar to FURM assessments.

What are the implications of not conducting thorough evaluations like those proposed by this framework?

The implications of not conducting thorough evaluations like those proposed by the FURM framework could have significant consequences for healthcare systems:

Patient Safety Concerns: Without rigorous evaluations encompassing usefulness estimates via simulations and ethical considerations upfront, there is an increased risk of deploying AI models that may inadvertently harm patients due to biases or inaccuracies in predictions.

Financial Risks: Inadequate financial projections may lead to unsustainable implementations where costs outweigh benefits significantly over time.

Ethical Dilemmas: Neglecting comprehensive ethical reviews could result in unintended consequences such as privacy breaches or inequitable treatment across patient populations.

Operational Challenges: Lack of detailed assessments regarding workflow integration requirements might lead to implementation hurdles affecting staff efficiency and overall system performance.

Legal Compliance Issues: Failure t