Monitoring the performance, value, and risk of AI tools in healthcare is becoming increasingly important, especially as more vendor solutions enter clinical environments. This perspective article introduces an early pipeline framework designed to help health systems systematically assess AI products before, during, and after deployment. Unlike traditional governance models that focus mainly on technical performance or ethical compliance, this framework emphasizes strategic alignment, executive sponsorship, measurable return on investment (ROI), and proactive risk assessment across multiple domains.
The framework guides decision-makers through key steps: ensuring that AI tools support the health system’s goals, assigning executive oversight, forecasting impact with clear metrics, and evaluating risks such as safety, data integrity, transparency, and operational disruption. A notable insight is that even accurate and promising AI models can under perform, or cause harm, if deployed without structured governance and a clear understanding of their expected value. The authors argue that success requires more than validation studies; it demands an upfront, practical evaluation of whether a solution is truly worth adopting.
Importantly, the framework is scalable and adaptable, making it suitable for health systems of varying sizes and capacities. It allows assessments to be staged or run in parallel, offering flexibility without sacrificing rigor. As regulatory scrutiny and AI adoption continue to rise, this model offers a practical blueprint for sustainable, safe, and high-impact implementation. Informal or reactive oversight will no longer be sufficient, health systems must adopt structured, ROI-focused governance from the very beginning.
Read the full study to learn how early, structured governance can help your team choose the right AI tools, manage risks, and ensure real return on investment.
An early pipeline framework for assessing vendor AI solutions to support return on investment
Nature Partner Journals, 2025
Abstract
The success of AI solutions in health systems depends on governance from use case inception through deployment and auditing. This proposed early pipeline governance framework for vendor AI solutions highlights a four-pronged approach: strategic alignment, executive sponsorship, impact and value case assessment, and risk assessment. Each component can be scaled to health systems of any size and the risk and impact assessments can take place simultaneously or sequentially.