The economic imperative for an AI Productivity Index in healthcare: Beyond clinical promise to fiscal accountability
TL;DR
An AI Productivity Index is proposed to complement existing safety, efficacy and economic assessments by evaluating operational impact, implementation burden, opportunity costs and post-deployment consequences.
Abstract
Artificial intelligence (AI) is increasingly embedded in healthcare delivery, yet its evaluation remains dominated by technical performance metrics that inadequately capture real-world system value. Clinical accuracy is necessary but insufficient to justify adoption in resource-constrained health systems facing workforce shortages and rising demand. AI technologies compete for limited financial, technical and cognitive resources, but there is no standardised framework to assess their effects on productivity, workflow, downstream utilisation or equity. This article argues that treating clinical validity as a proxy for value risks misallocating scarce resources and undermining trust in digital transformation. We propose an AI Productivity Index to complement existing safety, efficacy and economic assessments by evaluating operational impact, implementation burden, opportunity costs and post-deployment consequences. Embedding productivity measurement into procurement and governance processes could help align AI innovation with fiscal accountability, equitable access and sustainable healthcare delivery.