As the healthcare sector accelerates its integration of artificial intelligence, a new initiative from Health Care Without Harm (HCWH) is challenging hospitals and health systems to confront the hidden environmental costs of their digital infrastructure. The organization’s recently published U.S. Artificial Intelligence Procurement Guide serves as a strategic blueprint for healthcare leaders to leverage their massive purchasing power, demanding that AI vendors prioritize environmental stewardship alongside clinical utility.
The rapid adoption of AI is not without significant physical consequences. While often viewed as intangible software, AI relies on massive data centers that contribute to air pollution, high energy consumption, water scarcity, and community displacement. As healthcare organizations pursue digital transformation, HCWH is urging them to ensure that these technological advancements do not come at the expense of the very populations they serve.
“We want to be able to leverage the buying power of the healthcare sector to make all Americans, all people in this country, healthier,” said Jenny Keroack, director of climate solutions for HCWH’s U.S. Climate Program. “We want to see AI companies become net assets for their communities.”
The guide provides a practical framework for healthcare procurement departments to shift the landscape of AI development. It outlines 10 core disclosure questions that vendors must answer, covering everything from greenhouse gas emissions and energy mixes to water usage and data center efficiency. To ensure the transition is manageable, the guide includes scoring guidance that allows vendors a one-year grace period to collect data on third-party infrastructure. After this initial phase, however, providers are encouraged to incorporate these disclosures into legally binding contracts.
According to Emily Bancroft, CEO of HCWH, the goal is to formalize environmental responsibility. “Procurement is not only a tool for gathering this data. It’s also a tool for eventually putting obligations into contracts,” she noted.
Beyond simple disclosure, the guide promotes technical efficiency. It highlights the Sustainably Advancing Health AI (SAHAI) Framework, recently published in the New England Journal of Medicine, which suggests operational shifts such as processing non-urgent tasks during off-peak hours and utilizing “mixed-quality” models—using simpler, less energy-intensive models for basic data processing and reserving high-performance computing for complex clinical tasks. Furthermore, the guide advises organizations to toggle off optional AI features that offer little clinical value but significant energy costs.
These efforts aim to correct an industry imbalance. While large healthcare systems are increasingly aligning with the Science Based Targets initiative (SBTi) to reach net-zero emissions by 2050, many AI companies—particularly smaller startups—have yet to commit to similar rigorous standards. By centralizing environmental expectations, healthcare providers can force the market to adopt cleaner practices.
The urgency of this initiative is amplified by the current political climate. As federal environmental protections face potential reversals, such as the recent repeal of fuel-fired power plant emission limits, the healthcare sector’s role as a decentralized regulatory body becomes increasingly critical. Bancroft emphasizes that the healthcare industry has a long history of forcing supply chain change, having successfully phased out harmful chemicals in medical furniture and mercury in diagnostic devices.
The transition to “green” AI is not just an ethical imperative; it is also a financial one. Improving energy efficiency in AI operations can lower operational costs for both vendors and healthcare providers. As the sector continues to rely on AI for clinical outcomes, the mandate from HCWH is clear: health is not optional, and the vendors that supply the industry’s future must be held accountable for the planet’s health as much as the patient’s.
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