The Value Opportunity from Artificial Intelligence in U.S. Health Care Spending.
TL;DR
The analysis considered the savings potential from full national implementation of three specific AI technologies - machine learning (ML), natural language processing (NLP), and generative AI (genAI) - across administrative and medical expense categories for payers and providers (but excluded onetime implementation costs).
Abstract
Artificial intelligence (AI) offers the potential to improve productivity and reduce waste across the U.S. health care system. Quantifying achievable value from AI technologies can inform organizational strategies and national spending projections. This study aimed to estimate the annual run-rate net value achievable within 5 years through full adoption of AI use cases across major health care stakeholders and domains without compromising quality or access. This study applied observed implementation benchmarks and national expenditure data to 12 AI-enabled domains spanning five stakeholder groups - private payers, public payers, hospitals, physician groups, and other sites of care - collected during the period of October 2023 and March 2024. The financial impact was estimated using 2024 data (the latest year for which full data are available) of U.S. health care expenditures, using ranges from published literature and observed implementation evidence. No human participants were involved. The analysis considered the savings potential from full national implementation of three specific AI technologies - machine learning (ML), natural language processing (NLP), and generative AI (genAI) - across administrative and medical expense categories for payers and providers (but excluded onetime implementation costs). Annual net value (2024 U.S. dollars) and percentage reductions in total, administrative, and medical expenses by stakeholder group and AI technology were assessed. Assuming full adoption - i.e., a health care environment in which all stakeholders are all-in on AI adoption for all domains across administrative and medical expenses for all designated AI technologies, in this case, ML, NLP, and genAI - AI could generate US$438.9-US$810.7 billion in annual net value (5.7%-10.6% of the US$7.7 trillion in 2024 total health care expenses). GenAI-led use cases account for 54.4%-55.9% of the total AI opportunity. Administrative expenses could decline by US$120.1-US$252.0 billion (9.4%-19.8%) and medical expenses by US$318.8-US$558.6 billion (5.0%-8.7%). By stakeholder group, estimated annual values are as follows: private payers, US$205.1-US$357.0 billion (7.0%-12.1%); hospitals, US$130.6-US$219.5 billion (8.6%-14.4%); physician groups, US$29.0-US$89.9 billion (2.8%-8.6%); public payers, US$62.4-US$107.4 billion (5.1%-8.8%); and other sites of care, US$11.9-US$36.9 billion (1.3%-4.0%). Financial impact is concentrated in health care management, provider relationship management, and claims management for payers and in clinical operations and quality and safety for providers. Labor productivity and administrative automation account for the largest share of impact. Full implementation of these AI technologies could reduce U.S. health care spending by up to US$810.7 billion within 5 years without compromising quality and access. Realizing this full potential would require properly aligned incentive models (e.g., between physicians and the hospital), as well as coordinated organizational change, workflow redesign, and infrastructure investment, especially in clinical domains. Responsible scaled adoption supported by policy and industry efforts could help bend the U.S. health care cost curve.