Highlight
A recent survey of 233 US healthcare systems shows that 80% have deployed at least pilot-level automation tools, and 71% have integrated AI solutions, including GPT-based chatbots and transcription aids. Nearly half have comprehensive automation deployment, while just over a third have fully implemented AI. Cost reduction drives AI investments, yet fewer than 40% of CFOs believe AI investments reduce overall costs. Key barriers include lack of resources and funding, with CFOs and CIOs as primary decision-makers favoring AI vendors integrated with existing EHR systems. The digital health investment landscape shows robust AI startup funding, supported by strategic policy initiatives such as the White House AI action plan.
Study Background and Clinical Context
Artificial intelligence (AI) is increasingly recognized as a transformative force in healthcare, offering opportunities to enhance diagnostic accuracy, streamline administrative workflows, improve patient engagement, and reduce costs. In the context of escalating healthcare expenditures and workforce shortages, AI-driven automation tools including chatbots, natural language processing (NLP) transcription, and predictive analytics aim to optimize care delivery efficiencies. Despite technological advancements, adoption has encountered hurdles including implementation costs, integration challenges with electronic health record (EHR) systems, and limited organizational resources, necessitating a clear understanding of adoption patterns and barriers within healthcare systems.
Study Design and Population
This cross-sectional survey study sampled 233 healthcare system respondents representing diverse US medical facilities. The survey measured the adoption status of automation and AI tools, ranging from pilot to full deployment stages. Respondents reported on AI use cases, decision-making structures — including the roles of chief financial officers (CFOs) and chief information officers (CIOs) — perceived benefits such as cost reduction, and barriers to technology uptake. Additionally, attitudes toward vendor integration with EHR systems and willingness to share data with AI vendors were assessed. Funding trends in AI startups were also contextualized using venture capital data for early 2025.
Key Findings
Among respondents, 80% have deployed automation tools at least at pilot level, with 71% deploying AI-driven solutions. Full automation deployment was reported by approximately 50%, whereas only 36% indicated full AI integration, highlighting a gap between general adoption and mature use. Nearly 90% use AI in some form internally, including GPT-powered chatbots and AI transcription services, illustrating enthusiasm for AI applications that enhance clinical and administrative functions.
Cost reduction is the primary rationale for AI investment. However, skepticism remains, with only 39% of CFOs confident that AI will reduce overall costs. This disparity underscores the need for rigorous cost-effectiveness analyses and demonstration of economic value in real-world healthcare settings.
Resource and cost constraints emerged as the dominant barriers: more than 80% of respondents indicated insufficient internal resources to identify, select, and implement AI solutions. This points to critical organizational capacity gaps hampering wider AI adoption.
Decision-making around AI vendor selection lies mainly in the hands of CFOs and CIOs. Around 75% of respondents prefer vendors whose AI tools are integrated with their existing EHR systems, facilitating smoother workflows and data interoperability. Notably, 11% reported no intention to invest in AI before solutions are available via their EHR providers. Nearly 80% expect existing vendors or those in partnership with current suppliers to dominate AI pilot projects, reflecting entrenched supplier relationships and trust. Correspondingly, a similar proportion of respondents are more willing to share data with known vendors, indicating data security and privacy as critical considerations.
Investment trends mirror this optimism: AI startups captured 62% of digital health venture funding in early 2025, amounting to nearly $4 billion. Such investment inflows fuel AI innovation and market competition. Concurrently, the White House’s recently announced AI action plan underscores governmental commitment to responsible AI development and deployment in healthcare, reinforcing the strategic priority of AI to improve health outcomes and system efficiencies.
Expert Commentary
These findings align with the growing body of literature emphasizing the dual nature of AI in healthcare: high potential yet significant implementation barriers. Experts acknowledge automation and AI as necessary to meet increasing clinical demands but caution that without adequate infrastructural and financial support, organizations may struggle to realize AI benefits. The role of integrated EHR systems as gatekeepers to AI adoption suggests that future innovations must emphasize interoperability and vendor collaboration to increase uptake.
Moreover, widespread use of GPT-based and NLP-enabled tools raises questions about data governance, accuracy, and ethical use, which require ongoing clinical validation and regulatory oversight. The relatively low confidence of CFOs regarding cost savings signals the importance of robust health economic studies that can inform investment decisions.
Conclusion
The survey provides an important snapshot of AI adoption in US healthcare, revealing substantial but uneven deployment, key financial and resource hurdles, and firm preference for integrated vendor ecosystems. While AI promises cost efficiencies and improved care delivery, healthcare systems face organizational challenges that necessitate strategic investments in infrastructure, workforce training, and vendor partnerships. Continued governmental support, evidenced by the White House AI action plan, combined with sustained private funding underscores AI’s critical role in the future of healthcare. For clinicians and administrators alike, understanding these evolving dynamics is vital to harnessing AI’s potential and ensuring safe, effective, and equitable healthcare innovation.
References
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2. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230-243. doi:10.1136/svn-2017-000101
3. Obermeyer Z, Lee TH. Lost in thought — the limits of the human mind and the future of medicine. N Engl J Med. 2017;377(13):1209-1211. doi:10.1056/NEJMp1705348
4. White House Office of Science and Technology Policy. National AI Initiative: AI Action Plan. 2024. Available at: https://www.whitehouse.gov/ostp/news-updates/2024/06/01/national-ai-initiative-action-plan/ (Accessed June 2024)
5. Digital Health Venture Funding Report Q1 2025. Rock Health. 2025. Available at: https://rockhealth.com/reports/2025-q1-digital-health-funding/ (Accessed June 2024)
