Healthcare AI Adoption Grows, but Governance Lags Behind

The rapid adoption of artificial intelligence (AI) solutions in healthcare is reshaping how medical professionals diagnose, treat, and manage patient care. However, a recent study by UPMC’s Center for Connected Medicine and KLAS Research reveals that while many organizations are deploying AI tools, they are still grappling with the foundational infrastructure and governance necessary to maximize their impact.

Ninety-three percent of respondents report using third-party AI solutions, but only 44% have dedicated data platforms or environments for testing these tools before deployment. This gap highlights a critical need for health systems to build robust governance structures that ensure AI delivers meaningful and measurable value, according to Rob Bart, M.D., UPMC chief medical officer.

“What’s emerging from this research is a clear recognition that implementation is only the first step,” said Dr. Bart. “Health systems are now focused on building the governance structures, testing capabilities, and organizational strategies necessary to ensure AI delivers meaningful and measurable value.”

The report, titled "Validation and Trust: The Governance of AI Solutions at Health Systems," draws insights from 27 healthcare leaders, including those from health systems and ambulatory care organizations. These leaders highlight both the benefits and challenges of integrating AI into their operations.

Challenges in Data Quality and Infrastructure

One of the most significant pain points for data quality is the reliance on manual workarounds, spreadsheets, and inconsistent definitions across different teams. This suggests that many health systems still depend on labor-intensive processes to reconcile, interpret, and prepare data for reporting or AI-related tasks.

Clinical documentation tools are the most commonly cited AI solution among respondents, with 52% deploying such tools. Revenue cycle, coding, and billing applications follow closely at 36%. However, the majority of organizations report that their data analysis is primarily conducted within electronic health records (EHRs) or vendor analytics tools, followed by cloud data warehouses/lakehouses and multiple marts or warehouses.

Five respondents were unsure about where their data analysis takes place, indicating a lack of clarity and standardization in how data is managed across different systems. This inconsistency can lead to errors and inefficiencies, undermining the potential benefits of AI.

What Comes Next

As healthcare organizations continue to integrate AI into their workflows, the need for robust governance and infrastructure will only become more critical. The report underscores the importance of developing comprehensive testing capabilities and organizational strategies to ensure that AI solutions are both effective and ethical.

In addition to internal efforts, regulatory compliance and data privacy concerns must be addressed. As AI-assisted clinical decisions, connected health systems, and mobile healthcare continue to evolve, policymakers and industry leaders must work together to establish clear guidelines and standards.

The integration of AI in healthcare is not just a technological challenge but also a human one. Ensuring that these tools are used ethically and effectively will require ongoing collaboration between healthcare providers, technology developers, and regulatory bodies. By addressing the current gaps in governance and infrastructure, health systems can unlock the full potential of AI to improve patient outcomes and enhance the overall quality of care.

The journey toward effective AI governance is just beginning, but with a focused effort on building the necessary foundations, the future of healthcare looks promising.