Successful Adoption of AI Tools in Clinical Care: Key Insights from Experts
Implementing AI tools in clinical care involves careful evaluation of their clinical and financial returns, gaining clinician endorsement, and establishing robust governance. These insights were shared by experts during a recent webinar in HealthLeaders’ The Winning Edge series.
Healthcare providers are navigating a complex landscape filled with various AI tools that enhance clinical care, such as AI scribes and AI-driven clinical decision support systems. To effectively introduce these technologies, chief information officers, chief medical information officers, and other leadership roles must ensure frameworks are in place, including AI governance, risk management, and clinician support for new solutions.
The recent webinar, titled The Winning Edge for Clinical AI Advancement, featured two leading experts in the field: James Blum, MD, Chief Health Information Officer at University of Iowa Health Care, and Linda Stevenson, MBA, Chief Digital Information Officer at Fisher-Titus Medical Center. They discussed the importance of assessing ROI, building clinician support, and establishing effective governance for AI tools.
Evaluating Clinical and Financial ROI
Engaging clinical stakeholders early in the adoption stages is essential for assessing the clinical and financial ROI of an AI tool, the experts emphasized. This early involvement should focus on the specific issues that the new tool aims to resolve. By identifying how the AI solution addresses a clinical problem, organizations can better gauge its potential impact and justify the financial investment.
The ROI from AI tools can vary significantly based on their intended applications. Some tools, like those enhancing coding processes, yield straightforward financial benefits by improving revenue capture. However, other tools, such as AI scribes, offer more nuanced returns. According to Blum, the widespread adoption of AI scribes is crucial for maintaining competitiveness in attracting and retaining medical professionals.
At the University of Iowa Health Care, plans to implement AI scribes were initiated to support faculty recruitment and enhance clinician care. Stevenson highlighted Fisher-Titus Medical Center’s efforts in rural communities, where adopting AI tools has alleviated workforce challenges by automating tasks and reducing the burden on clinical teams.
To effectively determine the ROI of AI tools, it’s important to involve key stakeholders, including the CFO, ancillary service staff affected by these tools, and IT teams, the experts stated.
Building Clinician Support for AI Tools
Securing clinician backing is vital for the successful roll-out of AI solutions in healthcare settings. The panelists reiterated that, similar to assessing ROI, engaging clinicians from the onset to focus on specific problems is crucial for fostering acceptance.
This approach diverges from the way traditional IT systems, like electronic medical records, have often been mandated. Leaders should not dictate the use of AI tools but instead highlight gaps where AI can enhance clinicians’ professional experiences, Blum advised.
Executive support can significantly influence clinician buy-in for AI tools. Stevenson noted that leaders in IT, as well as chief medical, nursing, and information officers, should promote AI tools while explaining the rationale behind their acquisition. Addressing concerns regarding job security for clinical staff is also essential, as the panelists assured that AI tools are not advanced enough to replace clinicians.
Effective AI Tool Governance
Rather than creating separate governance frameworks, both the University of Iowa Health Care and Fisher-Titus Medical Center advocate for a streamlined approach to AI governance. The experts explained that AI solutions are treated similarly to other IT tools, following the same acquisition protocols, cybersecurity assessments, and performance evaluations.
One notable exception at the University of Iowa is the formation of an AI oversight committee, which includes experts like Blum and specialists in genomic data analysis. This group is tasked with evaluating the appropriateness of AI tools within the health system’s patient demographic.
Both organizations practice continuous governance of their AI solutions to ensure they meet expected ROI levels. They also utilize contract renewal periods as significant opportunities to review tool performance and reassess if they effectively address the intended clinical challenges.
In summary, successful adoption and implementation of AI tools in clinical care hinges on a comprehensive strategy that prioritizes clinician engagement, emphasizes ROI evaluation, and establishes clear governance structures. By embracing these principles, healthcare organizations can navigate the complexities of AI integration and enhance their clinical operations.
