Clinical Artificial Intelligence (AI) has the potential to significantly transform the healthcare industry by improving efficiency, diagnostic accuracy and patient outcomes. However, to effectively utilize AI in high-stakes clinical settings, it is important to have a detailed, analytics-driven approach to measure performance and ascertain the overall value added to the organization. A lack of robust analytical approach can compromise the long-term effectiveness of AI in clinical settings.
Analytics plays a crucial role in directing the evolution of an AI strategy. It helps organizations understand their data in three key areas: technical performance, user engagement and value demonstration.
Technical performance involves the comprehensive analysis of all cases within the scope of AI use. It uncovers key aspects such as timeliness, AI prevalence, and overall algorithm performance. For AI to truly enhance clinical workflow, it needs to reliably notify and activate the right clinicians at the correct time.
User engagement focuses on ensuring the technology is useful, usable and used. While AI can indeed drive clinical workflow improvements, it is not enough to only provide insights. The true value of AI lies in the actions that clinicians can take as a result of AI-driven notifications. Therefore, it is imperative to measure user engagement and acceptance rates in the organization. This can be achieved through system engagement, acceptance surveys, training, and Q&A sessions.
Finally, value demonstration comes into play once satisfactory levels of technical performance and user engagement have been established. It is about showcasing the downstream value deriving from the implementation of AI, such as reduced reading times, quality improvement, shorter hospital stays and time to treatment, increased efficiency, and case prioritization.
Healthcare leaders are encouraged to question critical aspects of their AI strategy, focusing on areas such as data drift, timely case analysis, and effective monitoring. Questions can include assessing the stability and longevity of the AI solution, understanding if algorithms run strictly on protocols or in the background, and exploring methods for measuring the performance and impact of a given algorithm.
To fully harness the potential of clinical AI, it is crucial for healthcare facilities to approach it analytically. By assessing the success of their AI strategy based on data-driven insights, organizations can better manage challenges, measure algorithm performance, and uncover new opportunities. Ultimately, this approach will enhance patient care and outcomes in an increasingly digital age.
A platform-based approach is recommended for AI integration, as it provides scalable and future-proof strategies. The growth and adoption of clinical AI is a promising journey, but to navigate it successfully, analytics should guide the way.
