AI Management: Preparing Healthcare for a New Era

By Henry Adams , Country Manager, InterSystems South Africa

A common question that arises in discussions about AI in healthcare is, "Where do we begin?" The landscape can feel overwhelming, but my simple answer is always, "Start small. Find the 'killer app.'" Focusing on a specific, manageable use case can make all the difference in demonstrating AI's value and setting the stage for broader adoption.

As the healthcare sector embraces AI, one of the greatest challenges we face is managing the change that comes with it. While AI holds incredible potential to improve patient outcomes and streamline operations, the true success of these technologies depends on effectively managing the change they bring. Integrating AI into existing healthcare environments requires careful coordination of people, processes, and, most importantly, data.

Healthcare is no stranger to innovation, but AI introduces a unique dynamic. AI can help optimise clinic no-show predictions, revenue cycle management, and chronic disease monitoring. However, for these systems to work effectively, change management is critical. Without careful planning, AI implementations risk overburdening healthcare professionals with new administrative tasks, slowing down workflows, and missing out on the value AI can bring.

Why Data Is the Foundation for AI

Healthy data serves as the essential foundation for AI, much like clean water was to 19th-century public health. Before healthcare organisations can fully benefit from AI's capabilities, the data that feeds into these systems must be accurate, well-structured, and free from bias. Ensuring data readiness is not just a technical task but a strategic one, as AI systems rely on high-quality information to deliver precise insights and improve patient outcomes.

At InterSystems, we've seen firsthand how essential healthy data is for AI applications. AI can only deliver the right insights if the data is accurate and complete. When organisations focus on data governance and quality, they can unlock the full potential of AI to drive both precision and efficiency in patient care.

Start Small, Build Confidence

When implementing AI, beginning with a clear, manageable use case is crucial. Focusing on a specific application—such as using AI to predict clinic no-shows or manage chronic diseases provides tangible results. This focused approach not only proves the value of AI but also builds trust and confidence among stakeholders, creating momentum for broader adoption.

Explore this resource for more insights on leveraging AI in healthcare, including how generative AI is revolutionising patient care and diagnosis.

Prepare for Challenges and Embrace Learning

AI is a powerful tool, but like any innovation, it comes with risks. Predictions suggest that up to 80% of AI projects may underperform initially. However, these early challenges should be viewed as learning opportunities, allowing organisations to refine their approaches. Starting with smaller, lower-risk AI initiatives offers the flexibility to adjust strategies quickly, paving the way for future successful, large-scale implementations.

Leading AI-Driven Change in Healthcare

The integration of AI in healthcare has the potential to transform patient outcomes, clinician workflows, and operational efficiency, but navigating this journey requires thoughtful change management. It starts with ensuring healthy data, engaging in small-scale projects, and building a culture willing to learn and adapt.

At InterSystems South Africa we are committed to helping healthcare organisations manage this change successfully. Focusing on collaboration, transparent leadership, strategic planning, and a commitment to continuous improvement is essential in ensuring that AI delivers on its promise to reshape healthcare.

Visit our website to learn about AI and its applications, benefits, and ethical considerations here.