From Reactive to Proactive: The Evolution of AI for Actionable and Explainable Healthcare Solutions

In the world of healthcare, artificial intelligence (AI) and machine learning are rapidly gaining prominence as powerful tools for optimizing patient outcomes and resource allocation. However, traditional machine learning models have often prioritized overall model performance over explainability, leading to a lack of actionable insights and potential misallocation of resources. At Siftwell Analytics, we have developed an AI solution that prioritizes explainability and actionability, enabling healthcare strategies to shift from reactive to proactive.

The Importance of Explainability in Healthcare AI

In healthcare, it is critical that predictive analytics be both transparent and understandable. This is key for ensuring health equity and for making the insights actionable to truly move the needle in the real world. At Siftwell, our approach balances strong performance with explainability – which drives actionability. This means that healthcare professionals can easily understand how the model is making predictions, enabling them to take more informed and proactive steps.

Building Causality into AI Systems

One key aspect of our approach is building causality into AI systems. Understanding the causal factors is crucial in healthcare decision-making, as it allows for more effective interventions and ultimately better patient outcomes. By transitioning from correlational observations to causal factors, we aim to provide healthcare professionals with a more accurate understanding of the underlying causes of certain outcomes. This approach ensures that resources are allocated effectively, and potential negative consequences are minimized.

Recent Use Case: Using AI to Encourage Breast Cancer Screenings

We are currently working with a health plan to drive positive outcomes with our technology by identifying women who are less likely to obtain breast cancer screenings and suggesting targeted interventions to encourage compliance. Our technology considers causal factors such as distance, family dynamics, and social determinants of health (SDoH) to guide healthcare professionals towards more effective and data-driven decision-making and member engagement. This approach ensures that healthcare resources are allocated effectively, and patients are more likely to receive timely and appropriate care.

Conclusion

Actionability is crucial for healthcare AI. By focusing our models on causal factors and balancing explainability with high performance, Siftwell is able to drive real-world action by our clients that improves lives.