Transparent Intelligence Explainability Frameworks for AI-Driven Clinical Decision Support in Healthcare Business Intelligence
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Abstract
Healthcare business intelligence (BI) enhances decision-making through evidence synthesis, visualization, and communication. New technologies and techniques introduce machine learning-based solutions that augment workflows and produce clinical insights (e.g. risk scores, disease progression predictions). However, such insights are notoriously opaque and fail to come with explanations. There are objective reasons why clinical stakeholders may reject insights that lack explainability: clinician trust directly affects decision-making performance, clinicians are trained to be safety vigilant, and AI-based support for decision-making processes is crucial in many use-cases. BI solutions that generate insights not only for clinicians but also for patients must also support patient understanding to be clinically meaningful. Decision workflows integrate clinical insights into user-facing systems
Accordingly, requirements for explainability apply to such solutions. Insufficiently transparent clinical insights and recommendations may lead to errors, waste resources, harm the credibility of the systems or healthcare more broadly, and ultimately harm patients. Recent studies show that unexplained clinical insights adversely affect clinician trust and decision-making performance, warning of potential negative repercussions when integrating untrusted AI-generated insights into clinical workflows. Support for reproducible clinical BI roadmap—covering model provenance, version control, explainability measures, importance of real-world monitoring, support for automated explanation generation at scale, and integration with clinical governance and workflow—may also help limit undesired consequences with AI-based clinical BI solutions
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