Clinical problem selection for machine learning-based clinical decision support in the intensive care unit: complexity, actionability, and the way forward

Abstract

Machine learning (ML)-based clinical decision support (CDS) in the intensive care unit (ICU) has the potential to improve medical decision-making and patient outcomes. The chasm between model development and bedside deployment threatens these outcomes. Drivers of the chasm are multifactorial and have been extensively studied. This perspective focuses on a critical phase of the ML pipeline that contributes to the chasm: problem selection. Problem selection is a challenging exercise requiring engagement of the entire multidisciplinary ML team, but there lacks a practical framework to guide discussions in a way that leads to meaningful candidate problem evaluation. We propose specific questions informed by the Information Value Chain Theory and other empirical groundings to consider while performing problem selection. The questions are focused on complexity and actionability and operationalized into a complexity-actionability problem evaluation (CAPE) checklist usable by ML teams to determine if the candidate problem-CDS pair is poised for impact or requires reformulation. We conclude by looking to the future where more effective CDS is routinely deployed to the bedside, while also suggesting that optimizing the execution of care in parallel with CDS is critical to achieve maximum value of the technology to bedside information and meaningful, scalable improvements in patient outcomes.

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