Widespread use of decision support at the point of care is one of the most promising yet difficult informatics interventions to implement, with the result that currently, despite the existence of well-validated online calculators and AI-based tools, the separate workflow and other design factors have meant these are used inconsistently, if at all, in practice. Further, variation in institutions’ health IT capacities may play a role, despite new mandates for interoperability. In addition, many clinicians may not have the time or training to interpret or evaluate such tools to a degree of confidence necessary for routine use. We discuss requirements and opportunities for increasing use and understanding, using lung cancer screening tools as an example. Issues include how to gather and integrate required information from multiple digital systems, the patient, the provider, population-based statistics, and clinical tests, send them to desired tools (singly or to multiple tools at once), receive and present risk scores and recommendations appropriately, and streamline follow-up tasks. ‘White box’ versus ‘black box’ approaches are discussed.

Learning Objective: Explore issues and opportunities in clinical decision support for lung cancer, at the point of care.


Yalini Senathirajah (Presenter)
University of Pittsburgh School of Medicine

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