Learning Effects from A GenAI-based Clinical Decision Support System in Primary Healthcare

Le résumé fourni par la source

Abstract To characterise the potential learning effects from a GenAI-based clinical decision support tool (CDST), we examined clinician behaviour within a cluster-randomised trial. The tool, AI Consult , parsed clinician notes written (in real-time) to document patient encounters and would raise green, yellow, or red flags to indicate no, potential, or critical risks of harm (respectively) in decisions the clinician made. Over several months, clinicians with access to the AI Consult tool produced fewer red (Intervention: 14% reduction, p = 0.032 vs. Control: 6% increase, p = 0.383) and yellow flags (Intervention: 6.8% reduction, p = 0.005 vs. Control: 3% increase, p = 0.231), whereas those without access to the tool showed no such effect. If this type of learning effect is a consistent emergent property across CDSTs, there might be an opportunity to reimagine their purpose: from addressing gaps in care quality to instead being a health system-strengthening investment.

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