Automated/integrated real-time clinical decision support in acute kidney injury

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Purpose of review

Health information technology advancements have resulted in recent increased sophistication of the electronic health record, whereby patient demographic, physiological, and laboratory data can be extracted real-time and integrated into clinical decision support (CDS).

Recent findings

The implementation of health information technology advancements into CDS in the renal realm has been focused mainly on assessment of kidney function to guide medication dosing in the setting of reduced function or to reactively detect acute kidney injury (AKI) heralded by an abrupt increase in serum creatinine. More recent work has combined risk stratification algorithms to guide proactive diagnostic or therapeutic intervention to prevent AKI or reduce its severity.


Early, real-time identification and notification to healthcare providers of patients at risk for, or with, acute or chronic kidney disease can drive simple interventions to reduce harm. Similarly, screening patients at risk for AKI with these platforms to alert research personnel will lead to improve study subject recruitment. However, sole reliance on electronic health record generated alerts without active healthcare team integration and assessment represents a major barrier to the realization of the potential of CDS to improve healthcare quality and outcomes.

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