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Real$-$time data analytics in the ICU can transform hundreds of data points into actionable decisions by aggregating, streamlining and applying data mining techniques, machine learning algorithms and clinical decision alerts. During aggregation and streamlining, all patient data points are compiled cohesively and made accessible to all providers concerned. Integrating medical devices and hospital information systems $($Imdsoft$,2014)$ can help inform clinicians about patient health severity, ensuring that patient data $($including vital signs and laboratory results$)$ is relevant data for care coordination.

Applying machine learning algorithms during data mining in the ICU could reveal hidden patterns and associations within large datasets, further assisting clinicians in decision making $($Carra et al$.,2020,$ p$.301).$ The input selections from the data affect both the type of algorithm chosen as well as its accuracy $($Carra et al$.,2020,$ p$.301).$ Data mining algorithms in the ICU are able to use data points to predict hospital and ICU readmissions and also assess and predict ICU length of stay in admissions patients using risk assessment to proactively plan for bed availability $($Carra et al$.,2020,$ p$.302).$ Clinical decision support systems also take hundreds of data points from patients' electronic health records and synthesizes the information into alerts according to medication, laboratory and administrative offering guidance and decision$-$making insights for clinicians $($Olakotan et al$.,2020,$ p$.1).$