Quant HC

Accurately Predict Inpatient Deterioration

Be at the forefront, helping clinicians saves lives and reduce length of stay


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Publisher: Quant HC

Author: Dr. Dana Edelson, CEO Quant HC and

Exec. Medical Director Inpatient Quality & Safety, Uni. of Chicago

Analytic: Real time in-patient risk monitoring algorithm

Subject: cardiac arrest, sepsis, infection, respiratory failure, organ failure

Dr. Edelson’s work in predictive analytics harnesses electronic health record data to identify subtle changes in clinical stability and alert clinicians, in near real-time, to patients at risk for clinical deterioration and cardiac arrest.


  • Significantly reduce LoS and costs: allows clinicians to pre-empt decompensation through targeted interventions
  • Reduce mortality: early detection of severe deterioration helps clinicians save lives
  • Accurate: detects 88% of patients deteriorating in a general ward with a median forward-prediction time of 33 hours
  • Proven: currently in use at leading hospital systems
  • Best-in-class: a highly precise early warning prediction tool 

Detect inpatient risks such as Sepsis

Key features:

  • Pulls 16-30 data points from EMR and other data sources
  • Returns a single value that summarizes the risk of all-cause deterioration
  • Tracks the patient score in real time to examine trends
  • Alerts rescue and resiliency teams
  • Reports on outcomes
  • Risk output can be used on physician rounds, at the bedside or through EMR


See it in action:


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