Harm Prevention

Discharge Optimization / LOS Outliers

We have to change the way we think about Length of Stay (LOS). We need to get past the idea that LOS is an administrative measure and start to think about it in terms of what we can do to optimize discharge for LOS outlier patients. The Jvion Cognitive Clinical Success Machine tells clinicians who is at risk of an extended stay at the time of admission. It tells them why the person is at risk and what can be done about it. The machine is like having a view into the future that shows you a patient’s outcome depending on the actions that you take today.

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LOS Deep Dive

Smiling Female Patient in Wheelchair Leaving Hospital
Length of Stay (LOS) Deep Dive
Managing patient length of stay is about providing the best, most appropriate and effective care in the optimum amount of time, without unnecessary...

Optimizing a Patient’s Discharge with Cognitive Machines

Optimizing a patient’s length of stay (LOS) directly improves health outcomes by reducing the risk of a hospital acquired condition and improving the overall patient experience. Moreover, by addressing LOS outliers—those patients who have longer than expected stays in the hospital—we lower the cost of care, improve resource allocation, and help to drive to improved financial outcomes.
LOS outlier patients in particular tend to use higher levels of resources across staffing, clinical testing, and pharmaceuticals than other hospital patients. In a recent study, LOS outlier patients accounted for only 5.4% of the study sample but accounted for almost 15% of the total hospital costs and 25% of the total inpatient days. But the challenge with LOS optimization overall is that it has multiple dependencies and complex interrelationships that flow across the hospital setting.
In a recent study, LOS outlier patients accounted for only 5.4% of the study sample but accounted for almost 15% of the total hospital costs and 25% of the total inpatient days.
The Cognitive Clinical Success Machine is uniquely capable of identifying outlier patients across inpatient episodes and delivering the recommended actions that will optimize LOS while reducing the likelihood of avoidable complications. The machine does this using the underlying Eigenspace architecture.
This approach enables the development of clinically relevant Eigen Spheres, which are used to find at-risk patients and extend to pin point the relevant clinical and socioeconomic factors driving the likelihood of an extended stay. A patient’s risk propensity and clinically relevant Eigen features are synthesized by the machine and translated into the specific patient-level actions that will help move what would have been a potential outlier to an optimized LOS. The machine does this day one of an inpatient stay regardless of the Diagnosis Related Group (DRG) and is able to derive the clinical conditions for the stay even if a working DRG is not available.


  • [1] MalgorzataCyganska, "The Impact Factors on the Hospital High Length of Stay Outliers," Procedia Economics and Finance, vol. 39, no. 1, pp. 251-255, 2016.
  • [2] Centers for Disease Control and Infection, "Sepsis - Basic Information," U.S. Department of Health & Human Services, 16 September 2016. [Online].
    https://www.cdc.gov/sepsis/basic/index.html — Accessed 6 September 2017
  • [3] Centers for Disease Control and Prevention, "Protect Your Patients from Sepsis Infographic," Department of Health and Human Services, 1 January 2016. [Online].
    https://www.cdc.gov/sepsis/pdfs/HCP_infographic_protect-your-patients-from-sepsis_508.pdf — Accessed 6 September 2017
  • [4] P. Thomas Desautels, B. Jacob Calvert, P. c. a. Jana Hoffman, B. Melissa Jay, M. Yaniv Kerem, M. P. Lisa Shieh, M. David Shimabukuro, M. M. Uli Chettipally, M. M. Mitchell D Feldman, M. Chris Barton, S. David J Wales and M. Ritankar Das, "Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach," JMIR Med Inform, vol. 4, no. 3, p. 28, July - Sept 2016.
Learn how hospitals across the country are using the Jvion Machine to stop patient illness, improve intervention effectiveness, and drive toward value-based models of care and reimbursement.
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