Cognitive Impact
Patient Impact

An AI Solution that Works for Healthcare

Cognitive Impact
  • Hospital Acquired Conditions

    Hospitals use Jvion’s Cognitive Machine to identify patients who are on a trajectory toward a hospital acquired condition and to determine the best clinical actions that will improve an outcome. The machine is so much more than a combination of predictive models aimed at the list of target conditions provided by the Centers for Medicaid and Medi...   Continue
  • Healthcare-associated Infections

    Preventing Healthcare-associated Infections (HAIs) is possible. But an effective HAI program requires two things: conscious effort on the part of all stakeholders and the ability to effectively intervene on the right patients. Jvion’s Cognitive Clinical Success Machine serves to reduce the cognitive burden placed on providers by identifying the...   Continue
  • Readmissions

    Providers use Jvion’s Cognitive Clinical Success Machine to reduce the full-spectrum of patient readmission events. It looks far beyond simple risk to the tens-of-thousands of factors that impact why a person will return to the hospital. It uses its hyperdimensional platform to determine if a patient is coming back and the exact actions that wil...   Continue
  • Bedside Patient Rescue

    Developed in collaboration with the Mayo Clinic, Bedside Patient Rescue combines the power of Jvion’s Cognitive Clinical Success Machine with a standardized approach to time-limited escalation of expertise. With the Jvion machine pointed at Bedside Patient Rescue, providers can reduce mortality by close to 10% and reduce the time to intervention...   Continue
  • Optimizing a Patient’s Discharge with Cognitive Machines

    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 ad...   Continue
  • Improved Patient Engagement and Experience Driven by Cognitive Machines

    If you knew the potential gaps in care that will result in a poor experience, you would fill them. And if you knew which clinical actions would result in the highest levels of engagement, you would do them. Jvion’s Cognitive Clinical Success Machine does both. It helps hospitals identify the gaps in care that will reduce patient satisfaction and...   Continue
  • Ambulatory Care Sensitive Vectors

    Providers are using Jvion’s Cognitive Clinical Success Machine to stop avoidable admissions and drive primary prevention of potentially avoidable conditions. The machine does so much more than simply identifying high-risk individuals. It finds the people within the community who are on a trajectory toward a potentially avoidable event and provid...   Continue
  • Provider-based Impact

    Living in between fee-for-service and value-based worlds is hard on providers. Luckily, Jvion’s Cognitive Clinical Success Machine is equipped to help ensure success by improving quality outcomes, lowering the cost of care, and optimizing operations. Its core capability is to give providers the cognitive power they need to see the future state o...   Continue
 
Learn how hospitals across the country are using Jvion's Cognitive Clinical Success Machine to stop patient illness, improve intervention effectiveness, and drive toward value-based models of care and reimbursement.

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Avoiding Sepsis in the Hospital Using the Cognitive Clinical Success Machine

Centers for Disease Control and Prevention (CDC) defines sepsis as the body’s extreme response to infection. It occurs when an infection that a patient already has triggers a life-threatening chain reaction. More than 1.5 million people get sepsis and at least 250,000 Americans die from sepsis each year. While anyone can get an infection that can lead to sepsis, there are groups of people who are at higher risk including adults over 65, people with chronic conditions, people with a compromised immune system, and children younger than one.
vaccination
Historically, it has been very hard to identify patients at risk of sepsis before onset of the infection. Existing methods have not met performance thresholds and tend to lead to extensive and expensive laboratory testing. However, with the application of the Cognitive Clinical Success Machine’s Eigen-based engine, we are identifying:
  • Individuals at risk of sepsis before they enter the hospital
  • Patients who are at risk of sepsis when they are in the acute care setting
  • Patients who had sepsis on the index admission who are at risk of readmission
This capability is driven by the transformative Eigen approach that underpins Jvion’s machine. By combining the established Eigen Sphere infrastructure—which enable the analysis of more than a quadrillion socioeconomic, behavioral, and clinical factors—with extensive clinical intelligence, the machine is able to more precisely and effectively identify patients on a trajectory toward sepsis. The resulting outputs enable faster and more effective clinical action that ultimately leads to improved outcomes for patients and the hospital.

References

  • [1] Centers for Disease Control and Infection, "Sepsis - Basic Information," U.S. Department of Health & Human Services, 16 September 2016. [Online]. Available: https://www.cdc.gov/sepsis/basic/index.html. [Accessed 6 September 2917 ].
  • [2] Centers for Disease Control and Prevention, "Protect Your Patients from Sepsis Infographic," Department of Health and Human Services, 1 January 2016. [Online]. Available: https://www.cdc.gov/sepsis/pdfs/HCP_infographic_protect-your-patients-from-sepsis_508.pdf. [Accessed 6 September 2017].
  • [3] 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.
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