Cognitive Impact

The Cognitive Clinical Success Machine identifies those individuals who had sepsis on the index admission who are at risk of a readmission and it delivers the recommended actions that will reduce that risk.

This vector enables the right clinical action and engagement with community resources to ensure the best possible patient outcome.

And because the machine accounts for the full etiology of the readmission, the recommended actions that it delivers are tailored to the demographic, socioeconomic, and clinical conditions that drive so much of the risk that a patient will return to the hospital.

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.

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.

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

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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Targeted, Primary Prevention of Sepsis with the Cognitive Clinical Success Machine

Sepsis, a body’s overactive, toxic response to an infection, is one of the most expensive and deadly syndromes. But detecting sepsis is difficult in large part because many of its signs and symptoms can be mistaken for other conditions. And this challenge is compounded by the need to detect and treat sepsis as early as possible to avoid escalation and possible death.

Using the Cognitive Clinical Success Machine, we are changing the way we identify and prevent sepsis to shift it from one of secondary care to primary prevention. Here is how.

According to the Centers for Disease Control and Prevention (CDC), more than 90% of adults and 70% of children who developed sepsis had a health condition that put them at risk. More than 40% of these cases were developed within the community setting. And within that group, certain types of diseases and infections led to sepsis more often including infections of the lungs, urinary tract, skin, and gut.

In a recent study published in the Morbidity and Mortality Weekly Report, more than 70% of patients who had a sepsis admission had a health event within the past 30 days stemming from a chronic condition that likely required frequent medical attention. While most sepsis initiatives focus on early detection and education, these occurrences could have been prevented through targeted strategies including vaccinations and disease management. But effective prevention requires the ability to determine who is at risk of developing sepsis within the ambulatory setting and before any signs are present. This is exactly where the Cognitive Clinical Success Machine is helping providers to do.

By determining who within the community is at risk of sepsis and the clinical actions that will reduce that risk, providers are using the Cognitive Clinical Success Machine to align programs such as pneumonia vaccinations to sepsis reduction initiatives. This capability is enabled by the machine’s Eigenspace platform that more effectively identifies the individuals who are on track to sepsis and the best actions that will result in a better health outcome for a patient. This is the only solution and approach able to provide a path to primary sepsis prevention for community-based sepsis events. And it is the only machine with the breadth and flexibility to help patients across settings and populations.

References

[1] Sepsis Alliance, "Definition of Sepsis," Sepsis Alliance, 1 1 2017. [Online]. Available: https://www.sepsis.org/sepsis/definition/. [Accessed 18 09 2017].

[2] Centers for Disease Control and Prevention, "Making Health Care Safer Think sepsis. Time matters.," Centers for Disease Control and Infection: National Center for Emerging and Zoonotic Infectious Diseases, Atlanta, 2016.

[3] M. Shannon A. Novosad, P. Mathew R.P. Sapiano, D. Cheri Grigg, M. Jason Lake, D. Misha Robyn, M. Ghinwa Dumyati, M. Christina Felsen, M. Debra Blog, M. Elizabeth Dufort, P. Shelley Zansky, M. Kathryn Wiedeman, M. Lacey Avery, M. Raymund B. Dantes, M. John A. Jernigan, M. Shelley S. Magill, M. Anthony Fiore and M. Lauren Epstein, "Vital Signs: Epidemiology of Sepsis: Prevalence of Health Care Factors and Opportunities for Prevention," Morbidity and Mortality Weekly Report, vol. 65, no. 33, pp. 864-869, 23 August 2016.

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Emergency Room High Utilizers

ER high utilizers have been defined as "people of modest means and poor health who go in and out of emergency rooms day after day, their fundamental health issues rarely resolved, at a tremendous and ever-growing cost to hospitals, municipalities and taxpayers." These individuals are largely suffering from chronic conditions and live in areas with restricted access to outpatient care facilities.

Emergency departments become the primary care provider for many who are unable to access and/or lack the resources needed to secure a regular primary care physician.

The impact to the system is significant. ER high utilizers and the resulting avoidable ER visits translate into increased resource constraints, financial waste, and overcrowding. The Emergency department is an expensive place to deliver care -- especially when the care administered is for non-emergency occurrences. According to the New England Healthcare Institute (NEHI), approximately $32B is wasted each year on avoidable ER visits.

The focus on these patients is primarily driven by the need to cut healthcare costs. While ER high utilizers are seen as a major contributor to waste, the equation isn't straight forward. Getting these patients to use primary care pathways is a start, but it doesn't address clinical and social complexity driving what are deemed avoidable ER visits. High utilizer interventions have to be tailored and account for the nuances within the population. For example, mental health and substance-abuse are contributing factors to avoidable ER visits and are correlated with high-levels of spend/resource allocation. The lack of mental health resources is a major underlying driver for these visits and one that has been well documented.

According to the New England Healthcare Institute (NEHI), approximately $32B is wasted each year on avoidable ER visits.

As value-based models of care and reimbursement redefine accountability and performance both inside and outside of the hospital gain industry traction, more focus will be placed on preventing avoidable ER visits and implementing interventions within the community. Finding the right care environment that leads to better health outcomes will ultimately reduce waste across the system, not just within the ER. And while ER high utilizers are a complex patient cohort, the right levels of care coordination and community-based interventions can help reduce the burden that they place on the hospital while improving the overall health of individual patients.

References

Jamieson D. The Treatment of Kenny Farnsworth. Washington Post Magazine 2009.

Emergency Department Overuse: Providing the Wrong Care at the Wrong Time. Cambridge, MA: New England Healthcare Institute; 2008

Frequent Users of the ER Fact Sheet; American College of Emergency Physicians.

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Chronic Condition Management Through Cognitive Machines

According to the National Council on Aging...

  • 92% of older adults have one, and 77% have at least two chronic conditions
  • Heart disease, cancer, stroke, and diabetes cause almost 60% of all deaths each year
  • Diabetes impacts 23% of the older population
  • An additional 57 million Americans aged 20+ have pre-diabetes
  • 90% of Americans aged 55+ are at risk for high blood pressure

Chronic conditions comprise more than three-quarters of the healthcare spend in the United States.

In addition to patient suffering, chronic diseases also contribute to higher rates of avoidable admissions and readmissions. The Center for Managing Chronic Disease has outlined the circles of influence that help manage chronic diseases and avoid complications.

These circles of influence include:

  • Self-management
  • Family
  • Clinical expertise
  • Work/school
  • Community awareness
  • Environment
  • Policy

Chronic diseases are complex problems that lead to higher mortality, utilization of services, and a greater cost. A recent study released by the Centers for Disease Control and Prevention (CDC) concluded that nearly 66% of all adult discharges from community-based hospitals have Multiple Chronic Conditions (MCCs). MCCs are associated with higher numbers of avoidable admissions and hospitalizations, and increase the risk of readmissions. Moreover, rates of avoidable admissions, hospitalizations, and readmissions are compounded by payer type, race, sex, and age indicating the complex nature of MCCs and the interplay with racial and socioeconomic factors.

Nearly 66% of all adult discharges from community-based hospitals have multiple chronic conditions.

As our population ages and chronic conditions are compounded, managing individuals with one or multiple illnesses will take an even more central role. Finding ways to predict possible readmission risks and complications to drive interventions and self-management, help improve overall health while reducing the risk of hospitalization.

The good news is that evidence strongly suggests that tailored interventions are not only feasible, they are highly effective at reducing admissions, length of stay, and avoidable readmissions for these individuals.

References

National Council on Aging: Chronic Disease Fact Sheet

The Center for Managing Chronic Disease: What is Chronic Disease

Steiner CA, Friedman B. Hospital Utilization, Costs, and Mortality for Adults With Multiple Chronic Conditions, Nationwide Inpatient Sample, 2009.