Identifying specific, disease level risks by patient so that care givers can reduce the number of hospital acquired conditions, improve health outcomes, and save clinical and financial resources
The US ranks 1st in healthcare spending and 37th globally in the quality of care delivered. In fact, we spend $4.6 trillion each year on healthcare. Of that spend, we waste $585 billion on unnecessary, error-ridden, and inefficiently delivered service. All of this waste has contributed to the number three cause of death in the US: medical errors. This means that more than 250,000 people die each year because of preventable event.
Jvion's Cognitive Clinical Success Machine delivers a high-definition view into the future state of a patient's health. It provides a holistic portrait of the patient including the likelihood that a patient will suffer from an avoidable event such as sepsis or a pressure ulcer. This portrait encompasses not just the patient’s risk, but also the recommended actions that will deliver the best health outcomes.
Jvion's tool is fast, it can be stood up in two weeks and fire future patient risk views in any frequency - even real time. And the tool is specific. It delivers the exact actions that will stop adverse events from happening. The machine accounts for the countless variables that influence if someone will get sick and the preventions that will work best. It has 200x the cognitive capacity of the human mind to assimilate and assess the factors that impact a decision. This means that Jvion's Cognitive Clinical Success Machine can see the impact of the thousands of socioeconomic, environmental, and behavioral factors that play a leading role in health outcomes and risk. It takes these factors, in addition to clinical and genomic data points, to provide clinicians with the most precise view into the future state of a patient's health and the actions that can be taken today to prevent illness, disease, and avoidable events.
Hospital Acquired Conditions (HACs) are conditions that are defined by section 5001(c) of the Deficit Reduction Act of 2005 that are
"a) high cost or high volume or both,
(b) result in the assignment of a case to a DRG that has a higher payment when present as a secondary diagnosis, and
(c) could reasonably have been prevented through the application of evidence‑based guidelines."
These common medical errors add up to more than $4.5 billion in additional spending each year.
HAC scores are based on six quality measures that fall into two domains:
To reduce the number of HAC events, CMS will penalize hospitals who have higher rates of patient complications than their peers. The total HAC score comprises the sum of a hospital's domain scores. For FY 2017, Domain 1 caries a weight of 15% while Domain 2 caries a weight of 85%. Each year, hospitals are reassessed to determine penalties.
Across FY 2016, 758 hospitals were subject to a one percent payment reduction applied to all Medicare discharges that fell between 10/1/2015 and 9/30/2016. The total estimated FY2016 savings for the program is $364 million.
Our solution's artificial intelligence, machine learning platform can help hospitals predict patients at high-risk of hospital acquired conditions and readmissions, and help physicians and clinicians apply targeted patient interventions to minimize risk of penalties. This includes septicemia prevention, pressure ulcer reduction and prevention, and other disease interventions driven by patient disease predictions and disease models. Using Jvion's solution, providers can stop illnesses and conditions before they happen.
For more information on the HAC Reduction Program, please visit CMS at
Find out how Jvion's solution can help you stop those illnesses and conditions that are costing your patients and your bottom line. Contact us at firstname.lastname@example.org.
- Measures: Hospital-Acquired Condition (HAC) Reduction Program https://www.qualitynet.org/dcs/ContentServer?c=Page&pagename=QnetPublic%2FPage%2FQnetTier3&cid=1228774294977
- Methodology: How Hospital-Acquired Conditions Are Calculated; http://www.kaiserhealthnews.org/Stories/2014/June/23/patient-injuries-methodology.aspx
- CMS http://www.medicaid.gov/Federal-Policy-Guidance/downloads/SMD073108.pdf