By Dr. John Showalter, MD, MSIS, Chief Product Officer, Jvion.
COVID-19 catalyzed a rapid shift to telehealth that was years in the making. Reimbursement, once a barrier to adoption, was overcome when CMS announced that Medicare would cover telehealth to allow socially-distant care to continue. As a result, 69% of all patient encounters were done via telehealth in April, with that proportion even higher in areas with severe outbreaks of COVID-19.
Today, April feels like a lifetime ago, and telehealth accounts for only 21% of visits. But the consensus is clear: telehealth is here to stay.
A recent survey found almost 70% of providers were more motivated to continue using telehealth after the pandemic, citing better access to care (68%), more timely care (83%), improved patient health (60%), and improved financial health for their practices (57%). And now that CMS has permanently expanded telehealth coverage, any uncertainty over the long-term financial viability of telehealth can be put to rest.
Of course, not everything can be done via telehealth. Telehealth works great for chronic disease management, behavioral health, hospital/ED follow-ups and preventative care, but there are many procedures that can only be done in person. How then can providers determine which patients should be seen in person and who can be seen via telehealth?
This question is now more urgent than ever, as hospitals nationwide confront a surge in patients admitted with Covid-19. To manage capacity and keep patients safe, providers will want to see patients virtually whenever possible.
Deferred care is another concern for providers. Some 41% of US adults deferred medical care they needed this year to avoid the coronavirus. To prevent these patients from deteriorating and suffering worse health outcomes in the future, it’s critical that providers re-engage with these patients as soon as possible before it’s too late. Telehealth is often the safest way to do so.
Jvion helps healthcare systems prevent patient harm and associated costs by enabling clinical staff to focus attention, resources, and individualized interventions on patients whose outcomes can be improved.
Jvion pinpoints the impactable patients who are on a risk trajectory that can be changed and provides the patient-specific recommendations that will drive to a better outcome. The Jvion Machine is a combination of Eigen-based mathematics, dataset of more than 30 million patients, and software that can be quickly applied to any of 50 preventable harm vectors (such as sepsis, readmissions, falls, avoidable ER visits, and pressure injuries) without the need to create new models or to have perfect data.
Jvion has worked in clinical settings for nearly a decade, with hospitals reporting average reductions of 30% in preventable harm incidents and avoidable cost savings of .3 million a year.
Jvion is based in Suwanee, Georgia.
What is the single-most innovative technology you are currently delivering to health systems or medical groups?
Jvion’s most innovative technology is its clinical AI platform, the Care Optimization and Recommendation Enhancement (CORE), an asset that empowers healthcare providers and payers and other healthcare organizations with the insight to proactively identify and address avoidable patient harm and lower costs.
The CORE can be applied across many use cases like Social Determinants of Health, Behavioral Health, Oncology, Hospital Acquired Conditions and Infections, Avoidable Utilization and many more. Jvion’s CORE pinpoints patients on a trajectory towards an adverse clinical outcome — based on a combination of clinical, socioeconomic, behavioral and environmental factors — and recommends the personalized evidence-based interventions most likely to improve outcomes for each patient’s unique needs.
How is your product or service innovating the work being done in these organizations to provide care or make systems run smoother?
With more than 4,500 factors analyzed per patient, the CORE can identify at-risk patients missed by traditional predictive analytics. And rather than simply assigning patients a risk score, the CORE identifies why patients are at risk, and recommends clinically-validated interventions personalized to reduce each patient’s risk.
The CORE can also reduce alert fatigue and the physician burnout that comes with the utilization of traditional stratification analytics. Most predictive analytics don’t provide any insight on whether high-risk patients outcomes can be improved, or how to improve it, which leads to overwhelming patient lists and risk alerts. The CORE reduces these alerts by focusing on the modifiable patients whose outcomes are most likely to be improved with the right intervention, and providing actionable insights that empower clinicians to intercept and course-correct.
What is the primary need fulfilled by the product or service?
As the industry continues to drive value-based care, there is a need to more accurately and efficiently identify patients that can be impacted through proactive intervention and, more specifically, how to intervene for each patient. To that end, Jvion’s mission is to primarily address the pervasive problem of preventable patient harm (defined as avoidable adverse clinical events or outcomes), which affects 1 in 20 patients and costs over $244 billion in avoidable medical expenses annually.
Jvion’s clinical AI can predict patient risk for a wide range of specific preventable harm incidents including sepsis, pressure injuries, falls, and hospital acquired infections and provide prioritized evidence-based recommendations to drive the best outcome for each individual.
What is the ROI of said product or service?
To date, the Jvion CORE AI solution has been deployed across about 50 health systems and over 300 hospitals, which report average reductions of 30% for admissions, 20% for readmissions, and average annual cost savings of $13.7 million.
Provide real examples of verifiable ROI of the product or service when used in or by a health system or medical group.
Baptist Health: Avoidable admissions and readmissions plague hospitals nationwide, and Provider-Sponsored Health Plans (PSHP) — health plans that are owned by a health system, physician group, or hospital — such as Baptist Health, are particularly vulnerable.
Over the course of two years, Baptist Health saved more than $13M by targeting and intervening on those covered employees at risk of an avoidable ER or inpatient visit. Using the Jvion CORE, Baptist Health was able to better identify at-risk individuals and take the clinical actions that would keep them healthy and out of the hospital. They also achieved an 18% drop in readmissions over two years.
Artificial intelligence (AI) is transforming healthcare, especially on its clinical side, where 62% of providers have already adopted an AI strategy. The American Hospital Association recently reported that when successfully implemented, clinical AI can improve patient outcomes and lower costs at each stage of the care cycle, from prevention and detection to diagnosis and treatment. Expect to see continued growth in AI adoption in 2020.
Here are a few of the most exciting AI trends to watch for:
Social Determinants of Health (SDoH) will become a core focus for healthcare AI solutions: AI has significant potential for helping reduce socioeconomic barriers to care. Across 2019, we have seen investment by CMS in the Accountable Health Communities Model, which is the first model to include social determinants of health. This model codifies what we already know — that socioeconomic factors influence an individual’s health and risk. Emerging AI technologies are actively creating value for patients by helping to make sense of large socioeconomic and environmental datasets, driving meaningful investments and action that will help to prevent avoidable utilization and guide effective distribution of community resources.
We will start to move into the “Slope of Enlightenment” within the Hype Cycle: Healthcare AI will move out of the “Trough of Disillusionment” as more evidence of AI’s ability to improve health outcomes emerge. AI-related topics will continue to gain prominence in research and the media. And the results of funded projects and pilots will become available to the broader industry.
The AI discussion will broaden beyond imaging and natural language processing: With the exponential increase in patient data, it’s only logical that it’s time for AI – the best way to synthesize that data – to have its moment. While imaging and natural language processing have dominated the healthcare AI conversation over the past few years, 2020 will mark an expanded understanding of AI solutions to include those focused on clinical decision support. These solutions integrate into existing clinical workflows to help direct resources to modifiable patients at risk of a target adverse event. Expect to see more investment and discussion around these solutions across 2020.