Jvion helps healthcare systems and providers prevent harm and lower costs through its AI-enabled prescriptive analytics solution. An industry first, the Jvion Machine™ goes beyond simple predictive analytics and machine learning to identify patients on a trajectory to becoming high risk and for whom intervention will likely be successful. Jvion determines the interventions that will more effectively reduce risk and enable clinical action. And it accelerates time to value by leveraging established patient-level intelligence to drive engagement across hospitals, populations, and patients.

Preventable harm is preventable. 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. Unlike traditional AI and predictive analytic solutions that merely identify high-risk patients and cause alarm fatigue, 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 16 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. As the leader in prescriptive analytics for preventable harm, Jvion has proven effective in clinical settings for nearly a decade, with hospitals reporting average reductions of 30% in preventable harm incidents and avoidable cost savings of $6.3 million a year.

Patients placed in the wrong bed status with improper documentation results in massive revenue loss and patient dissatisfaction.

Physicians can’t keep up with constantly changing criteria needed to admit patients to the hospital, and hospitals spend tons of money and resources fixing bed status issues retrospectively.

AdmissionCare provides the admitting physician with automated admission criteria - such as MCG - integrated directly into the EHR workflow to help document medical necessity that increases payer reimbursements and reduce denials.

How does it work?

• Integrate into the clinician's EHR workflow
• Determine the most appropriate bed status for each patient at admission
• Synchronize payer criteria with the clinician's documentation
• Collect revenue for the care provided, while avoiding costly denials

EvidenceCare is a unique type of clinical decision support system (CDSS) with its EHR-integrated and content-agnostic platform that empowers better care decisions by improving clinical workflows.

Founded in response to the professional experience of emergency physician Dr. Brian Fengler, the platform provides clinicians with evidence-based insights and measurable outcomes that improve hospital margins.

Based in Nashville, Tennessee, EvidenceCare is a 2x honoree of the INC 5000 list of fastest growing companies, a 2x Fierce Healthcare Best Product winner, and one of Modern Healthcare's Best Places to Work in Healthcare.

EHR Integrated on the front end of the clinician workflow

EHR Integrated on the front end of the clinician workflow