"SAMC has realized an average 16.75% average reduction in number of pressure injuries per month and a 25.5% average monthly reduction in sepsis using Jvion’s Cognitive Machine."
Southeast Alabama Medical Center (SAMC) is one of the leading provider organizations currently using Jvion’s Cognitive Machine as the system’s AI asset. The organizational goal is to enable quality improvements and lower rates of target diseases and avoidable events. Now, with the machine, clinicians are more effectively targeting patients on a trajectory toward and adverse event, determining if something can be done to change the outcome, and—if so—identifying the most effective interventions that will mitigate a patient’s risk. The Cognitive Machine is used by nurses, physicians, and other clinical resources to drive prevention efforts earlier in the episode of care before signs or symptoms are present. As a result, SAMC is enabling the true primary prevention of events and conditions that are traditionally treated with secondary prevention efforts including inpatient sepsis and pressure injuries.
Cognitive Machine Application
SAMC is using the power of the Cognitive Machine to target pressure injuries and inpatient sepsis within the inpatient population. Using each individual patient’s current clinical data, clinical history, and socio-economic information, the machine is able to answer hundreds of questions about a patient’s health and the actions that will most effectively reduce the risk of an adverse, avoidable event. Clinicians use the risk propensity outputs and recommended actions to drive more effective and efficient prevention efforts earlier in the episode of care. The ability to see who is at risk well before any clinical signs are present is helping SAMC shift prevention efforts from secondary to primary prevention thereby improving outcomes and reducing resource demands.
Ensuring Adoption Success
At SAMC, clinical leadership owned the adoption of the Cognitive Machine. They ensured that the right groups were engaged and sought change agents who would be able to effectively communicate the value and role of the AI asset. And SAMC nuanced the approach to implementation based on the adverse event/clinical target.
For pressure injuries, SAMC evaluated the potential impact to the patient population and the provider’s capacity to apply the Cognitive Machine’s recommended interventions. They determined that pressure injuries had the greatest potential for improved quality outcomes and that the internal program already in place would be open to new technology solutions.
For sepsis, physician and nursing leadership collaborated to develop clinical workflows and order sets that could be applied to patients identified as high and medium risk of sepsis. This shift in mindset and clinical approach from early identification of sepsis to preventing the procession toward sepsis by ensuring that the right stakeholders and leaders were aligned and engaged throughout the process.
SAMC is realizing tremendous value from the Jvion machine.