Reducing Mortality Through the Use of AI-Guided RRT Proactive Rounding

Activity Summary

Required reading for all learners: Implicit Bias impacts patient outcomes

Traditional rapid response teams (RRT) depend on notification by another person/system to identify the patient to be seen emergently. This reactive process can result in the RRT being alerted too late to rescue the patient. Moving to a proactive rounding model, RRTs are able to intervene early to prevent patient deterioration or cardiac arrest. Use of a predictive analytics program alerts the RRT to changes in the patient’s condition in real time to facilitate proactive intervention to reduce mortality.

Objectives

Describe traditional rapid response team models with both reactive and proactive rounding processes.
Compare and contrast existing early warning systems to identify patients at risk of deterioration.
Evaluate existing artificial intelligence algorithms using predictive analytics to identify patients at risk of deterioration.

Continuing Education Disclosure Statement

Criteria for Awarding Contact Hours

Learners must complete the entire activity and the associated evaluation AND read Implicit Bias impacts patient outcomes. No partial credit will be awarded.

Accreditation

American Association of Critical-Care Nurses is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center's Commission on Accreditation.
Provider approved by the California Board of Registered Nursing, Provider number CEP 1036, for {contactHours} contact hours.

Disclosures

The Nurse Planner has determined that no individuals with the ability to control content of this activity have relevant relationships with ineligible companies.

Activities with pharmacotherapeutic credit are to assist the APRN in fulfilling their education requirements for licensure and certification renewals.

Refund Policy

Continuing Education Activities are nonrefundable.