Date of Graduation
Fall 12-11-2020
Document Type
Project
Degree Name
Doctor of Nursing Practice (DNP)
College/School
School of Nursing and Health Professions
Department/Program
Nursing
Program
Executive Leader DNP
First Advisor
Priscilla Javed, DNP, RN, FACHE
Second Advisor
Dr. Juli Maxworthy DNP, PhD(c), MSN/MBA, RN, CNL, CPHQ, CPPS, CHSE, FNAP, FSSH
Abstract
Hospitals with strong and consistently activated rapid response teams (RRTs) have significantly fewer cardiac arrests. Early recognition of clinical deterioration supports the timely activation of RRTs, which increases earlier assessment and intervention. Current early warning tools are not sufficient and reliable for recognizing patient deterioration, and they are evolving, incorporating artificial intelligence (AI) to identify clinical decline much earlier. The project organization had previously implemented the medical early warning score tool into the RRT nurses’ practice to prioritize patient assessments, but this was not sustained due to its unreliability in identifying patients at risk.
Aiming to reduce the number of in-hospital cardiac arrests by implementing AI to recognize and notify the RRT of patient deterioration, the primary key performance indicator was the number of in-hospital cardiac arrests outside the intensive care setting. Outcomes data also included the number of rapid responses pre- and post-implementation. Qualitative data were collected from the project team and RRT nurses during the implementation and self-assessment.
Outcomes showed decreased cardiac arrests from 13 to 9, but the pre- and post-intervention cardiac arrest rate remained the same at 7.2%. The number and rate of rapid responses increased as expected based on previous evidence from 1.04 to 1.25 per day, indicating that the addition of AI technology stimulated recognition of patient deterioration. With more time and data as we continue to improve AI implementation, we can better understand the true effect. Future utilization of AI technology to support faster, more reliable clinical warnings should be considered.
Recommended Citation
Potolsky, Alicia, "Implementation of Artificial Intelligence Initiated Rapid Responses to Reduce In-Hospital Cardiac Arrest" (2020). Doctor of Nursing Practice (DNP) Projects. 233.
https://repository.usfca.edu/dnp/233
