Graduation Semester and Year

Summer 2026

Language

English

Document Type

Dissertation

Degree Name

Doctor of Nursing Practice

Department

Nursing

First Advisor

Dr. Deborah Behan

Abstract

Problem: Clinical deterioration among hospitalized patients remained a significant contributor to preventable morbidity and mortality, often due to delayed recognition and response in general care settings (Choi et al., 2025; Chung & Jung, 2024).

PICOTS: For registered nurses working on a general level of care inpatient hospital unit (P), how did a competency-based training program bundle incorporating didactic educational intervention of learning how to use technology, machine learning (ML) based early warning system (EWS) of trends and patterns to identify at risk patients, and low-fidelity simulation-based education (SBE) (clinical readiness to improve confidence with education and coaching) (I), compared to standard training practices (C), affect unplanned intensive care unit (ICU) transfers from rapid response team (RRT) activations and nurse confidence and technology utilization (O) over an eight-week period (T) in a general care ward of a teaching hospital in North Texas (S)?

Methods: This quality improvement (QI) project utilized the Plan-Do-Study-Act (PDSA) framework to implement a competency-based intervention that integrated didactic instruction, Epic Deterioration Index (EDI) training, and simulation-based training.

Findings: Implementation of the intervention was associated with improvements in nurse knowledge and confidence in recognizing and responding to clinical deterioration. Increased utilization of EWS tools and enhanced situational awareness.

Conclusions: A competency-based training program integrating simulation, artificial intelligence (AI) driven EWS, and nurse-centered technology improved nurse preparedness and supported earlier recognition of patient deterioration.

Keywords

Clinical deterioration, Early warning signs (EWS), Epic Deterioration Index (EDI), Simulation-based education (SBE), Failure to rescue (FTR), Rapid response team (RRT), Machine learning (ML) in healthcare, Quality improvement (QI), Educational intervention, Low fidelity simulation

Disciplines

Adult and Continuing Education | Critical Care Nursing | Educational Technology | Emergency Medicine | Nursing

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