Optimizing Alarm Management in the NICU

Designing Interfaces for supporting nurses to customize Alarm Limits

Master thesis (2023)

Authors

G. CHEN Industrial Design Engineering

Contributors

E. Ozcan Vieira Design Aesthetics - IDE (supervisor 1)
T.G. Goos Medical Instruments & Bio-Inspired Technology - Mechanical, Maritime and Materials Engineering (supervisor 2)
Faculty
Industrial Design Engineering
Copyright: © 2023 GONGYU CHEN
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Published Date

28-08-2023

Language

English

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Faculty

Industrial Design Engineering

Abstract

In the realm of healthcare, the effective management of alarm systems within the Neonatal Intensive Care Unit (NICU) is of paramount importance to ensure patient safety and reduce alarm fatigue among nurses. This study presents a comprehensive exploration of designing interfaces tailored to nurses' needs for adjusting alarm limits, aimed at enhancing their decision-making and patient care. Leveraging a multidisciplinary approach, this project amalgamates insights from nursing practices, user-centered design, and data visualization.

Drawing on meticulous literature review, direct observations, interviews, and user surveys, the project meticulously dissected the intricacies of nurses' interactions with alarm systems. This investigation highlighted the necessity for a dashboard that not only reduces the cognitive load on nurses but also empowers them to make informed decisions based on alarm data. The resulting system interfaces offer data customization and visualization capabilities, facilitating rapid and accurate alarm limit adjustments.

Therefore, this project has generated interfaces that involves tracking, analyzing, and visualizing the data logged by the nurses in response to alarms and their evaluations, thus supports nurses in adjusting alarm limits for an individual patient.

The evaluation of the designed interfaces through two kinds of questionnaires reflects a promising usability score, with nuanced insights about nurses' varying levels of comfort and trust with the interface. Future recommendations include personalized information delivery based on experience levels, Leverage machine learning algorithms and so on.