Optimizing ICU Care: Advancing Nursing Diagnoses with a Decision Support System.

Area of Science:

• Nursing Informatics
• Clinical Decision Support Systems
• Critical Care Nursing

Background:

• The Nursing Process (NP) is crucial for evidence-based patient care, with Nursing Diagnoses (NDs) forming a foundational step.
• Optimizing NP in high-acuity environments like Intensive Care Units (ICUs) presents unique challenges.
• Existing methods for ND identification can be time-consuming and may not always capture the complexity of ICU patient needs.

Purpose of the Study:

• To develop and evaluate VIGIL Care, a clinical decision support system (CDSS) aimed at improving the accuracy and efficiency of NP in ICUs.
• To refine a curated list of relevant nursing diagnoses and their indicators for ICU settings.
• To assess the system's performance in proposing patient-specific NDs and supporting nursing autonomy.

Main Methods:

• A Delphi method was employed with 13 experts to select 32 high-criticality/prevalence NDs from the NANDA-I taxonomy (2024-2026) for ICU care.
• A panel refined diagnostic indicators for the selected NDs, identifying highly or moderately relevant ones.
• VIGIL Care was developed, integrating patient data with logical rules to suggest NDs, featuring an explainable user interface.

Main Results:

• VIGIL Care demonstrated 86% sensitivity and 78% specificity in assessing 10 NDs across 20 patient cases.
• The system outperformed traditional nursing assessments, particularly under time constraints.
• The developed system supports nursing decisional autonomy through its explainable interface.

Conclusions:

• VIGIL Care represents a promising advancement in CDSS for enhancing NP in ICUs.
• The system's ability to identify relevant NDs efficiently suggests potential for improved patient outcomes.
• Further validation with larger sample sizes is recommended to confirm the system's effectiveness and generalizability in critical care settings.