Diagnostic concordance among nursing clinical decision support system users: a pilot study

Area of Science:

• Nursing Informatics
• Clinical Decision Support Systems
• Nursing Diagnosis

Background:

• Clinical Decision Support Systems (CDSS) aim to standardize nursing documentation.
• The Nanda International, Nursing Intervention Classification, and Nursing Outcome Classification (NNN) Taxonomy provides a standardized framework for nursing diagnoses.
• Assessing concordance in nursing diagnoses is crucial for evaluating the effectiveness of CDSS.

Purpose of the Study:

• To analyze nursing diagnostic concordance among users of the Electronic Documentation System of the Nursing Process of the University of São Paulo (PROCEnf-USP®).
• To evaluate the agreement in documented nursing diagnoses within a specific CDSS structured by the NNN Taxonomy.

Main Methods:

• A pilot, exploratory-descriptive study involving 37 participants (nurses, residents, undergraduates).
• Participants documented clinical case studies in PROCEnf-USP®.
• Nursing diagnoses were extracted and concordance analyzed using Light's Kappa.

Main Results:

• Overall poor concordance was observed across most documented nursing diagnoses (Kappa values ranged from poor to moderate).
• Specific diagnoses like "Risk for unstable blood glucose level" showed higher concordance (K=0.655) compared to others.
• Concordance was limited to only three diagnoses related to biological needs.

Conclusions:

• Low nursing diagnostic concordance suggests potential issues with clinical judgment skills, case study design, or sample size.
• Effective utilization of CDSS in nursing requires strong clinical reasoning skills.
• Further research is needed to improve diagnostic accuracy and system usability.