ArticleDigital health
Development and application of a clinical nursing decision support system for pressure injury in postoperative cardiac surgery patients.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence Integration in Multidisciplinary Wound Management: A Scoping Review of Barriers and Facilitators in Clinical Workflows.Journal of multidisciplinary healthcare · 2026Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop a clinical nursing decision support system for pressure injury and explore its application in managing pressure injury in postoperative cardiac surgery patients. Methods: A multidisciplinary research team was formed to develop a clinical nursing decision support system. Key indicators, including wound assessment accuracy, wound treatment accuracy, pressure injury healing rate, pressure injury incidence, and defect rates in nursing records, were compared before and after the clinical nursing decision support system utilization. Count data were described using frequency and composition ratio (%), and comparisons were made using the chi-square test or Fisher's exact probability method. Measurement data following a normal distribution were described by mean and standard deviation, while non-normally distributed data were described by median and interquartile range. Independent sample t-tests and rank-sum tests were used for between-group comparisons. A significance level of α = 0.05 was set, with results considered statistically significant if P < 0.05. Results: The clinical nursing decision support system implements an intelligent decision-making engine and interactive dashboard for human-computer interaction, enabling intelligent assessment and decision-making, re-evaluation reminders, interactive modules, intelligent auditing, and a three-level quality control system for pressure injury. After applying the clinical nursing decision support system, the pressure injury incidence in postoperative cardiac surgery patients decreased from 14.8% to 12.8%, with no statistically significant difference (P > 0.05). The pressure injury healing rate increased from 89.1% to 97.2%, wound assessment accuracy improved from 90.8% to 97.2%, and wound treatment accuracy increased from 88.3% to 96.5%. The defect rate in nursing records decreased from 15.3% to 7.7%, with all differences being statistically significant (P < 0.05). Conclusion: This study successfully developed and implemented a clinical nursing decision support system for pressure injury management in postoperative cardiac surgery patients. These results confirm the system's clinical utility in standardizing pressure injury care, optimizing nursing workflows, and elevating documentation quality. The clinical nursing decision support system provides an effective tool for enabling evidence-based, personalized interventions and strengthening closed-loop quality control in pressure injury management.
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