Introduction

The integration of artificial intelligence (AI) into nursing practice offers a significant opportunity to enhance patient care and support clinical decision-making. This study examines the deployment of a customized generative language model (GPT) as a clinical assistant in outpatient digestive care. The tool aims to streamline access to evidence-based information and promote structured patient follow-up. While its implementation holds considerable potential for improving efficiency and fostering clinical learning, it also presents challenges related to validation, user adoption, and building trust among nursing professionals.

Results & Findings

A digital tool named Digestive Nursing Follow-Up was developed and piloted in the Gastroenterology Departments of two university hospitals in Spain during Q1 2025. The tool was integrated into four clinical domains: inflammatory bowel disease, gastroesophageal reflux disease, irritable bowel syndrome, and advanced endoscopy. The AI assistant was designed to: Collect and organize structured clinical data; Provide evidence-based clinical suggestions aligned with institutional protocols; Facilitate learning and care continuity. Prior to implementation, participating nurses underwent a one-hour training session. The tool was then incorporated into routine consultations, and nurses were surveyed pre- and post-intervention using a 5-point Likert Scale. Open-ended feedback was also collected.

Summary & Discussion

80% of nurses rated the tool as easy to use following training, and 75% reported high satisfaction, citing improved time management and a reduction in repetitive tasks. Positive perceptions centered on enhanced consultation efficiency and clinical safety. However, concerns were expressed regarding the balance between technology and patient-centered care, as well as the need for ongoing training and more personalized outputs.

Conclusion

Among the strengths identified were improved time management, support for clinical decision-making, and enhancement of standardized nursing practice. The main weaknesses were resistance to change and the initial learning curve, which required additional support. Opportunities include the potential of AI to strengthen professional training, enhance professional autonomy, and contribute to the standardization of clinical procedures. The tool reinforces clinical safety by being based on established protocols, and its use can accelerate their implementation and promote continuous learning, helping to consolidate a more efficient care model. The observed technological dependence was more associated with increased consultation efficiency, highlighting the importance of progressive and balanced integration with clinical practice.

References

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Introduction

Acute pancreatitis (AP) is an acute inflammatory autodigestive disorder triggered by dysfunction of pancreatic exocrine activity and represents the most common gastrointestinal emergency in clinical practice. Global data indicate an annual incidence rate of 33.74 per 100,000 population and a mortality rate of 1.16 per 100,000. Despite ongoing optimization of therapeutic strategies, current clinical management primarily relies on supportive care and surgery. Hypertriglyceridemia (HTG) is the second leading etiological factor for AP, accounting for 10.36% of cases. Compared to other causes, HTG-associated acute pancreatitis (HTG-AP) exhibits higher recurrence rates and more severe complications (e.g., pancreatic necrosis and renal insufficiency). With the rising prevalence of HTG, the disease burden of HTG-AP is increasing, yet its specific pathogenic mechanisms remain unclear.

Aims & Methods

This study integrated whole-genome sequencing (WGS) and next-generation sequencing (NGS) to screen for shared mutations in the exonic regions of 5 HTG-AP family members. Candidate genes were identified based on MutationTaster classification and CADD scores. A mouse model carrying the PDLIM2 gene rs145349678 mutation was constructed using CRISPR/Cas9 technology, and an HTG-AP animal model was established. Pancreatic and hepatic histopathology (HE staining) and serological markers (TG, lipase, amylase, IL-6) were analyzed to evaluate the impact of the mutation on pancreatitis severity and hepatic lipid deposition.

Results

A clinical HTG-AP family was identified, with all 5 members exhibiting hypertriglyceridemia, including 2 cases of recurrent pancreatitis. WGS revealed 278 mutation sites across 10 chromosomes (involving 23 genes) in the family, and NGS validation narrowed these to 7 candidate loci. The PDLIM2 rs145349678 mutation (NM_001368120.1:exon9:c.C1055T:p.A352V), located within the LIM domain (which mediates protein localization and signaling transduction), was selected as the target due to cross-species conservation. Experimental results demonstrated that HTG-AP mice with the PDLIM2 mutation exhibited significantly aggravated pancreatic pathological damage (edema, hemorrhage, and inflammatory cell infiltration; *P* < 0.05), elevated serological markers (TG: 2.3 ± 0.4 vs. 1.5 ± 0.3 mmol/L; lipase: 520 ± 68 vs. 320 ± 45 U/L; IL-6: 48.2 ± 6.1 vs. 28.5 ± 4.3 pg/mL; *P* < 0.01 for all), and a 2.3-fold increase in hepatic lipid deposition (*P* < 0.001) compared to controls.

Conclusion

The PDLIM2 rs145349678 mutation exacerbates inflammatory responses and hepatic lipid accumulation in HTG-AP by modulating lipid metabolism. This finding provides new insights into the pathogenesis of HTG-AP and potential therapeutic targets.