Objectives: Nursing documentation consumes approximately 30% of nurses’ professional time, making improvements in efficiency essential for patient safety and workflow optimization. This study compares traditional nursing documentation methods with a generative artificial intelligence (AI)-based system, evaluating its effectiveness in reducing documentation time and ensuring the accuracy of AI-suggested entries. Furthermore, the study aims to assess the system’s impact on overall documentation efficiency and quality. Methods: Forty nurses with a minimum of 6 months of clinical experience participated. In the pre-assessment phase, they documented a nursing scenario using traditional electronic nursing records (ENRs). In the post-assessment phase, they used the SmartENR AI version, developed with OpenAI’s ChatGPT 4.0 API and customized for domestic nursing standards; it supports NANDA, SOAPIE, Focus DAR, and narrative formats. Documentation was evaluated on a 5-point scale for accuracy, comprehensiveness, usability, ease of use, and fluency. Results: Participants averaged 64 months of clinical experience. Traditional documentation required 467.18 ± 314.77 seconds, whereas AI-assisted documentation took 182.68 ± 99.71 seconds, reducing documentation time by approximately 40%. AI-generated documentation received scores of 3.62 ± 1.29 for accuracy, 4.13 ± 1.07 for comprehensiveness, 3.50 ± 0.93 for usability, 4.80 ± 0.61 for ease of use, and 4.50 ± 0.88 for fluency. Conclusions Generative AI substantially reduces the nursing documentation workload and increases efficiency. Nevertheless, further refinement of AI models is necessary to improve accuracy and ensure seamless integration into clinical practice with minimal manual modifications. This study underscores AI’s potential to improve nursing documentation efficiency and accuracy in future clinical settings.

Objectives: Nursing documentation consumes approximately 30% of nurses’ professional time, making improvements in efficiency essential for patient safety and workflow optimization. This study compares traditional nursing documentation methods with a generative artificial intelligence (AI)-based system, evaluating its effectiveness in reducing documentation time and ensuring the accuracy of AI-suggested entries. Furthermore, the study aims to assess the system’s impact on overall documentation efficiency and quality. Methods: Forty nurses with a minimum of 6 months of clinical experience participated. In the pre-assessment phase, they documented a nursing scenario using traditional electronic nursing records (ENRs). In the post-assessment phase, they used the SmartENR AI version, developed with OpenAI’s ChatGPT 4.0 API and customized for domestic nursing standards; it supports NANDA, SOAPIE, Focus DAR, and narrative formats. Documentation was evaluated on a 5-point scale for accuracy, comprehensiveness, usability, ease of use, and fluency. Results: Participants averaged 64 months of clinical experience. Traditional documentation required 467.18 ± 314.77 seconds, whereas AI-assisted documentation took 182.68 ± 99.71 seconds, reducing documentation time by approximately 40%. AI-generated documentation received scores of 3.62 ± 1.29 for accuracy, 4.13 ± 1.07 for comprehensiveness, 3.50 ± 0.93 for usability, 4.80 ± 0.61 for ease of use, and 4.50 ± 0.88 for fluency. Conclusions Generative AI substantially reduces the nursing documentation workload and increases efficiency. Nevertheless, further refinement of AI models is necessary to improve accuracy and ensure seamless integration into clinical practice with minimal manual modifications. This study underscores AI’s potential to improve nursing documentation efficiency and accuracy in future clinical settings.

Objectives: Nursing documentation consumes approximately 30% of nurses’ professional time, making improvements in efficiency essential for patient safety and workflow optimization. This study compares traditional nursing documentation methods with a generative artificial intelligence (AI)-based system, evaluating its effectiveness in reducing documentation time and ensuring the accuracy of AI-suggested entries. Furthermore, the study aims to assess the system’s impact on overall documentation efficiency and quality. Methods: Forty nurses with a minimum of 6 months of clinical experience participated. In the pre-assessment phase, they documented a nursing scenario using traditional electronic nursing records (ENRs). In the post-assessment phase, they used the SmartENR AI version, developed with OpenAI’s ChatGPT 4.0 API and customized for domestic nursing standards; it supports NANDA, SOAPIE, Focus DAR, and narrative formats. Documentation was evaluated on a 5-point scale for accuracy, comprehensiveness, usability, ease of use, and fluency. Results: Participants averaged 64 months of clinical experience. Traditional documentation required 467.18 ± 314.77 seconds, whereas AI-assisted documentation took 182.68 ± 99.71 seconds, reducing documentation time by approximately 40%. AI-generated documentation received scores of 3.62 ± 1.29 for accuracy, 4.13 ± 1.07 for comprehensiveness, 3.50 ± 0.93 for usability, 4.80 ± 0.61 for ease of use, and 4.50 ± 0.88 for fluency. Conclusions Generative AI substantially reduces the nursing documentation workload and increases efficiency. Nevertheless, further refinement of AI models is necessary to improve accuracy and ensure seamless integration into clinical practice with minimal manual modifications. This study underscores AI’s potential to improve nursing documentation efficiency and accuracy in future clinical settings.

Coronavirus disease 2019 (COVID-19), a multi-organ disease impacting the respiratory system and various organs, has recently been linked to diverse cutaneous manifestations. COVID toes, a cutaneous sign of COVID-19 infection, is relatively common in children and young adults, although its clear association with COVID-19 has not been widely reported. This report presents the case of a 1-year-old infant with COVID toes. The patient exhibited violaceous discoloration in the distal toes. Further, the patient exhibited no symptoms of COVID-19 infection and the enzyme-linked immunosorbent assay was negative for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2); therefore, the patient was initially diagnosed with frostbite. However, the infant’s condition deteriorated despite treatment with nonsteroidal anti-inflammatory drugs and a warm-water bath. After a skin biopsy and serum SARS-CoV-2 test, the patient was diagnosed with COVID toes and treated with systemic steroids, photobiomodulation therapy, and dressing. This case underscores the importance of recognizing chilblain-like lesions in pediatric patients during the COVID-19 pandemic, emphasizing the need for awareness of COVID toes among healthcare professionals.

Angiosarcoma is a rare yet aggressive tumor that originates from endothelial cells lining the blood or lymphatic vessels. Angiosarcoma is commonly associated with radiotherapy, often developing as a secondary cancer following irradiation. In this report, we present a case of a 66-year-old woman who underwent a lumpectomy with adjuvant radiotherapy for left breast cancer 9 years ago. She was referred to our clinic due to erythematous nodules and papules on the left breast that developed 10 months prior. A cutaneous biopsy revealed irregular, anastomosing vessels lined by crowded swollen endothelial cells, exhibiting nuclear atypia. The immunohistochemical stains for alpha-smooth muscle actin and CD31 were positive, while the ki-67 index was elevated. However, the stain was negative for human herpesvirus 8. Clinical and histopathological features were consistent with angiosarcoma associated with adjuvant radiotherapy. The patient underwent a left total mastectomy with sentinel lymph node biopsy. However, 18 months later, multiple bone metastases were noted on positron emission tomography-computed tomography, and the patient received palliative radiotherapy and supportive care.

Epidermolysis bullosa simplex with mottled pigmentation (EBS-MP) is an autosomal dominant disease characterized by nonscarring blistering after minor trauma, reticulated pigmentation, and palmoplantar hyperkeratosis. EBS-MP is caused by a mutation in the KRT5 or KRT14 gene encoding the keratinocyte intermediate filament. A 14-year-old girl presented with reticulated hyperpigmentation of the trunk and both extremities, which was observed 9 years ago.Additionally, punctate hyperkeratotic papules were observed on both the palms and soles. She had a history of being diagnosed with EBS as a baby. Skin biopsies were performed on both the hyperpigmented and hyperkeratotic papules. Based on the clinical and histopathological findings, the patient was diagnosed with EBS-MP, and next-generation sequencing was performed. Genetic screening identified a p.P25L mutation in the KRT5 gene.Herein, we report a case of p.P25L mutation in the KRT5 gene in a patient with EBS-MP.

Congenital cutaneous candidiasis (CCC) is a rare disease caused by Candida spp. that occurs within the first six days of life. Its exact pathogenesis remains unclear; however, the suspected pathomechanisms include maternal vulvovaginal candidiasis and ascending infections. A preterm, 1,550-g male infant presented with generalized maculopapules and pustules on his whole body. The patient’s mother had undergone cervical cerclage at a gestational age (GA) of 29 weeks due to an incompetent internal os of the cervix. The pregnancy was terminated at GA 37-week because the mother developed chorioamnionitis. We performed a potassium hydroxide microscopic examination, skin biopsy, and fungal culture test on the baby. Microscopic examination of the skin scrapings revealed pseudohyphae with yeasts, and Candida albicans was identified in the culture test. Maternal placental biopsy revealed fungal organisms, and the baby was diagnosed with CCC due to an ascending infection. The skin lesions completely disappeared after intravenous liposomal amphotericin B treatment.