Research on the intelligent internet nursing model based on the child respiratory and asthma control test scale for asthma management of preschool children

BACKGROUND Childhood asthma is a common respiratory ailment that significantly affects preschool children.Effective asthma management in this population is particularly challenging due to limited communication skills in children and the necessity for consistent involvement of a caregiver.With the rise of digital healthcare and the need for innovative interventions,Internet-based models can potentially offer relatively more efficient and patient-tailored care,especially in children.AIM To explore the impact of an intelligent Internet care model based on the child respiratory and asthma control test(TRACK)on asthma management in preschool children.METHODS The study group comprised preschoolers,aged 5 years or younger,that visited the hospital's pediatric outpatient and emergency departments between January 2021 and January 2022.Total of 200 children were evenly and randomly divided into the observation and control groups.The control group received standard treatment in accordance with the 2016 Guidelines for Pediatric Bronchial Asthma and the Global Initiative on Asthma.In addition to above treatment,the observation group was introduced to an intelligent internet nursing model,emphasizing the TRACK scale.Key measures monitored over a six-month period included the frequency of asthma attack,emergency visits,pulmonary function parameters(FEV1,FEV1/FVC,and PEF),monthly TRACK scores,and the SF-12 quality of life assessment.Post-intervention asthma control rates were assessed at six-month follow-up.RESULTS The observation group had fewer asthma attacks and emergency room visits than the control group(P<0.05).After six months of treatment,the children in both groups had higher FEV1,FEV1/FVC,and PEF(P<0.05).Statistically significant differences were observed between the two groups(P<0.05).For six months,children in the observation group had a higher monthly TRACK score than those in the control group(P<0.05).The PCS and MCSSF-12 quality of life scores were relatively higher than those before the nursing period(P<0.05).Furthermore,the groups showed statistically significant differences(P<0.05).The asthma control rate was higher in the observation group than in the control group(P<0.05).CONCLUSION TRACK based Intelligent Internet nursing model may reduce asthma attacks and emergency visits in asthmatic children,improve lung function,quality of life,and the TRACK score and asthma control rate.The effect of nursing was significant,allowing for development of an asthma management model.

Clinical Effect of Intelligent Emergency Nursing Mode in Patients with Severe Traumatic Brain Injury

Objective: Severe traumatic brain injury (sTBI) is one of the common acute and critical diseases in neurosurgery. So we aim to explore the clinical effectiveness of an intelligent emergency care model in patients with severe traumatic brain injury. Methods: Eighty patients with severe traumatic brain injury (sTBI) who were treated in Zhuji People’s Hospital of Zhejiang Province from January 2019 to December 2021 were selected as the study subjects. The patients were divided into an observation group and a control group with 40 patients in each group according to the random number table method. Patients in the control group received conventional first-aid nursing mode intervention, and the intelligent emergency nursing mode was used for the observation group based on the control group. Comparisons were conducted between the two groups on the time of arrival to the emergency room, the time from the emergency room to the operating room, Glasgow Coma Scale (GCS) score before surgery, GCS score when leaving the Intensive Care Unit (ICU), the average length of ICU stay, the average length of hospital stay, the total hospital costs. Results: The time of arrival to the emergency room, the time from the emergency room to the operating room, the average length of ICU stay, the average length of hospital stay, and the total hospital costs in the observation group were significantly lower than those in the control group, and the differences were statistically significant (All P Conclusion: Intelligent emergency nursing mode can shorten the time of sTBI rescue, the length of ICU stay, and the average length of hospital stay, reduce the total hospitalization cost, improve the prognosis, with good efficacy, reduce the total cost of hospitalization, and improve the prognosis with better efficacy.

Scope of technology in health care, special focus on nursing

Integrating artificial intelligence(AI)into health care reshapes nursing practices and education,enhancing patient care and clinical processes.This article discusses the transformative potential of AI in nursing,from streamlining documentation and diagnosis using AI applications to the evolution of nursing.The utilization of AI in primary care through automated communication strategies and the emergence of humanistic AI solutions are explored.As nurses adapt to AI-driven health-care technologies,balancing present needs with future demands becomes imperative.AI provides substantial advantages,but it's crucial to address challenges to ensure the successful integration of technology in healthcare and maintain the delivery of high-quality patient care in our tech-driven healthcare environment.