Implementation of Hospital-to-Home Model for Nutritional Nursing Management of Patients with Chronic Kidney Disease Using Artificial Intelligence Algorithm Combined with CT Internet

Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study

Background

The healthcare landscape is rapidly evolving, with artificial intelligence (AI) emerging as a transformative force. In this context, understanding the viewpoints of nursing professionals regarding the integration of AI in future nursing care is crucial.

Aims

This study aimed to provide insights into the perceptions of nursing professionals regarding the role of AI in shaping the future of healthcare.

Methods

A cohort of 23 nursing professionals was recruited between April 7, 2023, and May 4, 2023, for this study. Employing a thematic analysis approach, qualitative data from interviews with nursing professionals were analyzed. Verbatim transcripts underwent rigorous coding, and these codes were organized into themes through constant comparative analysis. The themes were refined and developed through the grouping of related codes, ensuring an authentic representation of participants' viewpoints.

Results

After careful data analysis, ten key themes emerged including: (I) Perceptions of AI readiness; (II) Benefits and concerns; (III) Enhanced patient outcomes; (IV) Collaboration and workflow; (V) Human-tech balance: (VI) Training and skill development; (VII) Ethical and legal considerations; (VIII) AI implementation barriers; (IX) Patient-nurse relationships; (X) Future vision and adaptation.

Conclusion

This study provides valuable insights into nursing professionals' perspectives on the integration of AI in future nursing care. It highlights their enthusiasm for AI's potential benefits while emphasizing the importance of ethical and compassionate nursing practice. The findings underscore the need for comprehensive training programs to equip nursing professionals with the skills necessary for successful AI integration. Ultimately, this research contributes to the ongoing discourse on the role of AI in nursing, paving the way for a future where innovative technologies complement and enhance the delivery of patient-centered care.

Development of standardized nursing terminology for the process documentation of patients with chronic kidney disease

Introduction

European Nursing care Pathways (ENP) is a professional care language that utilizes software to map care processes and utilize the data for research purposes, process control, and personnel requirement calculations. However, there is a lack of internationally developed terminology systems and subset specifically designed for the nutritional management of CKD. The aim of this study was to create a subset of the standardized nursing terminology for nutrition management in patients with chronic kidney disease (CKD).

Materials and methods

According to the guidelines for subset development, four research steps were carried out: (i) Translation of version 3.2 of the ENP (chapter on kidney diseases) and understanding of the framework structure and coding rules of the ENP; (ii) Identification of relevant six-dimensional nursing terms; (iii) Creation of a framework for the subset; (iv) Review and validation by experts.

Results

A subset for CKD nutritional care was created as part of this project, comprising 630 terms, with 17 causal relationships related to nursing diagnoses, 115 symptoms, 31 causes, 34 goals/outcomes, 420 intervention specifications and 13 resources, including newly developed care terms. All terms within the subset have been created using a six-step maintenance procedure and a clinical standard pathway for nutrition management in the SAPIM mode.

Implications for nursing practice

This terminology subset can facilitate standardized care reports in CKD nutrition management, which is used to standardize nursing practice, quantify nursing, services, guidance on care decisions, promoting the exchange and use of CKD nutrition data and serve as a reference for the creation of standardized subset of nursing terminology in China.

H2H rehabilitation care promotes high quality recovery of patients with lung cancer comorbid with chronic obstructive pulmonary disease

H2H is a patient and family-centered approach that integrates hospital and home care, emphasizing continuity, individualized rehabilitation training, and the active participation of patients and their families. However, it is still unclear whether H2H improves the efficacy for patients with COPD and lung cancer. This study investigated the efficacy of Hospital-to-Home (H2H) rehabilitation nursing for lung cancer patients with Chronic Obstructive Pulmonary Disease (COPD). We conducted a retrospective analysis to the clinical data of 95 patients treated in the Pingdingshan University Medical College from January 2018 to January 2020. We compared the effects of conventional nursing (control group, n=45) and H2H nursing (observation group, n=50) on the clinical efficacy for the patients. In this study, after nursing intervention, the quality of life and adverse emotions in the observation group were significantly improved compared to the control group (P<0.0001). Moreover, the lung function and blood oxygen saturation of patients in the H2H nursing model improved after the intervention (P<0.0001). In addition, there was no difference in the 3-year survival rate between the control group and the observation group (P=0.260). Multivariate COX regression analysis showed that the nursing scheme had no effect on the patients' 3-year survival, but the SAS score, SDS score, and CEA were independent prognostic factors affecting the 3-year survival rate (P<0.05). These results demonstrate that H2H rehabilitation care significantly improves the quality of life, emotional health, and lung function of patients with COPD and lung cancer, but does not affect the patients' 3-year survival rate.

Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings

AIMS AND OBJECTIVES

To examine the nature and use of automation in contemporary clinical information systems by reviewing studies reporting the implementation and evaluation of artificial intelligence (AI) technologies in healthcare settings.

METHOD

PubMed/MEDLINE, Web of Science, EMBASE, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies reporting evaluation of AI in clinical settings from January 2021 to December 2022. We documented the clinical application areas and tasks supported, and the level of system autonomy. Reported effects on user experience, decision-making, care delivery and outcomes were summarised.

RESULTS

AI technologies are being applied in a wide variety of clinical areas. Most contemporary systems utilise deep learning, use routinely collected data, support diagnosis and triage, are assistive (requiring users to confirm or approve AI provided information or decisions), and are used by doctors in acute care settings in high-income nations. AI systems are integrated and used within existing clinical information systems including electronic medical records. There is limited support for One Health goals. Evaluation is largely based on quantitative methods measuring effects on decision-making.

CONCLUSION

AI systems are being implemented and evaluated in many clinical areas. There remain many opportunities to understand patterns of routine use and evaluate effects on decision-making, care delivery and patient outcomes using mixed-methods. Support for One Health including integrating data about environmental factors and social determinants needs further exploration.

Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review

Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.