The application of a simple blood test to predict prognosis in acute heart failure (AHF) patients is not well established. Neutrophil-lymphocyte ratio (NLR) is inexpensive and easy to obtain in hospitalized patients using a routine blood test. We evaluate the prognostic implications of NLR as an independent predictor of in-hospital and long-term mortality in AHF patients. Among 5625 patients enrolled in the Korean Acute Heart Failure registry, 5580 patients were classified into quartiles by their NLR level, and analyzed for in-hospital and post-discharge three-year mortality. Patients in the highest NLR quartile had the highest in-hospital and post-discharge three-year mortality. The same results were seen by dividing the aggravating factor into the infection or ischemia group and the non-infection or non-ischemia group. For patients aggravated from infection or ischemia, a cut-off NLR value was 7.0 that increase the risk of in-hospital and post-discharge three-year mortality. In subgroups of patients not aggravated from infection or ischemia, a cut-off NLR value was 5.0 that increase the risk of in-hospital and post discharge three-year mortality. Elevated NLR in AHF patients at the index hospitalization is an independent predictor for in-hospital and post-discharge three-year mortality. Taken together, NLR is a marker for risk assessment of AHF patients.

Aims: The aim of this study was to identify the effects of a 24‐week interactive text message‐based mobile health intervention (called) on enhancing the self‐care behaviours of patients with heart failure.Background: Whether text message‐based mobile health intervention can be used to improve long‐term adherence to self‐care behaviours among heart failure patients remains unclear.Design: A quasi‐experimental study with a pretest–post‐test design and repeated measures.Methods: Data from 100 patients (mean age, 58.78 years; 83.0% men) were analysed. The intervention group (n = 50) used the program over 24 weeks, which consisted of weekly goal setting and interactive text messaging, while the control group (n = 50) received usual care. Trained research assistants collected data using self‐reported Likert questionnaires. Primary (self‐care behaviours) and secondary (health literacy, eHealth literacy, and disease knowledge) outcome variables were measured at baseline and at 1, 3 and 6 months after intervention for follow‐up.Results: The findings showed that the intervention group demonstrated significantly better self‐care behaviours than the control group during the 6 months. Notably, the trajectory of self‐care behaviours of the patients in the intervention group showed a steep rise between the first‐ and third‐month follow‐up, followed by high stability between the third‐ and sixth‐month follow‐up. In addition, the intervention group had significantly higher disease knowledge than the control group at the first‐ and sixth‐month follow‐up.Conclusions: We found that the program, as an interactive text messaging service, may be an optimal strategy for improving long‐term adherence to self‐care behaviours through motivating and providing social support.Relevance to the Nursing Practice: The WithUs program can help nurses and other healthcare professionals to track patients' health indicators such as symptom severity, diet and physical activity. In addition, nurses can take an important role in evaluating the efficacy of the app in relation to patients' health outcome.Patient or Public Contribution: Patients have completed a self‐reported questionnaire after providing informed consent.

Background: A patient–family caregiver dyad approach is necessary to improve adherence to self-care behaviors by patients with heart failure (HF). However, there is a lack of mobile health (mHealth) interventions that engage both patients and their family caregivers to promote HF self-care. Objective: The purpose of this quasi-experimental study was to develop and confirm the feasibility and effectiveness of a novel mHealth intervention based on patient–family caregiver dyads for promoting adherence to self-care by patients with HF. Methods: We developed a dyadic mHealth program with 2 main features: a basic feature app and an interactive text-based chatbot. The intervention group (35 of 70 HF patient–family caregiver dyads; 50%) underwent a dyadic mHealth program for 24 weeks, while the control group (35 of 70 dyads; 50%) received usual care. Adherence to self-care behaviors, family caregivers’ contributions to self-care behaviors, and health-related quality of life were evaluated. Data were collected using self-administered questionnaires at baseline and at 1 month, 3 months, and 6 months post enrollment. The outcomes were analyzed using intention-to-treat analysis. Results: The intervention group had significantly better adherence to self-care behaviors (β=4.68, 95% CI 0.99-8.37) and family caregivers’ contributions to self-care behaviors (β=8.76, 95% CI 4.63-12.88) over 6 months compared with the control group. The 6-month follow-up health-related quality of life scores for patients (β=0.07, 95% CI 0.00-0.13) and family caregivers (β=0.08, 95% CI 0.03-0.13) were significantly greater in the intervention group than in the control group. The 1-month follow-up disease knowledge scores for patients (β=0.16, 95% CI 0.03-0.29) and family caregivers (β=0.12, 95% CI 0.00-0.25) were significantly greater in the intervention group than in the control group. The intervention also had a significant effect on mutuality at the 1-month follow-up for patients (β=0.11, 95% CI 0.00-0.21) and family caregivers (β=0.15, 95% CI 0.01-0.30). However, health literacy was significantly higher in the intervention group than in the control group only for patients at 1 month (β=0.14, 95% CI 0.04-0.25). The intervention had no significant effects on depressive symptoms, social support with patient and family caregivers, and caregiver burden with family caregivers. Conclusions: This study found that the dyadic mHealth intervention was beneficial for improving patients’ adherence to self-care behaviors and family caregivers’ contributions to self-care behaviors by providing information and motivation and improving health-related quality of life for patients with HF and family caregivers. Further studies should confirm the generalizability, feasibility, and long-term health outcomes of this intervention. Trial Registration: Clinical Research Information Service (CRIS) KCT0008786; https://tinyurl.com/3684ur4r

Objectives: This study aimed to develop and implement robotic process automation (RPA) for identifying missing codes during insurance claim post-review at a tertiary hospital and to evaluate its feasibility and effectiveness Methods: As a single-centre, operational implementation, an RPA system integrated with optical character recognition (OCR) and electronic medical record (EMR) platforms was developed using Blue Prism. The system compared 532 surgical procedure codes with 21 cutting device codes, automatically flagging discrepancies. Accuracy and efficiency were compared with manual review. Results: Between 1 and 31 May 2025, the RPA system analysed 61 claim statements and performed 199 OCR processes. The Google Cloud Vision API (application programming interface) achieved 100% detection accuracy without false positives, while Tesseract yielded lower accuracy. The RPA reduced average processing time from 120 (manual review) to 54 min, representing a 55% efficiency gain. Discussion: RPA reliably automated repetitive, rule-based administrative tasks, improving accuracy and standardisation of insurance claim audits. Secure system architecture ensured compliance with healthcare data protection standards. User-centred development and integration with EMR demonstrated feasibility in complex healthcare workflows. Conclusion: Implementing RPA for insurance claim post-review significantly enhanced efficiency and accuracy, reduced administrative workload and provided a scalable model for digital transformation in healthcare administration.

Vasovagal syncope is a reversible condition that can cause serious events, such as cerebral hemorrhage. Predicting its occurrence is challenging; thus, this study aimed to develop machine learning models that use heart rate variability to predict the occurrence of vasovagal syncope episodes. We investigated 80 patients experiencing syncope who underwent Holter monitoring during the head-up tilt test. Twenty-six machine learning models were examined for the prediction of vasovagal syncope using heart rate variability parameters. We randomly split the subjects into training and testing sets at a 7:3 ratio (training set: 46 positive and 9 negative subjects; testing set: 21 positive and 4 negative subjects). The XGB classifier achieved the highest performance compared with that of the other algorithms (accuracy, 0.72; area under the curve, 0.801; sensitivity, 0.857; specificity, 0.621). When examining the time taken to predict syncope occurrence using the developed prediction model, it was found that the model could forecast an episode approximately 3 min (equivalent to 193.2 beats) prior to the onset of symptoms. The machine learning model, which utilized heart rate variability parameters, was proficient in preemptively predicting vasovagal syncope incidents. In practical applications, this approach may help prevent unexpected events by leveraging the heart rate variability to predict vasovagal syncope.