Risk Management and Healthcare Policy最新文献

Background: The increasing global burden of chronic conditions demands novel interventions that are both interactive and sustainable. Gamification has emerged as an innovative strategy to enhance patient engagement and self-management in chronic disease care. Although gamification is widely adopted in healthcare, a review of evidence on its effectiveness across various clinical settings remains inconsistent.

Purpose: This review aimed to identify the effectiveness of gamification-based interventions in improving outcomes among patients with chronic diseases.

Methods: A comprehensive systematic literature search was conducted using three major databases: EBSCOhost, PubMed, and Scopus, along with two search engines, including Google Scholar and Sage Journal, without year limitations, following PRISMA 2020 and Cochrane methodological guidelines. Eligible studies were RCTs involving adult patients with chronic illness that implemented gamification interventions. Data were extracted and analysed through qualitative thematic synthesis.

Results: A total of 17 RCTs met the inclusion criteria. Three categories of interventions were identified: (1) active video games for rehabilitation, (2) virtual reality (VR)-based intervention, and (3) digital gamification for education and behaviour change. Across these studies, four consistent outcome domains were identified: physical function improvement, psychological well-being, adherence and self-management, and motivation and engagement. Most studies reported significant improvements in physical function.

Conclusion: Gamification demonstrates multidimensional benefits, integrating physical, psychological, and behavioural improvements within patient-centred digital health frameworks. The success of these interventions depends on aligning game design mechanics with clinical objectives. Future studies should emphasise hybrid, long-term models combining VR, mobile platforms, and clinician feedback systems to enhance sustainability and scalability in chronic disease management.

Objective: To explore the risk factors of urinary tract infection after transurethral bipolar plasmakinetic prostatectomy (TUPKP) in patients with prostatic hyperplasia (BPH), to construct a nomogram model for predicting postoperative urinary tract infection, and evaluate the differentiation and consistency of the model.

Methods: A total of 580 BPH patients who underwent TUPKP between October 2016 and October 2022 were included as the modeling group, and 115 patients treated from November 2022 to November 2024 formed the validation group. Patients were classified into UTI and non-UTI groups based on the occurrence of UTI within 1 month postoperatively. Clinical data were analyzed using univariate and multivariate logistic regression to identify risk factors. A nomogram was constructed using R software, and its performance was assessed with ROC and calibration curves.

Results: The incidence of postoperative UTI in the modeling group was 14.83%. Compared with the non-UTI group, the UTI group had significantly higher age, diabetes prevalence, preoperative catheterization, and routine nursing ratio, along with longer operation and catheterization times, and shorter antibiotic use duration (P<0.05). Multivariate analysis revealed that age (OR=1.061), diabetes (OR=1.889), operation time (OR=1.063), and indwelling catheter time (OR=1.912) were independent risk factors (P<0.05). The nomogram demonstrated good discrimination (AUC=0.825, 95% CI: 0.780-0.869) and calibration (Hosmer-Lemeshow test P=0.390). External validation showed similar performance (AUC=0.818, 95% CI: 0.711-0.925) with good consistency.

Conclusion: Age, diabetes, duration of surgery, and postoperative indwelling catheter time are risk factors for urinary tract infection in patients with benign prostatic hyperplasia undergoing TUPKP. The constructed nomogram model demonstrates good discrimination and consistency.

Purpose: Screening for HIV and Hepatitis C (HCV) is critical in caring for individuals with substance use disorders (SUD). This is particularly relevant in identifying prescribing habits for Pre-Exposure Prophylaxis (PrEP). The objectives were to examine HIV and HCV screening rates, and PrEP prescription among managed care beneficiaries with SUD and without an HIV diagnosis and determine the factors associated with the screenings and PrEP prescriptions.

Methods: We conducted a retrospective cross-sectional analysis (January-December 2021) of managed care claims for beneficiaries who visited either urban primary care clinics or emergency departments affiliated with an academic medical center. Sample included 2381 Medicaid and dually eligible for Medicare and Medicaid managed care beneficiaries with SUD, continuously enrolled for 12 months, 21 years or older, and without an HIV diagnosis. Substances included in the analysis were alcohol, opioid, cocaine, cannabis, and other psychoactive drugs. Univariate descriptive statistics and multivariable logistic models were used to address the objectives. Rates of HIV and HCV screening, and PrEP prescriptions were examined. Outcome variables for multivariable logistic regression were whether beneficiaries had HIV screening, HCV screening, and PrEP prescription. The predictors were age, gender, race, primary language, area of residence, insurance type, chronic conditions, tobacco use, polysubstance use, number of providers seen, primary care physician seen, emergency department visits, hospitalizations, and annual cost of care.

Results: About 22% of beneficiaries had HIV or HCV screening; PrEP prescriptions were non-existent in this sample. About 83% visited their primary care physician (PCP). The predictors of HIV and HCV screenings include gender, area of residence, polysubstance use disorder, PCP visits, and hospitalizations.

Conclusion: Results indicate low HIV and HCV screening rates and no PrEP prescriptions among the managed care population with SUD. Specifically, beneficiaries with rural residence, females, and Medicaid beneficiaries need targeted interventions and missed opportunities exist at PCP offices.

Background: Digital literacy is increasingly recognized as a key determinant of health, yet the mechanisms linking it to self-rated health in transitional economies like China remain underexplored. This study examines how digital literacy influences self-rated health, directly and indirectly through mental health, while exploring heterogeneity across age and gender groups.

Methods: Using data from the 2023 Chinese General Social Survey (n=8,039 adults aged 18 and above), we constructed a multidimensional digital literacy index via entropy weighting and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), integrating dimensions of digital access, usage, and entertainment. Structural equation modeling (SEM) and multi-group analyses were employed to test relationships, controlling for gender, age, and education. Model fit was assessed using RMSEA, CFI, and other indices; robustness was verified through alternative specifications, sensitivity checks, and outlier trimming.

Results: Digital literacy had a significant positive effect on self-rated health (β=0.115, p<0.001), comprising a direct effect (β=0.076, p<0.001) and an indirect effect via mental health (β=0.039, p<0.001; mediation proportion=33.9%). Multi-group SEM revealed heterogeneity: effects were strongest in young and middle-aged females (β=0.141-0.143, p<0.001) and weaker in older adults (eg, β=0.050 for females >60, p<0.01). Mental health mediated more strongly in older groups (β=0.500, p<0.001). The model explained 38.5% of variance in self-rated health.

Conclusion: Digital literacy positively influences self-rated health by enhancing resource access and mental well-being, with pronounced benefits for younger and female populations. Policymakers should prioritize age-appropriate digital literacy initiatives with psychological support to reduce disparities, aligning with China's "Healthy China 2030" and "Digital China" strategies.

Introduction: The well-being of healthcare workers (HW) affects both their psycho-physical state, and the quality of care provided. In Emergency Department (ED), overcrowding, long work shifts and the criticality of patients can affect the professionals' quality of life and empathy.This empirical study aims to evaluate the HWs' professional quality life and empathy in an ED.

Methods: With a cross-sectional design, we administered the "Jefferson Scale of Empathy" (JSE) and the "Perception of the quality of professional life" (ProQOL) to 70 HWs in a General Hospital ED and collected demographic and work variables of participants. We statistically analyzed data.

Results: We collected responses from 16 doctors, 39 nurses, and 15 healthcare assistants, with a response rate of 70%. The JSE score (111.13 ± 11.75) showed high empathy levels in all professions. The PROQOL burnout (23.73 ± 5.53; chi-squared = 8.80; p = 0.012) and compassion fatigue (43.73 ± 9.49; chi-squared = 10.48; p = 0.005) scores showed statistically significant differences between the three HWs. Doctors were the profession most affected by stress (23.12 ± 6.47; chi-squared = 5.70; p = 0.058), burnout (27.62 ± 5.97; chi-squared = 8.80; p = 0.012) and compassion fatigue (50.75 ± 10.6; chi-squared = 10.48; p = 0.005) compared to other HWs. At multiple linear regressions, JSE score, as dependent variable, was associated with ProQOL burnout (Coeff: -0.88; p = 0.021) and stress (Coeff: 0.76; p = 0.048), whereas secondary traumatic stress score, as dependent variable, was positively associated with HWs' years of employment (Coeff: 0.38; p = 0.040).

Discussion: Stress and burnout were higher among physicians than among other HWs, but empathy was high among all HWs, with no sex difference. These findings may inform future training programs and organizational policies aimed at improving the HWs professional quality of life, suggesting that support for HWs are essential for quality of care.

Background: While the concept of active aging has been extensively studied in high-income countries, China faces distinct demographic challenges, including a rapidly growing elderly population, accelerated aging, and aging prior to widespread economic prosperity. These trends highlight the urgent need for a context-specific conceptual and evaluative framework to measure active aging, tailored to China's socio-cultural and economic realities.

Methods: This study employs the Latent Dirichlet Allocation (LDA) topic model to construct a multidimensional indicator system for measuring active aging among older adults in China. Drawing on five waves of nationally representative panel data from the China Health and Retirement Longitudinal Study (CHARLS), spanning 2011 to 2020, we evaluate individual-level active aging scores using a quantitatively derived framework.

Results: The measurement system consists of six core dimensions and 21 indicators: (1) physical health and functional capacity, (2) psychological well-being and life satisfaction, (3) family caregiving and social security, (4) economic security and intergenerational support, (5) social participation and enabling environments, and (6) lifelong learning and self-management. All scale-based measures demonstrated acceptable internal consistency (Cronbach's alpha ≥ 0.70). The average active aging score among the full sample was 0.4912±0.0907.

Conclusion: Active aging levels in China have shown consistent improvements over the observation period, with the most pronounced gains in the eastern region. The central region has seen a narrowing of differences, while the eastern, northeastern, and western parts of the country have seen a widening of differences. Key positive correlates of active aging include educational attainment, urban residence, male gender, alcohol consumption, and being married. Negative associations were found for older age, geographic region, presence of chronic conditions, number of surviving children, and smoking. Among these, education attainment, urban-rural status, age and gender emerged as the most influential factors.

Background: The Barthel Index (BI) is a standardized tool used to evaluate patients' ability to perform daily activities. Lower scores on the index indicate greater dependency. The distribution of nursing workload is often uneven and rarely assessed using validated measures, particularly on busy days, such as those following patient admission days when care demands are higher.

Purpose: This study aims to quantify nursing workload in two hospital wards using the Barthel Index.

Methods: This cross-sectional comparative study collected patient data through direct observation and interviews with all patients in two hospital wards in northern Greece on a busy working day. The 10-item BI was employed to determine each patient's level of dependency.

Results: A total of 62 patients (31 females; mean age 74.5 years, range 20-94) participated in this study. Ward A had 12 nurses, while ward B had 13. On a heavy workday, the mean BI score was 45 in ward A and 20 in ward B, indicating higher patient dependency in Ward B.

Conclusion: The BI provides a quick and objective assessment of patient dependency, which reflects nursing workload. Integrating BI assessments into routine practice could support evidence-based staffing decisions and allow for better alignment of nurse allocation with the specific needs of each ward, especially during high-demand periods.

Introduction: Hospital-acquired pneumonia (HAP) remains a major challenge in clinical practice, particularly due to polymicrobial infections and antimicrobial resistance. Traditional diagnostic methods, such as culture and PCR, are limited by low sensitivity, slow turnaround time, and inability to detect fastidious or novel pathogens. Metagenomic next-generation sequencing (mNGS) offers an unbiased approach to pathogen detection and may improve diagnostic accuracy and clinical decision-making.

Methods: We conducted a retrospective study of 300 adult HAP patients admitted to Beijing Rehabilitation Hospital, China. Bronchoalveolar lavage fluid samples were analyzed using the Illumina sequencing platform for mNGS. Detection rates, pathogen spectrum, resistance gene identification, and treatment modifications were compared with conventional culture methods.

Results: mNGS achieved a pathogen detection rate of 92%, significantly higher than the 72% achieved by culture. It identified a broader spectrum of bacteria, fungi, and viruses, including Pseudomonas, Klebsiella, and Aspergillus, which were often missed by culture. Polymicrobial infections were detected in 28% of cases, and antibiotic resistance genes were identified in 30% of samples. The median turnaround time for mNGS results was 48 hours after BAL sampling. Based on mNGS findings, treatment regimens were adjusted in 26% of patients.

Conclusion: mNGS demonstrated superior diagnostic performance compared with culture by increasing pathogen detection rates, identifying resistance genes, and guiding treatment adjustments in HAP patients. Despite its promise for precision medicine, further studies are needed to assess cost-effectiveness and generalizability, given the retrospective and single-center design of this study.

Objective: To investigate the risk factors associated with dry eye disease (DED) development in cataract (CAT) patients following phacoemulsification surgery, with a focus on lens nucleus hardness grading, and to develop a predictive model for individualized clinical management.

Methods: This retrospective study included 150 cataract patients who underwent phacoemulsification from January 2023 to January 2025. Lens nucleus hardness was graded using the Emery system. Preoperative assessments included ocular surface status and systemic comorbidities. Logistic regression was used to identify independent risk factors, and a predictive model was developed and evaluated by receiver operating characteristic (ROC) analysis.

Results: Postoperative DED occurred in 38.7% of patients. Multivariate analysis revealed that diabetes mellitus, history of keratoconjunctivitis, conjunctivochalasis grade ≥III, lens nucleus hardness grade ≥IV, and 3.0 mm clear limbal incision were independent risk factors (all P<0.05). The prediction model showed good performance (AUC=0.836), with 84.5% sensitivity and 69.6% specificity.

Conclusion: Lens nucleus hardness, along with key clinical factors, independently predicts DED risk after cataract surgery. The developed model may assist in early risk identification and personalized perioperative management.

Healthcare is a constantly evolving field enriched by new technologies, medications, and treatment methods. However, these continuous innovations also introduce new complexities that can pave the way for medical errors to arise. As a result, quality of care and patient safety are always at stake, highlighting the imperative to set up processes to avoid errors in healthcare at any cost. This need for systematic approaches has led to the adoption of Quality Improvement Science (QIS), which deals with the early identification of problems and suggests ways to prevent them in a proactive manner. This study explores the principles of QIS as applied to patient safety, examining various approaches and proposing strategies to implement effective solutions. It further investigates methods for constant quality improvement, emphasizing the roles of technology and human resources in enhancing healthcare quality and patient safety. In particular, it studies how artificial intelligence (AI) strengthens information gathering and organization to provide practical insights. Furthermore, this study discusses the enablers and barriers to successful implementation of these quality improvement processes. Crucially, this paper provides a comprehensive and actionable framework for selecting appropriate QIS tools and indicators, developed through a structured synthesis of QIS literature and represented as decision flows that enable systematic care delivery problem identification and analysis.