Journal of evaluation in clinical practice最新文献

Rationale, Aims and Objectives

This study aims to evaluate the psychometric properties of the Korean Social Health Scale for the Elderly (K-SHSE) using Rasch analysis, to ensure its validity and reliability in measuring social health among older Korean adults.

Methods

A total of 300 community-dwelling adults aged 55 and older in Korea completed the K-SHSE through an online survey. Rasch analysis was conducted using the Partial Credit Model to assess unidimensionality, item fit, difficulty hierarchy, rating scale functioning, precision and differential item functioning (DIF). Analyses were performed separately for the three subdomains: Social Support (SS), Social Adjustment (SA), and Perceived Environmental Resources (PER).

Results

Of the 25 original items, 10 were excluded due to misfits statistics. The final scale retained 15 items across the three subdomains. All subdomains satisfied the assumption of unidimensionality. The SS domain demonstrated strong reliability and person separation, while the SA and PER domains showed limited discriminative ability. Four items displayed statistically significant DIF by sex. The item difficulty hierarchy aligned with theoretical expectations, and no significant floor or ceiling effects were observed.

Conclusion

All domains of the K-SHSE demonstrated acceptable psychometric properties for assessing social health among older adults in Korea, with particularly strong performance in the SS domain. Although some items exhibited DIF or showed limited reliability, their inclusion did not bias the measurement model. These findings support the use of the K-SHSE as a valid and reliable tool for evaluating social health in both research and practical settings.

Aims

This synthetic simulation, using no real patient data, study aimed to evaluate and compare the performance of three prominent large language models (LLMs)—ChatGPT-4.1, Grok-3 and DeepSeek—in generating medical nutrition therapy aligned dietary plans for adults with type 2 diabetes mellitus (T2DM).

Methods

A simulation-based design was employed using 24 standardized virtual patient profiles differentiated by gender and body mass index (BMI) category. Each LLM was prompted in Turkish to generate 3-day meal plans. Outputs were assessed for energy and macro-/micronutrient accuracy, adherence to national and international T2DM guidelines and alignment with the nutrition care process (NCP).

Results

ChatGPT-4.1 showed the highest alignment with energy requirements (70.9%) but overestimated fat intake. Grok-3 demonstrated superior energy accuracy (83.1%) but failed to meet several micronutrient targets. DeepSeek adjusted protein intake according to BMI but underdelivered carbohydrates. None of the models demonstrated full concordance with the NCP framework, particularly in the diagnosis and monitoring components. Frequent hallucinations and lack of clinical contextualization were noted. Integration of retrieval-augmented generation (RAG) was identified as a potential improvement strategy.

Conclusion

While LLMs showed promise in generating baseline dietary guidance in a simulated context, these results reflected concordance with guideline documents only and concordance with guideline documents only and should not be interpreted as evidence of equivalence to dietitian-led care. These findings reflected model behaviour in synthetic scenarios only and highlighted the need for RAG integration and expert supervision before any clinical application.

Rationale

Musculoskeletal (MSK) conditions are a leading cause of global disability, yet MSK physical examination remains a well-documented gap in medical education. Learners frequently report low confidence in performing these exams. Medical education programmes have addressed this by engaging adult patient educators with lived experience, an approach that has been shown to improve clinical skills. However, little is known about engaging adolescents as patient educators for the MSK exam, despite arthritis being a common chronic condition in this age group. As adolescents are at a unique developmental stage, their perspectives can help medical learners develop age-appropriate, patient-centred care. Exploring how adolescents themselves perceive this role is essential to designing effective educational programmes. This study represents an important first step in informing the development of a future patient-educator programme involving adolescents with juvenile idiopathic arthritis (JIA).

Aims and Objectives

This study explored the perceptions of adolescents (13–18 years) with JIA about their potential involvement as patient-educators of the MSK exam.

Method

We conducted 19 semi-structured interviews at two Canadian paediatric centres and analysed transcripts using thematic content analysis.

Results

Results showed that adolescents were generally enthusiastic about patient education and recognised the value of lived experience in training medical learners.

Conclusion

Understanding adolescents' perceptions is key to developing future medical education programmes that meaningfully integrate their experiences.

Objective

Diabetes mellitus is a chronic disease that presents significant health challenges worldwide. Accurate diabetes prediction facilitates early intervention and personalized healthcare strategies, thereby improving patient care and reducing healthcare processing costs. Ensemble-based machine learning (ML) methods enhance predictive performance.

Method

This study explores various ML classifiers, both individually and in ensemble configurations, including decision trees, random forests, k-nearest neighbors, Naive Bayes, AdaBoost (AB), XGBoost (XB), and multilayer perceptron (MLP) for prediction. The performance of each method is evaluated through rigorous experimentation and comparative analysis across multiple aspects.

Results

The performance of the best ML model, MLP, is compared with that of the proposed CatBoost classifier and the ensemble model to identify the most effective approach for diabetes prediction in minimal duration. The proposed CatBoost classifier's execution time of 4.27 s, which is approximately 98.64% faster than the ensemble model's 314.96 s. This demonstrates CatBoost's significant advantage in computational efficiency over ensemble-based classifiers.

Conclusion

By leveraging the diverse and complementary strengths of ML classifiers, this study contributes to the advancement of precision medicine and personalized healthcare for individuals at risk of diabetes.

Objectives

Given the rising number of older adults reliant on family and friend caregivers (i.e., those who provide informal care), the Saskatchewan Caregiver Experience Study aimed to examine the experiences of these caregivers in Saskatchewan and identify unmet needs. This paper describes our knowledge translation and mobilization efforts of our study's findings.

Methods

Researchers partnered with the Saskatoon Council on Aging (SCOA) to conduct the study, recruiting 355 family and friend caregivers. We evaluated impacts across conceptualization, data collection and knowledge mobilization using the Knowledge Engagement Impact Assessment Toolkit to assess how effectively our study's design has the potential to impact policy and practice, which involved completing an Assessment Matrix (quantitative assessment) and Assessment Portrait (qualitative assessment). A stakeholder webinar served as the primary knowledge translation event.

Findings

An Assessment Matrix revealed moderate impact scores for conceptualization and knowledge mobilization phases. However, the Assessment Portrait reflected collaboration, thorough policy alignment and outreach. Data collection and analysis scored lower. We reflected on this lower score in the Assessment Portrait as being due to fewer avenues for reciprocal engagement and capacity-building during this stage. Policy recommendations, formed in collaboration with SCOA, were presented at the webinar and called for expanded respite care and streamlined system navigation.

Conclusion

By systematically evaluating research activities, this study highlights the critical role of knowledge translation in shaping caregiver support. Findings reinforce the importance of early and ongoing stakeholder collaboration, user-friendly dissemination methods and targeted policy action. Employing a structured framework for measuring engagement impact can guide targeted interventions, ensuring that caregiver programming, legislative reforms and improved care quality align with evolving population needs and priorities.

Background

The implementation of Diagnosis-Related Group (DRG) payment systems globally has intensified pressure on healthcare institutions to control costs while maintaining quality. Quality Control Circles (QCCs) have emerged as a promising continuous quality improvement method in nursing management. This study investigated the effectiveness of QCC-based nursing quality improvement on DRG cost control in general surgery departments.

Methods

A cross-sectional survey was conducted among 50 healthcare professionals (18 physicians, 21 nurses, 8 nursing quality control staff, and 3 others) in a general surgery department. The survey assessed QCC implementation intensity, DRG knowledge, nursing improvement effects, and cost control obstacles. Descriptive statistics, Spearman correlation analysis, and Mann-Whitney U tests were performed to analyze relationships between variables.

Results

QCC implementation showed high intensity scores (M = 4.16, SD = 0.93) with strong correlations to standardized operational procedures (ρ = 0.798). Nursing quality improvements were substantial (M = 4.38, SD = 0.73), and higher scores in key dimensions such as standardized preoperative education and multimodal analgesia were associated with fewer postoperative complications and shorter length of stay, thereby directly supporting DRG cost control.

Conclusion

QCC-based nursing quality improvement demonstrates effectiveness in supporting DRG cost control in general surgery through standardized processes and enhanced nursing practices. Among these achievements, the rationalization management score of high‑value consumables was the highest (M  =  4.40), directly reducing the proportion of consumables cost within the DRG group, which makes the cost‑control effect more specific and clinically meaningful. The findings suggest that systematic implementation of QCC methodologies can facilitate both quality improvement and cost containment objectives within DRG payment frameworks.

Clinical trial number: Not applicable.

Background

Pharmacological therapy in older persons is inherently complex due to the interplay of age-related physiological changes, multimorbidity, and polypharmacy. These factors increase the vulnerability of elderly patients to drug interactions and adverse drug reactions.

Aim

This review aims to examine the mechanisms, causes, and consequences of drug interactions in older persons, and to highlight strategies to optimise pharmacological therapy in this population.

Methods

A narrative review of current literature was conducted, focusing on studies that address age-related pharmacological changes, multimorbidity, polypharmacy, and drug–drug interactions in elderly patients.

Results

Findings from the literature indicate that drug interactions represent one of the most preventable medical errors in older persons' pharmacotherapy. The global scale of this problem highlights the need for increased awareness and collaborative approaches among healthcare providers.

Conclusion

Improving the quality of pharmacotherapy in older adults requires a concerted effort to prevent drug interactions, optimise prescribing practices, and ensure patient safety. Collaborative interventions can reduce adverse drug events and enhance therapeutic outcomes in elderly care.

Background

Individuals with visual impairment encounter considerable challenges in managing their treatments, which stem from difficulties in identifying medications, as well as reading and recalling information. Despite the implementation of various management strategies, errors remain prevalent and have the potential to compromise health. Consequently, pragmatic recommendations have been formulated in French for community pharmacists to enhance the quality of care for this vulnerable population.

Aim and Objective

To build an interdisciplinary consensus on guidelines for the care of individuals with visual impairment, with the aim of promoting their inclusion in healthcare.

Method

A consensus-building Delphi survey was conducted between May and October 2024 by a European panel of experts. The interdisciplinary panel consisted of ophthalmologists with a specialisation in low vision, orthoptists, ergotherapists, psychologists, and community pharmacists. The recommendations were encoded in the form of an online questionnaire on the LimeSurvey platform. The experts were invited to indicate their degree of agreement on a nine-point Likert scale. Descriptive statistics were produced using IBM® SPSS 29 Advanced software. This process was repeated until a consensus was reached between all the experts.

Results

A total of four rounds of the Delphi method were required by the panel of ten experts to evaluate the initial set of 32 recommendations. In conclusion, an introduction to the guidelines and 30 recommendations, which have been grouped into six main categories, were validated.

Conclusion

The consensus process has facilitated the consolidation of recommendations and ensured their relevance, thereby enabling the dissemination of high-quality content to French-speaking community pharmacists to support their practice and promote health equity.

Background

Readmission following acute myocardial infarction (AMI) poses significant challenges to health systems and patient outcomes. The LACE index, a composite of Length of stay, Acuity of admission, Comorbidities, and Emergency department visits, is widely used for readmission prediction. However, its performance in large-scale, real-world Korean cohorts remains understudied.

Objective

This study aimed to validate the predictive performance of the LACE index for 30-day readmissions in AMI patients using a nationally representative Korean cohort.

Methods

This retrospective cohort study analyzed data from the Korean National Health Insurance Service Sample (NHISS) database from 2011 to 2020. A total of 609,640 adult patients hospitalized for AMI were included. The LACE index was calculated for each patient, and 30-day readmissions were identified. Logistic regression was used to estimate odds ratios (ORs) for readmission. Model discrimination was assessed using ROC curve analysis and C-statistics. Subgroup and survival analyses were performed by age, LACE score, and comorbidity burden.

Results

Among 609,640 AMI patients, 205 (0.034%) experienced 30-day readmission. Patients with a LACE score of ≥ 10 had significantly higher odds of readmission (OR = 2.65; 95% CI: 1.68–4.19, p < 0.001) compared to those with scores 0–4. Middle-aged adults (35–64 years) also showed elevated readmission risk (OR = 3.42; 95% CI: 1.74–6.73, p < 0.001), while older adults (≥ 65 years) did not have significantly different risk. The LACE index showed moderate discriminatory performance (C-statistics = 0.71). Kaplan–Meier survival curves demonstrated significantly lower 30-day survival among patients with LACE ≥ 10.

Conclusions

Study findings suggest the LACE index is a useful tool for predicting 30-day readmissions among AMI patients in Korea. Its simplicity and moderate accuracy support its application in clinical and policy-level risk stratification strategies. Future prospective studies should refine prediction models by incorporating additional clinical variables.