Practical Conclusions Research Articles

Documenting trends in blockchain technology adoption: a systematic review

Purpose This study aims to critically analyze existing research on blockchain technology adoption, examining the dominant models and methodologies used, the primary domains where blockchain is applied and the emerging opportunities across various sectors. Design/methodology/approach Using a methodical systematic review approach, the authors meticulously examined a pool of 1,322 collected articles, subjecting 38 studies to rigorous assessment. Through this comprehensive analysis, the authors unveiled the key models and influential factors that intricately shape the trajectory of blockchain adoption. Findings The primary models identified for investigating blockchain adoption were the technology acceptance model and technology–organization–environment. Apart from the core variables within these models, the pivotal determinants influencing various blockchain applications include perceived trust, perceived cost and social influence. In addition, this study highlights supply chain management as a prominent domain for blockchain application adoption. Practical implications Understanding these influential factors and models can guide practical decisions and aid stakeholders in formulating effective strategies for blockchain adoption in diverse sectors. Originality/value This study contributes to advancing the understanding of blockchain adoption dynamics by unveiling the prevalent models and determinants shaping adoption. This study offers valuable insights into the factors influencing the use and adoption of blockchain technologies across diverse sectors.

Abstract 4120680: Language AI Chat BOTs as Pioneers in Cardiovascular Diagnosis? A Retrospective Analysis on Real-Time Patients in a Tertiary Care Centre

GPT-3 and GPT-4, show promise in assisting healthcare professionals with clinical questions. Their performance in real-time clinical scenarios, particularly with cardiovascular symptoms, remains underexplored. This study aims to evaluate their precision and reliability compared to physicians. Hypothesis: We hypothesize that board-certified emergency department attendings at Maimonides Medical Center exhibit higher accuracy and reliability than GPT-3.5 and GPT-4 in generating differential diagnoses based on history and physical examination for patients presenting to the emergency department. Methods: Patient data from Maimonides Medical Center's emergency department, collected from January 1, 2023, to April 30, 2023, was analyzed. Demographics, symptoms, medical history, and discharge diagnoses recorded by emergency room attendings were examined. ChatGPT-3.5 and GPT-4 generated differential diagnoses, which were compared with those by attending physicians. Accuracy was determined by comparing each rater's diagnoses with discharge diagnosis, calculating the proportion of correctly identified cases. Precision was assessed using Cohen's kappa coefficient and Intraclass Correlation Coefficient to measure agreement between raters. Results: Patient demographics showed mean age of 49.12 years, with 57.3% males and 42.7% females. Chief complaints included fever/sepsis (24.7%), gastrointestinal issues (17.7%), and cardiovascular problems (16.4%). Diagnostic accuracy against discharge diagnoses was highest for Chat GPT-4 (85.5%), followed by Chat GPT-3.5 (84.6%) and ED attendings (83%). Cohen’s kappa demonstrated moderate agreement (0.7) between AI models, with lower agreement observed for ED attendings. Stratified analysis revealed higher accuracy for gastrointestinal complaints with Chat GPT-4 (87.5%) and cardiovascular complaints with Chat GPT-3.5 (81.34%). Conclusion: Our study demonstrates that Chat GPT-4 and GPT-3.5 exhibit comparable diagnostic accuracy to attendings, highlighting their potential to aid decision-making in dynamic clinical settings. The stratified analysis revealed comparable reliability and precision of the AI chat bots for cardiovascular complaints which represents a significant proportion of the high risk patients presenting to the emergency department. This study contributes to integrating AI models into medical practice, enhancing efficiency and effectiveness in clinical decision-making

Exploring the Correlation Between Bone Marrow Edema in the Tibial Plateau and Surgical Preferences in Stage 3 Gonarthrosis Patients

Abstract:* Objective: This research aimed to investigate the association between the presence of bone marrow edema in the tibial plateau and the patient's inclination towards surgery in individuals diagnosed with stage 3 gonarthrosis. Background/Purpose: A cohort of 40 patients with confirmed stage 3 gonarthrosis was enrolled in this study. Magnetic Resonance Imaging (MRI) scans were employed to assess the presence and severity of bone marrow edema in the tibial plateau. Patient demographics, clinical history, and radiographic data were collected. A comprehensive statistical analysis, including chi-square tests and logistic regression, was performed to identify any significant relationships. Results: A statistically significant positive correlation was found between edema and age (r = 0.47, p < 0.01), indicating that as age increases, the presence of edema also tends to increase. No significant correlations were found between edema and BMI (r = -0.06, p = 0.70), or between BMI and age (r = 0.08, p = 0.62). This suggests that, within this sample, BMI does not significantly relate to either edema or age. Patients with more pronounced bone marrow edema were more likely to opt for surgery. Additionally, age and previous conservative treatment outcomes were identified as potential confounding factors influencing the decision-making process. Conclusion: The findings of this research underscore the relevance of bone marrow edema in the tibial plateau as a factor influencing the surgical inclination of stage 3 gonarthrosis patients. Understanding this relationship can contribute to a more personalized approach to treatment decisions in orthopedic practice, allowing healthcare professionals to better plan the interventions based on individual patient characteristics and preferences. Further studies are warranted to explore additional factors that may contribute to the complex decision-making process in managing advanced knee osteoarthritis.

Long-Term outcomes after stress echocardiography in real world practice: five-year follow-up of the UK Evarest study

Abstract Aims Stress echocardiography is widely used to assess patients with chest pain. The clinical value of a positive or negative test result to inform on likely longer-term outcomes when applied in real world practice across a healthcare system has not been previously reported. Methods and Results 5503 patients recruited across 32 UK NHS hospitals between 2018 and 2022, participating in the EVAREST/BSE-NSTEP prospective cohort study, with data on medical outcomes up to 2023 available from NHS England were included in analysis. Stress echocardiography results were related to outcomes, including death, procedures, hospital admissions and relevant cardiovascular diagnoses, based on Kaplan-Meier analysis and Cox proportional hazard ratios. Median follow-up was 829 days (IQR 224–1434). A positive stress echocardiogram was associated with a greater risk of myocardial infarction (HR 2.71, 95% CI 1.73–4.24, P<0.001), and a composite endpoint of cardiac-related mortality and myocardial infarction (HR 2.03, 95% CI 1.41–2.93, P<0.001). Hazard ratios increased with ischaemic burden. A negative stress echocardiogram identified an event-free ‘warranty period’ of at least five years in patients with no prior history of coronary artery disease, and four years for those with disease. Conclusions In real-world practice, the degree of myocardial ischaemia recorded by clinicians at stress echocardiography correctly categorises risk of future events over the next five years. Reporting a stress echocardiogram as negative correctly identifies patients with no greater than a background risk of cardiovascular events over a similar time period.

The Shadows of Internationalisation

This forum proposes the use of concept of “shadows” in the analysis of internationalisation policies to make visible the decision-making processes and practices appearing at all levels of internationalisation policies. We focus on the problematic effects appeared at the intersect of internationalisation policies implemented by the countries perceived academic values differently on example of Finland as the EU member state and pre-war Russia. We consider these effects as preventing equal access to quality higher education and life-long learning for all (SDG4, target 4.3). The forum conceptualises the lessons of the EU/Finnish-Russian internationalisation for further usage the suggested theoretical lens in policy implementation in other countries developed their own internationalisation policy on different value basis. This study address to what happens when the internationalisation policies promoted European view on fundamental academic values, are implemented with such policies in the countries where these values are perceived in another way. We answer the question of how and why the shadows of internationalisation appear and have an impact at a macro-, meso-, and micro-level, and conceptualize them for future policy improvement. The forum discusses the implications of internationalisation policies from the practice perspective and what might be learnt by the stakeholders previously involved in these cooperation partnerships.

Rapid diagnosis of Mycoplasma pneumoniae and prediction of antibiotic resistance by nanopore adaptive sampling

AbstractBackgroundMicrobial infections, particularly in children, require rapid and accurate diagnostics. It is difficult to differentiate pathogens from commensal organisms, and it is impossible to identify antibiotic resistance genes that belong to pathogens with current methods. Third‐generation sequencing provides rapid library preparation and real‐time data acquisition. Nanopore normal sampling (NNS) enables unbiased sequencing of clinical samples without amplification, aiding pathogen identification and antimicrobial resistance gene prediction. However, clinical samples often contain a considerable amount of human DNA, potentially masking pathogen data. Nanopore adaptive sampling (NAS) aims to selectively enrich pathogens, promising improved diagnostics for acute infections and better treatment decisions in clinical practice. This study aimed to determine the utility of NAS in enhancing the real‐time detection of pathogens and predicting AMR in infectious disease outbreaks.MethodsThis study used NAS technology to rapidly and directly detect Mycoplasma pneumoniae infection in bronchoalveolar lavage fluid samples from 28 pediatric patients at Shenzhen Children's Hospital. We assessed the efficacy of NAS compared with that of NNS by evaluating the number of microbial reads and the amount of microbial DNA data. We then compared the accuracy of detecting pathogens between NNS and NAS and between NAS and real‐time polymerase chain reaction assays. Furthermore, we predicted antimicrobial resistance (AMR) and examined AMR genes associated with pathogens.ResultsNAS showed up to a 14.67‐fold increase in the amount of microbial DNA data from patients' samples compared with NNS within the initial 2.5 h of sequencing. Additionally, NAS reduced the amount of host DNA data by up to 6.67‐fold compared with NNS. Unlike TaqMan real‐time polymerase chain reaction assays, NAS technology identified dominant pathogens and provided detailed insight into the abundance of the microbial community. Furthermore, NAS was able to predict AMR profiles of microbial communities and attribute specific AMR traits to individual microbes within the samples.ConclusionThis study shows that NAS advances the clinical diagnosis because it can rapidly detect pathogens directly from patients' samples and provides antimicrobial resistance information for clinical guidance. These abilities further facilitate the application of NAS in personalized treatment, reduce the misuse of broad‐spectrum antibiotics, and promote patients' recovery.

Revolutionizing Cardio Vascular Disease Diagnosis Through Lasso Regression Model

Cardiovascular diseases (CVDs) are among the most prevalent and fatal health conditions globally, necessitating early and accurate diagnosis to mitigate risk and enhance treatment outcomes. Traditional diagnostic methods often rely on a combination of clinical assessments and static risk models, which can be constrained by their inability to handle the complexity and interrelationships of multiple risk factors. In this research, we propose a novel approach to revolutionizing cardiovascular disease diagnosis through the application of the Lasso (Least Absolute Shrinkage and Selection Operator) regression model, a powerful machine learning technique for feature selection and predictive modeling. The Lasso regression model is particularly advantageous for high-dimensional medical datasets, where multiple clinical features may be correlated. By applying Lasso, we aim to address two critical challenges in cardiovascular disease prediction: identifying the most relevant predictors from large and complex datasets and reducing model over-fitting, which is common when working with a large number of co-variates. Our results demonstrated that the Lasso model significantly improves prediction accuracy when compared to traditional logistic regression and other machine learning models. Lasso regression has the potential to revolutionize cardiovascular disease diagnosis by providing a data-driven, efficient, and interpretable solution that bridges complex medical datasets with practical clinical decision-making. Keywords: Cardio vascular diseases (CVD), Machine learning models, Losso regression model.

Decision Support System for the Design Process of Apatite Biopolymer Composite Parts

Abstract In connection with the increase in the number and severity of various types of bone tissue injuries received as a result of wounds during military operations in Ukraine, an important issue in orthopedics and traumatology is making informed decisions about the possibility of restoring the integrity and functions of bone tissue when using different types of composition, porosity and strength of apatite-biopolymer composites. The scientific direction of research is the development of principles and methods for making scientifically based decisions in the design and additive manufacturing of bone substitutes based on apatite-biopolymer composites with functional properties depending on the nature of the localization of the cavity bone defect and its size. A set of methods for analyzing images of bone tissue, taking into account its spatial structure, which are obtained by sensors of different physical nature, with the use of neural network models, development of methods of their design, optimization and training is proposed. The new knowledge obtained as a result of the project will become the necessary basis for making optimal decisions in practice for the introduction of the latest methods of treatment and prosthetics in trauma surgery, oncology, cranio-maxillofacial surgery, dentistry, taking into account the risks of biocompatibility of apatite-biopolymer composites. Software development of an intelligent decision support system will be used to design bone substitutes with controlled composition, structure, porosity and mechanical strength for the further selection of additive technology for its production from apatite-polymer composites, which will contribute to increasing the efficiency of treatment and prosthetics in orthopedics and traumatology.

Efficacy of modal curvature damage detection in various pre-damage data assumptions and modal identification techniques

The efficacy of modal curvature approach for damage localization is discussed in the paper in the context of input data. Three modal identification methods, i.e., Eigensystem Realization Algorithm (ERA), Natural Excitation Technique with ERA (NExT-ERA) and Covariance Driven Stochastic Subspace Identification (SSI-Cov), and four methods of determining baseline data, i.e., real measurement of the undamaged state, analytical function, Finite Element (FE) model and approximation of current experimental mode shape, are considered. Practical conclusions are formulated based on analysis of two cases. The first is a laboratory beam with a notch and the second is a stonemasonry historic lighthouse with modern restoration in its upper part. The analysis shows that NExT-ERA and SSI-Cov in combination with approximation of current mode shape provide high efficacy in damage localization alongside relatively straightforward determination of baseline data. It proves that the construction of advanced FE models of a structure can be replaced with a much simpler method of baseline data acquisition. Furthermore, the research shows the structural mode shapes identified with ERA may not always indicate the presence of damage.

How the Relationship of Cultural Intelligence and Social Connectedness Fosters Psychological Well-Being Among International Students in Northern India?

Studying abroad is a popular option for students, but it is unclear yet how the links between connectedness and cultural intelligence might enhance their experiences. To fill this knowledge gap, the current study develops a conceptual model based on ‘contact theory’. This study has examined the relationship between social connectedness, cultural intelligence and psychological well-being. Data were collected from 361 international students enrolled in various northern Indian universities. In the study, consistency-based structural equation modelling was used to test the proposed model. The results of the study showed that social connectedness has a significant influence on psychological well-being, and cultural intelligence partially mediated the relationship between social connectedness and psychological well-being. Additional research recommended a number of theoretical and practical conclusions to assist higher educational institutions, government and society in developing policies for international students.

Critical illness-related corticosteroid insufficiency - an overview of pathogenesis, clinical presentation and management

According to the Society of Critical Care Medicine, critical illness-related corticosteroid insufficiency (CIRCI) characterizes hypothalamic-adrenal axis insufficiency following acute medical conditions of various causes, i.e., sepsis, septic shock, acute respiratory distress syndrome, community-acquired pneumonia, and status after major surgical procedures. Due to highly variable etiology, understanding the pathomechanism and management of CIRCI assumes relevance for all centers providing intensive care. During CIRCI, multiple peripheral adaptations develop, and cortisol distribution volume increases due to hypothalamic-adrenal axis dysregulation, alterations in cortisol metabolism, and tissue resistance to corticosteroids. The proper diagnosis and treatment of CIRCI may be challenging in many cases. Although we have been acquainted with CIRCI since 2008, it remains a difficult condition with widely variable approaches among clinicians due to inconsistent high-quality study results determining the effect of corticosteroids on mortality. Corticosteroids are widely used in acutely ill patients, highlighting the necessity for reliable knowledge to support crucial clinicians’ decisions in daily medical practice. In this review, we provide an overview of the clinical management of patients with CIRCI based on current recommendations and selected studies.

A Paradigm for Shared Decision-Making in Pediatric Anesthesia Practice for Children with Autism for the Generalist Clinician.

A Paradigm for Shared Decision-Making in Pediatric Anesthesia Practice for Children with Autism for the Generalist Clinician.

The anatomy of clinical decision-making: aligning AI design with ICU routines

PurposeIn the face of numerous explanations for why AI-driven decision support systems (DSS) have failed to deliver on their promise of improving organizational decision-making, this paper problematizes the under-theorized mismatch between the design of DSS and the actual decision-making processes that the technology is supposed to support. We examine this mismatch by studying the implementation of a DSS in the intensive care unit (ICU) of a large academic hospital.Design/methodology/approachBased on 27 months of ethnographic fieldwork, we contend that the studied DSS was designed on the assumption that individual intensivists are responsible for making life-critical discharge decisions at one particular moment in time.FindingsHowever, our study of actual decision-making practices reveals that discharge decision-making is instead a protracted process, involving multiple actors fragmented across time and space. To account for these complexities, we advocate for a “dynamic routines” perspective, which highlights the actual patterns of action pursued throughout a clinical decision-making process.Originality/valueOur application of this perspective contributes to a more granular understanding of discharge decision-making, which can help future DSS designers better grasp the peculiarities and complexities—or “anatomy”—of the decision-making process. We also suggest integrating an “anticipatory ethnographic approach” into the design and pre-implementation phases of future DSS to help bridge the current gap between design assumptions and actual decision-making practices.

Grey Relation System Applications in Multi-Attribute Decision-Making and Forecasting in Financial Management

Objective: The objective of the paper is to verify the Grey relation analysis methods and compare them with known models in financial management. The first part is devoted to MADM (multi-attribute decision methods) and the second one to grey forecasting methods. Method: Localization GRA and globalization GRA multi-criteria models are described and compared with the TOPSIS method. The forecasting GRA(1,1) model based on cumulative data is described, derived and compared with the linear growth model. The models are verified on the data set. Results and Discussion: The globalisation model based on a pair-wise comparison of alternatives is different, and the ranking of alternatives is fully different in comparison with the TOPSIS model. It was found that the localisation GRA model and TOPSIS, both constructed on similarity measures, present similar and comparable results. So, the grey relation approach considers the uncertainty of input data. The forecasting model GRA(1,1) is computed and compared with the growth model. Results show that the growth model is the trendy model, and the non-trendy series is overestimated or underestimated, so the GRA model looks more robust. Research Implications: The problem of imprecise data is investigated. And it is a realistic condition for practical decision-making. The problem of lack of data is often a phenomenon in decision-making. Traditional statistical methods cannot be applied. Therefore, grey relation methodology is one approach to overcoming the problem of imprecise data and lack of data. Uncertainty and lack of data problems are topics important for research and the managerial community. Originality/Value: Sometimes, in financial decision-making, high-quality data or long-term time series is not at our disposal, and on the other hand, decisions have to be made. This problem is often neglected and solved by not-relevant traditional statistical models. Therefore, methods that allow the solving of such problems are useful. It was shown that a grey relation methodology for multicriteria financial performance evaluation and forecasting shows more robust and stable results and could be more relevant for imprecise data or short time series data in financial decision-making problems.

Mental healthcare and pragmatic shared decision-making in general practice: An interview study.

Healthcare providers in general practice are expected to deliver mental healthcare to patients through shared decision-making (SDM). It is unclear if they perceive their SDM to be affected by challenging circumstances in mental healthcare, eg, how waiting time for therapy plays a role. To explore how healthcare providers and patients in general practice engage in SDM, given the challenging circumstances in mental healthcare. A qualitative interview study in seven Dutch general practices. Semi-structured interviews were conducted with general practitioners (GPs; N=9), practice nurses mental health (PNMHs; N=8) and patients that sought mental healthcare (N=18). The interviews were deductively and inductively thematically analyzed. The participants mainly reported on SDM regarding treatment in secondary mental healthcare. The PNMHs explained they lack an overview of available treatments and waiting times in facilities. The PNMHs therefore instruct patient to also search for options themselves. Most patients found this approach burdensome, especially those new to mental healthcare. These patients were said to often express no strong treatment preferences and rely on advice by their healthcare providers. The GPs and PNMHs explained that in such cases, they often adopt a pragmatic approach and for example refer indecisive patients to facilities with little waiting time. The healthcare providers and patients in general practice experience they adapt their approach to SDM in reaction to the circumstances in mental healthcare. Further exploration of how SDM is implemented and shaped by challenging circumstances across different healthcare settings is needed.

Patient experiences of treatment-resistant depression (TRD): A systematic review and qualitative meta-synthesis

Treatment-resistant depression (TRD) occurs when depressive symptoms persist after a patient has tried at least two antidepressants at an appropriate dose for an adequate length of time, as judged by their clinician. Understanding what it is like to live with and seek care for TRD can inform treatment planning and contribute to health technology assessments for depression-related therapies. Our objective was to systematically review this evidence through an investigation of qualitative literature about the experiences of people who have TRD. We searched MEDLINE, CINAHL, PsycINFO, and the Web of Science Core Collection. Publications eligible for inclusion were English-language primary empirical qualitative studies or the qualitative component of mixed methods studies examining adults’ experiences with TRD or with specific treatment options for TRD. Two reviewers independently screened and extracted data, as well as critically appraised included studies using the Standards for Reporting Qualitative Research (SRQR) instrument. This review was guided by integrative meta-synthesis. Data analysis consisted of a staged coding process similar to grounded theory. The protocol was registered in PROSPERO prior to the search (record ID: CRD42022356813). Sixteen studies were included from screening 18,568 results. Two studies focused on the overall experience of TRD and 14 explored experiences of specific treatments such as ketamine. Patients described the depressive symptoms they experienced, a sense of futility and desperation to find an effective treatment, and their experiences with new, sometimes experimental, therapies including deriving benefits beyond the clinical. Overall, few studies have examined the patient experience of TRD, reflecting the clinical complexity of this patient population. As most literature coalesced around experiences of specific treatments, greater examination of the overall experience of TRD is needed to inform clinical practice, and administrative and funding policy decisions for interventions that may be effective for this patient population. PROSPERO registration number: CRD42022356813.

Quality of Life in Mild Cognitive Impairment and Mild Dementia Associated with Alzheimer's Disease: A Systematic Review.

Mild cognitive impairment (MCI) and Alzheimer's disease (AD) have a profound impact on patients' quality of life (QoL), with progressive declines occurring as the disease advances. This systematic review aims to summarize the published evidence on patient-reported outcomes (PROs) in individuals with MCI due to AD and mild AD dementia. Comprehensive searches were conducted across five major databases to identify studies reporting on utility values, disutilities, and QoL measures in these patient populations. A total of 23 studies were included that utilized various QoL assessment tools, including EQ-5D (n = 14), SF-36/SF-12 (n = 4), and QOL-AD (n = 11). Reported EQ-5D scores ranged from 0.81 to 0.92 for patients with MCI and from 0.67 to 0.85 for those with mild AD, indicating a noticeable decline in QoL as the disease progresses. QOL-AD scores ranged from 33.8 to 42.5 for MCI and from 32.4 to 38.1 for mild AD, equally reflecting the greater impairment in QoL with disease advancement. Interventions were generally associated with smaller declines in PROs compared to placebo, suggesting a positive impact of treatment in mitigating QoL deterioration. The findings underscore the significant QoL differences between MCI and mild AD, emphasizing the potential benefit of early intervention to preserve QoL and delay disease progression. This review highlights the importance of continued research to better understand QoL in patients with MCI and mild AD dementia, particularly in terms of capturing comprehensive patient-reported outcomes and evaluating the effectiveness of interventions over time. These findings can contribute to a more informed approach in clinical practice and support decision-making in the management of early-stage AD.

Navigating diverse information environments on environmental sustainability: information practices of decision-makers in Finnish high-tech companies

Navigating diverse information environments on environmental sustainability: information practices of decision-makers in Finnish high-tech companies

Integrating innovation and shared decision-making in allergy and immunology practice

Integrating innovation and shared decision-making in allergy and immunology practice

A patient-centered approach to dietary supplements for patients with chronic liver disease.

The use of dietary supplements by patients with chronic liver disease is prevalent and rising. Despite the known risks of dietary supplements, including hepatotoxicity, adulteration, and contamination, patients with chronic liver disease often turn to dietary supplements to support their liver and/or overall health but are not necessarily empowered with the information or guidance from their liver practitioner to do so. This article provides practitioners with a framework for balancing the risks and benefits of dietary supplements in patients with chronic liver disease, offering examples of independent resources and certifications to use this framework in clinical practice. We offer 3 common clinical scenarios to highlight how the use of this framework can improve communication and decision-making in clinical practice. By adapting principles from Integrative Medicine, this article advocates for a patient-centered approach to dietary supplements in patients with chronic liver disease, encouraging open dialogue between clinicians and their patients to facilitate informed decision-making and personalized care.