Improve Health Care Outcomes Research Articles

Predictors of Health Care Practitioners' Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology.

Artificial intelligence-enabled clinical decision support systems (AI-CDSSs) offer potential for improving health care outcomes, but their adoption among health care practitioners remains limited. This meta-analysis identified predictors influencing health care practitioners' intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Additional predictors were examined based on existing empirical evidence. The literature search using electronic databases, forward searches, conference programs, and personal correspondence yielded 7731 results, of which 17 (0.22%) studies met the inclusion criteria. Random-effects meta-analysis, relative weight analyses, and meta-analytic moderation and mediation analyses were used to examine the relationships between relevant predictor variables and the intention to use AI-CDSSs. The meta-analysis results supported the application of the UTAUT to the context of the intention to use AI-CDSSs. The results showed that performance expectancy (r=0.66), effort expectancy (r=0.55), social influence (r=0.66), and facilitating conditions (r=0.66) were positively associated with the intention to use AI-CDSSs, in line with the predictions of the UTAUT. The meta-analysis further identified positive attitude (r=0.63), trust (r=0.73), anxiety (r=-0.41), perceived risk (r=-0.21), and innovativeness (r=0.54) as additional relevant predictors. Trust emerged as the most influential predictor overall. The results of the moderation analyses show that the relationship between social influence and use intention becomes weaker with increasing age. In addition, the relationship between effort expectancy and use intention was stronger for diagnostic AI-CDSSs than for devices that combined diagnostic and treatment recommendations. Finally, the relationship between facilitating conditions and use intention was mediated through performance and effort expectancy. This meta-analysis contributes to the understanding of the predictors of intention to use AI-CDSSs based on an extended UTAUT model. More research is needed to substantiate the identified relationships and explain the observed variations in effect sizes by identifying relevant moderating factors. The research findings bear important implications for the design and implementation of training programs for health care practitioners to ease the adoption of AI-CDSSs into their practice.

Exploring the elderly user experience in using digital health interventions: a case study of the MySejahtera application

In recent years, there has been increased interest in using digital health tools to improve healthcare outcomes and encourage healthy lifestyles. However, older adults, who often face challenges with technology, may encounter difficulties when using these tools. This study focused on understanding how elderly individuals experience the MySejahtera app, a digital health tool designed to help manage health during a pandemic. The research involved 30 elderly users through focus groups, interviews, and user experience evaluations. The study identified key themes in the elderly user experience, such as the need for simplicity, efficiency in the app's interface, ease of learning, concerns about security, and emotional reactions. Recommendations were made to enhance the engagement of elderly users with digital health apps. While the MySejahtera app shows promise for older adults, the study highlights the importance of addressing specific design considerations and providing support to improve user satisfaction. Overall, the research offers valuable insights and recommendations for designing and implementing digital health applications that better meet the needs and preferences of elderly users.

Artificial Intelligence (AI) Applications for Point of Care Ultrasound (POCUS) in Low-Resource Settings: A Scoping Review.

Advancements in artificial intelligence (AI) for point-of-care ultrasound (POCUS) have ushered in new possibilities for medical diagnostics in low-resource settings. This review explores the current landscape of AI applications in POCUS across these environments, analyzing studies sourced from three databases-SCOPUS, PUBMED, and Google Scholars. Initially, 1196 records were identified, of which 1167 articles were excluded after a two-stage screening, leaving 29 unique studies for review. The majority of studies focused on deep learning algorithms to facilitate POCUS operations and interpretation in resource-constrained settings. Various types of low-resource settings were targeted, with a significant emphasis on low- and middle-income countries (LMICs), rural/remote areas, and emergency contexts. Notable limitations identified include challenges in generalizability, dataset availability, regional disparities in research, patient compliance, and ethical considerations. Additionally, the lack of standardization in POCUS devices, protocols, and algorithms emerged as a significant barrier to AI implementation. The diversity of POCUS AI applications in different domains (e.g., lung, hip, heart, etc.) illustrates the challenges of having to tailor to the specific needs of each application. By separating out the analysis by application area, researchers will better understand the distinct impacts and limitations of AI, aligning research and development efforts with the unique characteristics of each clinical condition. Despite these challenges, POCUS AI systems show promise in bridging gaps in healthcare delivery by aiding clinicians in low-resource settings. Future research endeavors should prioritize addressing the gaps identified in this review to enhance the feasibility and effectiveness of POCUS AI applications to improve healthcare outcomes in resource-constrained environments.

Education & Communication to Improve Healthcare Outcomes Academy (ECHO Academy)

Education & Communication to Improve Healthcare Outcomes Academy (ECHO Academy)

Publication patterns of graduates from a DNP program with submission of a manuscript as a degree requirement

BackgroundNurses who earn a Doctor of Nursing Practice (DNP) degree are expected to make essential contributions to the scholarship of practice and the improvement of health care outcomes. The DNP program at Duke University School of Nursing requires that students demonstrate scholarship competence by writing a manuscript based on their DNP project and submitting it for publication. PurposeThe purpose of this article is to share an evaluation of the effectiveness of this approach. MethodsThe authors used a bibliometric study design. ResultsThis study demonstrates that manuscripts authored by students based on the DNP projects they conduct while earning their DNP degree can be published and are cited by other scholars. Additionally, the majority of these authors continue to publish scholarly work in the nursing and broader health-focused literature after they graduate. ConclusionNurses who developed and submit manuscripts for publication based on their DNP project contribute to the literature and evidence base for practice.

ViT-PSO-SVM: Cervical Cancer Predication Based on Integrating Vision Transformer with Particle Swarm Optimization and Support Vector Machine.

Cervical cancer (CCa) is the fourth most prevalent and common cancer affecting women worldwide, with increasing incidence and mortality rates. Hence, early detection of CCa plays a crucial role in improving outcomes. Non-invasive imaging procedures with good diagnostic performance are desirable and have the potential to lessen the degree of intervention associated with the gold standard, biopsy. Recently, artificial intelligence-based diagnostic models such as Vision Transformers (ViT) have shown promising performance in image classification tasks, rivaling or surpassing traditional convolutional neural networks (CNNs). This paper studies the effect of applying a ViT to predict CCa using different image benchmark datasets. A newly developed approach (ViT-PSO-SVM) was presented for boosting the results of the ViT based on integrating the ViT with particle swarm optimization (PSO), and support vector machine (SVM). First, the proposed framework extracts features from the Vision Transformer. Then, PSO is used to reduce the complexity of extracted features and optimize feature representation. Finally, a softmax classification layer is replaced with an SVM classification model to precisely predict CCa. The models are evaluated using two benchmark cervical cell image datasets, namely SipakMed and Herlev, with different classification scenarios: two, three, and five classes. The proposed approach achieved 99.112% accuracy and 99.113% F1-score for SipakMed with two classes and achieved 97.778% accuracy and 97.805% F1-score for Herlev with two classes outperforming other Vision Transformers, CNN models, and pre-trained models. Finally, GradCAM is used as an explainable artificial intelligence (XAI) tool to visualize and understand the regions of a given image that are important for a model's prediction. The obtained experimental results demonstrate the feasibility and efficacy of the developed ViT-PSO-SVM approach and hold the promise of providing a robust, reliable, accurate, and non-invasive diagnostic tool that will lead to improved healthcare outcomes worldwide.

Generative Artificial Intelligence: Enhancing Patient Education in Cardiovascular Imaging.

Cardiovascular disease (CVD) is a major cause of mortality worldwide, especially in resource-limited countries with limited access to healthcare resources. Early detection and accurate imaging are vital for managing CVD, emphasizing the significance of patient education. Generative artificial intelligence (AI), including algorithms to synthesize text, speech, images, and combinations thereof given a specific scenario or prompt, offers promising solutions for enhancing patient education. By combining vision and language models, generative AI enables personalized multimedia content generation through natural language interactions, benefiting patient education in cardiovascular imaging. Simulations, chat-based interactions, and voice-based interfaces can enhance accessibility, especially in resource-limited settings. Despite its potential benefits, implementing generative AI in resource-limited countries faces challenges like data quality, infrastructure limitations, and ethical considerations. Addressing these issues is crucial for successful adoption. Ethical challenges related to data privacy and accuracy must also be overcome to ensure better patient understanding, treatment adherence, and improved healthcare outcomes. Continued research, innovation, and collaboration in generative AI have the potential to revolutionize patient education. This can empower patients to make informed decisions about their cardiovascular health, ultimately improving healthcare outcomes in resource-limited settings.

Racial Disparities in Sports Cardiology

ImportanceRacial disparities in cardiovascular health, including sudden cardiac death (SCD), exist among both the general and athlete populations. Among competitive athletes, disparities in health outcomes potentially influenced by social determinants of health (SDOH) and structural racism remain inadequately understood. This narrative review centers on race in sports cardiology, addressing racial disparities in SCD risk, false-positive cardiac screening rates among athletes, and the prevalence of left ventricular hypertrophy, and encourages a reexamination of race-based practices in sports cardiology, such as the interpretation of screening 12-lead electrocardiogram findings.ObservationsDrawing from an array of sources, including epidemiological data and broader medical literature, this narrative review discusses racial disparities in sports cardiology and calls for a paradigm shift in approach that encompasses 3 key principles: race-conscious awareness, clinical inclusivity, and research-driven refinement of clinical practice. These proposed principles call for a shift away from race-based assumptions towards individualized, health-focused care in sports cardiology. This shift would include fostering awareness of sociopolitical constructs, diversifying the medical team workforce, and conducting diverse, evidence-based research to better understand disparities and address inequities in sports cardiology care.Conclusions and RelevanceIn sports cardiology, inadequate consideration of the impact of structural racism and SDOH on racial disparities in health outcomes among athletes has resulted in potential biases in current normative standards and in the clinical approach to the cardiovascular care of athletes. An evidence-based approach to successfully address disparities requires pivoting from outdated race-based practices to a race-conscious framework to better understand and improve health care outcomes for diverse athletic populations.

Endometriosis in Transgender Men: Bridging Gaps in Research and Care-A Narrative Review.

Endometriosis is a debilitating gynecological condition commonly seen in individuals designated female at birth; however, there has been limited research focused on its prevalence and impact among transgender men. This narrative review aims to fill a critical knowledge gap by exploring the epidemiology, clinical manifestations, management strategies, and quality-of-life implications of endometriosis among transgender individuals who identify as male. Specifically, this study seeks to estimate the prevalence rates and describe the symptoms experienced by transgender men undergoing testosterone therapy. Additionally, it addresses the diagnostic challenges posed by hormonal treatments and the lack of culturally competent healthcare services for this population. Recent molecular studies indicate that hormonal imbalances, such as increased estrogen synthesis and progesterone resistance, are significant factors in the persistence of endometriosis symptoms despite testosterone therapy. Moreover, evidence suggests that testosterone therapy may not always suppress endometrial activity completely, contributing to the persistence of symptoms in some individuals. Endometriosis in transgender men requires personalized approaches that consider both testosterone therapy and its interactions with endometriosis, as well as fertility preservation and the psychosocial aspects of treatment. This review emphasizes the necessity of taking an inclusive approach in both research and clinical practice to improve healthcare outcomes for this underserved population. The results demonstrate how continued research, education, and healthcare services tailored specifically to transgender men are necessary to better understand and treat endometriosis, thus improving both their overall health and quality of life.

Open Science Publishing for the Drug Repurposing Community

Drug Repurposing Central is an innovative Open Science publishing portal designed to advance the field of drug repurposing. Developed under the REPO4EU Horizon Europe project, this platform features a comprehensive suite of tools and resources to support the dissemination of research, including a dedicated preprint server (DrugRxiv), two diamond open access journals ( Drug Repurposing and Network Medicine), and an interactive research collection. By integrating open peer review, persistent identifiers, machine-readable open licenses, and alternative metrics, Drug Repurposing Central enhances transparency, reproducibility, and accessibility. This portal addresses the balance between Open Science and intellectual property protection, ensuring that research outputs are both openly accessible and potentially patentable. As a beacon of Open Science, Drug Repurposing Central aims to foster collaboration, increase research impact, and drive the development of new treatments, contributing to the global effort to improve healthcare outcomes.

Optimizing Telehealth: Leveraging Key Performance Indicators for Enhanced TeleHealth and Digital Healthcare Outcomes (Telemechron Study).

Over the past decade, the use of telehealth has garnered increasing attention. The focus on quality aspects has seen significant growth in tandem with the telehealth expansion. Having useful indicators in this area is becoming increasingly strategic for fully integrating the technology into the health domain. These indicators can help monitor and evaluate the quality of telehealth services, guiding improvements and ensuring that these digital solutions meet the necessary standards for effective healthcare delivery. The purpose of this study is to analyze Key performance indicators (KPIs) in telehealth within institutional websites and the scientific dissemination world by means of a narrative review. A narrative review was proposed with these two specific points of view based on a standardized checklist and a quality control procedure for including scientific papers in the analysis. Results from scientific studies emphasize KPIs such as patient outcomes, operational efficiency, technical reliability, and cost-effectiveness. These include measures like improvements in condition management, patient satisfaction, consultation numbers, waiting times, and cost savings. Institutional documents from entities like the WHO also show diverse perspectives, focusing on equitable access, clinical excellence, patient prioritization, response times, and patient and staff satisfaction. The findings suggest that adopting a comprehensive set of KPIs and continuously monitoring and evaluating telehealth services can enhance their effectiveness, efficiency, and equity, ultimately improving healthcare outcomes and accessibility.

Nurse-Led Telehealth Oncology Clinic on ‘Home Management During Chemotherapy’ for Gastrointestinal Cancer Patients: Study Protocol of A Mixed Method Study

Introduction: Telehealth is increasingly being used for symptom management among cancer patients on chemotherapy. Objective of the study is to develop Nurse-led Tele-health Oncology Clinic for GI cancer patients regarding ‘Home Management during Chemotherapy’. Methods: The study will follow sequential explanatory mixed method design where during quantitative phase, using RCT (CTRI/2024/01/062028), GI cancer patients of age 18-65 years and undergoing 2nd or 3rd chemotherapy cycle will be randomised after obtaining consent to experimental (EG) & control (CG) groups. Ethical Clearance is already obtained. EG will be followed through Nurse-led Clinic which includes multiple virtual educational & counselling sessions, e-booklet on side effects management, PMRT, Support groups & telephonic follow ups. Effectiveness will be measured in terms of Quality of life, severity of side effects and Anxiety using FACT-G, CTCAE and DASS respectively. In qualitative phase, using extreme case sampling, in-depth interviews from consented participants will be conducted to explore experiences towards intervention. Analysis: Analysis of quantitative data will use descriptive and inferential statistics. This follows thematic analysis and integration of data. Conclusion: The study protocol will provide guidance to optimize utility of tele-medicine technology to improve healthcare outcomes especially for the GI cancer patients.

Scientometric Analysis of the World Scientific Production on Augmented and Virtual Reality in Dental Education.

The aim of this study was to perform a comprehensive bibliometric analysis of virtual reality (VR) and augmented reality (AR) applications in dental education. A cross-sectional research was carried out using a bibliometric methodology. This process entailed the assessment of metadata from scientific publications that are catalogued in the Scopus database, covering the period from January 2018 to August 2023. A variety of indicators were utilized to scrutinize scientific production and dissemination within the academic community. These encompassed elements such as the author, the publication itself, the number of citations, institutional and collaborative affiliations, geographical location, journal quartile ranking, h-index, Source Normalized Impact per Paper (SNIP), Field-Weighted Citation Impact (FWCI), SCImago Journal Rank (SJR), and the CiteScore. Several institutions from different countries and their academic output were found. Beihang University stands out with 16 scholarly articles, followed by Stanford University with 16 articles and 170 citations. The Q1 quartile has experienced a steady increase, reaching 87 scientific articles. The top 10 authors in scientific production on augmented and VR in dentistry include Joe Amal Cecil, Avinash Gupta, and Miguel A Pirela-Cruz. In terms of co-authorship by country, the United States, Germany, and China are the most predominant in the clusters represented. However, other clusters also have a significant presence. By analyzing the explored trends and themes of keyword co-occurrence, four main clusters were identified. The yellow cluster contained the largest amount of research with the keyword "virtual reality." In addition, the blue cluster was found to be best related to the green "simulation," purple "virtual reality (VR)," and light blue "human-centered computing" clusters. This study evidenced the availability and quality of the data used for the analysis. Future studies could consider the use of VR systems with integrated eye tracking and compare their effect in dentistry during dental procedures. The clinical importance of this study lies in its potential to improve dental education. The VR and AR can provide dental students with immersive, hands-on learning experiences, which can enhance their understanding and clinical skills. Furthermore, the translational value of this study extends beyond dental education. The insights gained from this research could be applicable to other fields of medical education where hands-on training is crucial. Thus, the findings of this study have the potential to influence the broader landscape of medical education, ultimately leading to improved healthcare outcomes. How to cite this article: Alvitez-Temoche D, Silva H, Aguila ED, et al. Scientometric Analysis of the World Scientific Production on Augmented and Virtual Reality in Dental Education. J Contemp Dent Pract 2024;25(4):358-364.

Precision medicine in pharmacy: assessing pharmacogenomics competence among pharmacists and pharmacy students.

Pharmacogenomics, a key component of precision medicine, aims to improve healthcare outcomes. As pharmacists play a pivotal role in this evolving field, an assessment of their preparedness to apply pharmacogenomics is imperative. In this cross-sectional study, a validated questionnaire (Content Validity Ratio > 0.741, p < 0.05) that demonstrated reliability (Cronbach's alpha for all scales > 0.7) gathered data on demographics, knowledge, attitudes, barriers, and confidence in pharmacogenomics among pharmacists and pharmacy students in Jordan. Statistical analysis assessed associations and their strength within the collected data and variables. This study included 514 pharmacists and pharmacy students. Knowledge scores were moderate and correlated with academic level and pharmacy school attended. Most participants were open to providing pharmacogenomics testing and interpretation through pharmacy services, but the majority demonstrated concerns about potential misinterpretation of test results and the resulting patients' anxiety. Students cited limited accessibility, while pharmacists identified the lack of standardised guidelines as the main roadblock. This study highlights the need for education to prepare pharmacists for their role in pharmacogenomics. Despite positive attitudes from pharmacists, addressing knowledge gaps, the low confidence in recommending pharmacogenomics tests, and concerns about implementation are essential.

Dr. Himmatrao Bawaskar: Transforming Rural Healthcare Through Scientific Inquiry and Advocacy.

Dr. Himmatrao Bawaskar, a distinguished figure in Indian healthcare, has made significant contributions to medical research and public health, particularly in rural areas. Born in 1951 in Maharashtra, his journey from a rural upbringing to receiving one of the highest civilian awards of the Government ofIndia, the Padma Shri, reflects his dedication to the field of medicine and public health. Dr. Bawaskar's groundbreaking research on scorpion stings, notably the use of prazosin, has revolutionized treatment protocols, significantly reducing mortality rates. Beyond scorpion stings, his work spans diverse medical areas, including snake bites and cardiovascular diseases. Moreover, Dr. Bawaskar's advocacy for ethical practices and healthcare reform underscores his commitment to improving healthcare outcomes. His legacy serves as an inspiration for future generations of healthcare professionals and policymakers, emphasizing the transformative power of dedication, compassion, and scientific inquiry in addressing critical healthcare challenges.

Strengthening neurosurgical training programs in Sub-saharan Africa: a medical student's perspective.

This article provides a nuanced exploration of the state and exigencies of neurosurgical training in sub-Saharan Africa (SSA), viewed through the discerning lens of a medical student. The region has a pronounced scarcity of neurosurgical services, further compounded by sociocultural intricacies and infrastructural inadequacies, resulting in elevated mortality and morbidity rates. The insufficiency of neurosurgeons, facilities, and training centers, particularly in remote areas, exacerbates this predicament. The imperative to fortify neurosurgical training programs is underscored, necessitating a multifaceted approach inclusive of international collaborations and innovative strategies. The challenges impeding neurosurgical training program implementation range from constrained infrastructure to faculty shortages and financial constraints. Recommendations encompass infrastructural investments, faculty development initiatives, and augmented community engagement. An exploration of neurosurgical training programs across diverse African regions reveals commendable strides and imminent deficits, warranting heightened international collaboration. Furthermore, technological innovations, including virtual reality, robotics, and artificial intelligence, are posited as transformative conduits for augmenting neurosurgical training in SSA. The article concludes with a sagacious compendium of recommendations, encompassing standardized curricula, mentorship paradigms, and stringent evaluation mechanisms, all combining efficaciously fortifying neurosurgical insight in SSA and producing transformative improvements in healthcare outcomes.

Hair &amp; Scalp Disease Detection Using Machine Learning &amp; Image Processing

This study predicts hair disorders and provides tailored therapeutic suggestions using a deep learning model based on the VGG (Visual Geometry Group) architecture. A common dermatological problem, hair diseases can have a big impact on a person's physical and mental well-being. Early and accurate diagnosis as well as tailored treatment recommendations are essential for effective management. Inspired by the VGG design, we built a convolutional neural network (CNN) model in this study to analyze images of hair and scalp conditions. The model was trained using a vast and diverse set of images depicting various hair and scalp conditions. Transfer learning was used to modify the pre-trained VGG model so that it could recognize particular characteristics associated with particular hair issues. The prediction model consistently and reliably recognized a variety of hair problems that included dandruff, fungal infections, and alopecia. Reduction of false-positive and false negative outcomes in diagnosing diseases is dependent on high sensitivity and specificity. The suggested AI-based system has the potential to transform the dermatology field by providing prompt and accurate diagnosis of hair diseases and customized treatment recommendations. This study contributes to the ongoing efforts to use artificial intelligence and deep learning to improve healthcare outcomes, especially in the dermatology and skincare domains. Key Words: Convolutional Neural Network (CNN), Deep Learning, Hair Disorders, Visual Geometry Group (VGG).

Association of geographical disparities and segregation in regional treatment facilities for Black patients with aneurysmal subarachnoid hemorrhage in the United States.

This study investigates geographic disparities in aneurysmal subarachnoid hemorrhage (aSAH) care for Black patients and aims to explore the association with segregation in treatment facilities. Understanding these dynamics can guide efforts to improve healthcare outcomes for marginalized populations. This cohort study evaluated regional differences in segregation for Black patients with aSAH and the association with geographic variations in disparities from 2016 to 2020. The National Inpatient Sample (NIS) database was queried for admission data on aSAH. Black patients were compared to White patients. Segregation in treatment facilities was calculated using the dissimilarity (D) index. Using multivariable logistic regression models, the regional disparities in aSAH treatment, functional outcomes, mortality, and end-of-life care between Black and White patients and the association of geographical segregation in treatment facilities was assessed. 142,285 Black and White patients were diagnosed with aSAH from 2016 to 2020. The Pacific division (D index = 0.55) had the greatest degree of segregation in treatment facilities, while the South Atlantic (D index = 0.39) had the lowest. Compared to lower segregation, regions with higher levels of segregation (global F test p < 0.001) were associated a lower likelihood of mortality (OR 0.91, 95% CI 0.82-1.00, p = 0.044 vs. OR 0.75, 95% CI 0.68-0.83, p < 0. 001) (p = 0.049), greater likelihood of tracheostomy tube placement (OR 1.45, 95% CI 1.22-1.73, p < 0.001 vs. OR 1.87, 95% CI 1.59-2.21, p < 0.001) (p < 0. 001), and lower likelihood of receiving palliative care (OR 0.88, 95% CI 0.76-0.93, p < 0.001 vs. OR 0.67, 95% CI 0.59-0.77, p < 0.001) (p = 0.029). This study demonstrates regional differences in disparities for Black patients with aSAH, particularly in end-of-life care, with varying levels of segregation in regional treatment facilities playing an associated role. The findings underscore the need for targeted interventions and policy changes to address systemic healthcare inequities, reduce segregation, and ensure equitable access to high-quality care for all patients.

Differentiating Prodromal Dementia with Lewy Bodies from Prodromal Alzheimer's Disease: A Pragmatic Review for Clinicians.

This pragmatic review synthesises the current understanding of prodromal dementia with Lewy bodies (pDLB) and prodromal Alzheimer's disease (pAD), including clinical presentations, neuropsychological profiles, neuropsychiatric symptoms, biomarkers, and indications for disease management. The core clinical features of dementia with Lewy bodies (DLB)-parkinsonism, complex visual hallucinations, cognitive fluctuations, and REM sleep behaviour disorder are common prodromal symptoms. Supportive clinical features of pDLB include severe neuroleptic sensitivity, as well as autonomic and neuropsychiatric symptoms. The neuropsychological profile in mild cognitive impairment attributable to Lewy body pathology (MCI-LB) tends to include impairment in visuospatial skills and executive functioning, distinguishing it from MCI due to AD, which typically presents with impairment in memory. pDLB may present with cognitive impairment, psychiatric symptoms, and/or recurrent episodes of delirium, indicating that it is not necessarily synonymous with MCI-LB. Imaging, fluid and other biomarkers may play a crucial role in differentiating pDLB from pAD. The current MCI-LB criteria recognise low dopamine transporter uptake using positron emission tomography or single photon emission computed tomography (SPECT), loss of REM atonia on polysomnography, and sympathetic cardiac denervation using meta-iodobenzylguanidine SPECT as indicative biomarkers with slowing of dominant frequency on EEG among others as supportive biomarkers. This review also highlights the emergence of fluid and skin-based biomarkers. There is little research evidence for the treatment of pDLB, but pharmacological and non-pharmacological treatments for DLB may be discussed with patients. Non-pharmacological interventions such as diet, exercise, and cognitive stimulation may provide benefit, while evaluation and management of contributing factors like medications and sleep disturbances are vital. There is a need to expand research across diverse patient populations to address existing disparities in clinical trial participation. In conclusion, an early and accurate diagnosis of pDLB or pAD presents an opportunity for tailored interventions, improved healthcare outcomes, and enhanced quality of life for patients and care partners.

Strengthening healthcare delivery in Rwanda: Implementation science training for reproductive health researchers

BackgroundAddressing the gap between research and practice is crucial for enhancing reproductive healthcare outcomes. In Rwanda and other low- and middle-income countries, bolstering health researchers’ implementation science (IS) capacity is essential. We present a pre-post-intervention study assessing the influence of an intensive IS training program on Rwandan reproductive health researchers’ perceived IS knowledge and self-efficacy in applying IS in their own research. MethodsTo introduce IS principles, we held a one-day training for a diverse cohort of 25 sexual and reproductive health researchers in Rwanda. The training encompassed modules on IS concepts, methodologies, and practical applications. Pre- and post-training assessments gauged changes in participants’ perceived IS knowledge and self-efficacy in applying IS in their own work. ResultsThe study revealed a significant improvement in self-efficacy related to performing IS related tasks. Researchers reported heightened confidence in designing and implementing evidence-based interventions. In terms of perceived knowledge, participants retained what they learned at 4 months. The training fostered a collaborative learning environment, encouraging participants to exchange ideas and experiences. ConclusionTargeted training in IS appears to enhance reproductive health researchers’ capacity to translate research into practice, potentially leading to improved healthcare outcomes in Rwanda. Moving forward, we advocate for the Ministry of Health to establish structures for IS research agenda-setting, particularly for sexual and reproductive health and rights. Ideally, universities, health systems, and research institutions will incorporate IS capacity strengthening into their routine activities. Ongoing training is crucial to reinforce and expand IS knowledge. Our findings are expected to inform future interventions and guide policy development.