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BACKGROUND: The field of robotic-assisted surgery is rapidly growing as many robotic surgical devices are in development and about to enter the market. Currently, there is no universally accepted language for labeling the different robotic systems. To facilitate this communication, we created what is, to our knowledge, the first classification of surgical robotic technologies that organizes and classifies surgical robots used for endoscopy, laparoscopy and thoracoscopy. METHODS: We compiled a list of surgical robots intended to be used for endoscopy, laparoscopy, and/or thoracoscopy by searching United States, European, Hong Kong, Japan, and Korean databases for approved devices. Devices showcased at the 2023 Annual Meeting for the Society of Robotic Surgery were added. We also systematically reviewed the literature for any existing surgical robotic classifications or categorizations. We then created a multidisciplinary committee of 8 surgeons and 2 engineers to construct a proposed classification of the devices included in our search. RESULTS: We identified 40 robotic surgery systems intended to be used for endoscopy, laparoscopy and/or thoracoscopy. The proposed classification organizes robotic devices with regard to architecture, port design, and configuration (modular carts, multi-arm integrated cart, table-attachable or arm-table integration). CONCLUSION: This 3-level classification of robotic surgical devices used for endoscopy, laparoscopy and/or thoracoscopy describes important characteristics of robotic devices systematically.

Purpose: To determine the accuracy of dental implant placement using haptic robotic guidance in a large clinical series. Materials and Methods: In a prospective single-arm clinical study, 108 patients received 273 individual endosteal implants. A virtual preoperative restorative and surgical plan was created from a CBCT scan and matched to the surgical workspace on the day of surgery via either a tooth-based or bone-based fiducial splint. Intraoperatively, the surgeon manipulated a handpiece attached to a haptic robotic guidance arm. A variety of drills and implants were used. Both the osteotomy and the implant placement were guided by 3D haptic constraints according to the virtual plan. Postoperative CBCT scans permitted the calculation of the actual implant placement deviations compared to the plan for accuracy. Precision was calculated by comparing SDs from published literature. Results: The implants were evenly distributed by arch, with 47% placed in the maxilla and 53% in the mandible. The mean ± SD signed depth deviation was 0.14 ± 0.87 mm proud. The global angular deviation averaged 1.42 ± 1.53 degrees, with 95% confidence limits of 1.24 and 1.60 degrees. The crown of the actual placed implant showed an average deviation from the plan of 1.10 ± 0.69 mm and the apex of the placed implant showed a deviation of 1.12 ± 0.69 mm. Haptic robotic guidance showed greater precision than freehand, static computer-guided, and dynamic computer-guided implant placement. Conclusions: This large clinical series of 273 implants shows a high accuracy of implant placement compared to the published accuracy for angular deviations for any technology, as well as demonstrating statistically greater precision. Long-term clinical studies are necessary to establish the true effect of increased accuracy on clinical outcomes. Using haptic robotic guidance provides accurate implant placement while allowing additional benefits compared to computer-guided surgery, namely full visualization of the surgical field and the ability to change the plan intraoperatively. [ABSTRACT FROM AUTHOR]

The aim of this retrospective study was to evaluate the performance of implants placed with the All-on-4 technique after a mean time in function of 9 years. A total of 34 patients with 156 implants were selected for this study: 18 patients underwent tooth extraction on the day of implant placement (Group D), and 16 patients were already edentulous (Group E). A periapical radiograph was taken at a mean follow-up of 9 years (range: 5 to 14 years). Success and survival rates and the prevalence of peri-implantitis were calculated. Statistical analysis was used to assess comparisons between groups. After a mean follow-up of 9 years, the cumulative survival rate was 97.4% and the success rate was 77.4%. The difference between the initial and final radiographs resulted in a mean marginal bone loss (MBL) of 1.3 ± 1.06 mm (range: 0.1 to 5.3 mm). No differences were seen between Groups D and E. Peri-implantitis affected 15 implants (9.6%) in 9 patients (26.5%). This study shows that the All-on-4 technique is a reliable treatment method for both edentulous patients and patients requiring tooth extractions, with results maintained over a long follow-up period. The present MBL results are similar to those around implants in other rehabilitation types. [ABSTRACT FROM AUTHOR]

Radiological findings play an essential and complementary role in diagnosing Covid-19, assessing its severity, and managing its patients. Artificial intelligence technology based on medical imaging, which has made exciting developments by being applied in many areas, has become an area of interest for the rapid and accurate detection of the disease in the fight against the Covid-19 pandemic. The main difficulty is the inability to obtain a large dataset size with quality and standard images that neural networks need to perform well. Aiming at this problem, this study proposes a Siamese neural network-based deep learning framework for accurate diagnostics of Covid-19 using chest X-ray (CXR) images. The pre-trained VGG16 architecture, based on the transfer learning approach, forms the backbone of the Siamese neural network. The outputs of the backbones are joined together by a merging layer, and then the output passes through a fully connected layer. Based on this structure, category-aware Siamese-based models are produced for each class. The predictions from the models are combined using a voting mechanism to reduce the possibility of misclassification and to make better decisions. The framework was evaluated using a publicly available dataset for the 4-class classification task for Covid-19 pneumonia, lung opacity, normal, and non-Covid-19 viral pneumonia images. The findings reveal the high discrimination ability of the framework, trained using only 10 images per class in less training time, achieving an average test accuracy of 92%. Our framework, which learns a single Siamese-based pairwise model for each class, effectively captures class-specific features. Additionally, it has the potential to deal with data scarcity and long training time problems in multi-class classification tasks. [ABSTRACT FROM AUTHOR]

Background: Globally, digital transformation has been sweeping through healthcare in recent years. Reflecting this global change, Australia's health and social care sector is also undergoing rapid digitalisation. Digital approaches can enhance care planning and coordination activities, as well as improve efficiencies in documentation and coordination of care. As the aged care environment continues to become digitalised into the future, there is an expectation that nurses practise the delicate art and science of compassionate caregiving in a technology-proliferated environment where care planning, provisioning, and documenting require digital knowledge and skills. Aim: To explore the perspectives and expectations of nurses working in residential aged care setting regarding the utilisation of technology to enhance care at the end-of-life. Methods: A qualitative descriptive research study design based on the secondary analysis of data collected as part of a larger study. Data collection was conducted using six semi-structured interviews and 11 focus group discussions with care workers, nurses, and nursing managers working in Australian residential aged care setting. Results: A total of 64 participants took part in this study. Overall, four themes were generated from the data as following: engagement with various digital systems and platforms; 2) ambivalence toward technology; 3) challenges and concerns in technology use; and 4) anticipated technology roles in end-of-life care. This study found that, nurses in Australian RAC are open to engage with technologies for end-of-life provision, despite some ambivalence and challenges encountered in the process. Conclusion: Nurses in residential aged care have an important role in end-of-life care of many older Australians. Digital approaches offer care and coordination opportunities however require the aged care sector and nurses to take up these opportunities. While nurses demonstrate openness to technology, focus needs to be placed on technology use support. This presents an opportunity for nurses to actively shape the future of digital innovations in aged care, ensuring high-quality, compassionate care for residents in their final stages of life. [ABSTRACT FROM AUTHOR]

Organisms possess remarkably adaptive ability to complex environments. For example, chameleons can alter their skin color to adapt to varying environments, which has inspired significant advances in bioinspired soft electronic skins (E‐skins), and their wide applications in wearable sensors, intelligent robots, and health monitoring. However, current bioinspired E‐skins face challenges in ultra‐stretchability, high sensitivity, and long‐term stability owing to the intrinsic limitations associated with their mismatched interface between soft matrix and hard conductive fillers, hindering their practical applications. Here, it is reported that bioinspired structural color electronic skins (SC E‐skins) that consist of liquid metal particles (LMPs), periodical ordered colloidal crystal arrays, and ultra‐stretchable hydrogel, imparting synergistic and durable electrical–optical sensing capabilities. Such SC E‐skins demonstrate outstanding performances including superior flexibility (elongation at break > 1100%), high sensitivity (gauge factor = 3.26), fast synergetic electric–optical response time (≈100 ms), outstanding durability (over 1500 cycles), and high accuracy (R2 > 99.5%). These bioinspired SC E‐skins with excellent capability of converting mechanical signals into synergetic electrical–optical outputs hold great promise for smart wearable devices, affording a new horizon in developing advanced health monitoring technologies. [ABSTRACT FROM AUTHOR]

Background: The adoption of information and communication technology (ICT) has been the fastest and most widespread in the South Korean workplace. While ICT has several advantages, it can also cause stress among workers. However, the relationship between the introduction of ICT in the workplace and mental health problems among Korean workers remains unclear. Methods: We conducted a cross-sectional analysis of data obtained from the sixth Korean Working Conditions Survey (KWCS). In total, 40,019 participants answered a question about whether ICT had been introduced in their workplace. Among these, we compared the rate of complaints of mental health disorders between those who answered "yes" (n = 3,250) and those who answered "no" (n = 36,769). We analyzed the data using the SAS statistical software and calculated the odds ratios (ORs) and 95% confidence intervals (95% CIs) for anxiety, insomnia, and depression using multiple logistic regression models. We also stratified the data on Korean workers to assess the impact of ICT on subgroups. Results: In our nationally representative cohort, on experiencing ICT incorporation at work, 384 participants (11.8%) reported anxiety, 138 (4.2%) reported insomnia, and 296 (9.1%) reported depression. In contrast, among participants who did not have access to ICT, 1,929 (5.2%) reported anxiety, 702 (1.9%) reported insomnia, and 4,404 (12.0%) reported depression. The ORs (95% CIs) for anxiety and insomnia complaint rates were 2.47 (2.19–2.79) and 2.55 (2.10–3.10), respectively, among workers who experienced new ICT adaptations in comparison with those who did not. However, no significant relationship was observed between ICT adoption and depression. Conclusions: The causes of mental health problems in the workplace should be identified and addressed. We found that the introduction of ICT in the workplace was significantly related to anxiety and insomnia symptoms among Korean workers, after controlling for the selected covariates. This information can be used to identify subgroups in the workplace that are vulnerable to ICT changes and tailor interventions to their social and demographic profiles. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is increasingly used in health care to improve diagnostics and treatment. Decision‐making tools intended to help professionals in diagnostic processes are developed in a variety of medical fields. Despite the imagined benefits, AI in health care is contested. Scholars point to ethical and social issues related to the development, implementation, and use of AI in diagnostics. Here, we investigate how three relevant groups construct ethical challenges with AI decision‐making tools in prostate cancer (PCa) diagnostics: scientists developing AI decision support tools for interpreting MRI scans for PCa, medical doctors working with PCa and PCa patients. This qualitative study is based on participant observation and interviews with the abovementioned actors. The analysis focuses on how each group draws on their understanding of 'good health care' when discussing ethical challenges, and how they mobilise different registers of valuing in this process. Our theoretical approach is inspired by scholarship on evaluation and justification. We demonstrate how ethical challenges in this area are conceptualised, weighted and negotiated among these participants as processes of valuing good health care and compare their perspectives. [ABSTRACT FROM AUTHOR]

Simple Summary: In liver surgery, the complex and individualized nature of liver vascular anatomy makes planning and execution challenging. Traditional 2D intraoperative ultrasonography (IOUS) often suffers from interpretability issues due to noise and artifacts. This paper introduces an AI-based model, the "2D-weighted U-Net model," designed to enhance real-time IOUS navigation by accurately segmenting key blood vessels, including the inferior vena cava, hepatic veins, portal vein, and its major branches. Our deep learning model demonstrated high performance, with Dice scores ranging from 0.84 to 0.96 across different vessels. This advancement promises improved precision in liver resection procedures and sets the stage for future development of real-time multi-label segmentation for broader liver vasculature. Background/Objectives: In the field of surgical medicine, the planning and execution of liver resection procedures present formidable challenges, primarily attributable to the intricate and highly individualized nature of liver vascular anatomy. In the current surgical milieu, intraoperative ultrasonography (IOUS) has become indispensable; however, traditional 2D ultrasound imaging's interpretability is hindered by noise and speckle artifacts. Accurate identification of critical structures for preservation during hepatectomy requires advanced surgical skills. Methods: An AI-based model that can help detect and recognize vessels including the inferior vena cava (IVC); the right (RHV), middle (MHV), and left (LVH) hepatic veins; the portal vein (PV) and its major first and second order branches the left portal vein (LPV), right portal vein (RPV), and right anterior (RAPV) and posterior (RPPV) portal veins, for real-time IOUS navigation can be of immense value in liver surgery. This research aims to advance the capabilities of IOUS-guided interventions by applying an innovative AI-based approach named the "2D-weigthed U-Net model" for the segmentation of multiple blood vessels in real-time IOUS video frames. Results: Our proposed deep learning (DL) model achieved a mean Dice score of 0.92 for IVC, 0.90 for RHV, 0.89 for MHV, 0.86 for LHV, 0.95 for PV, 0.93 for LPV, 0.84 for RPV, 0.85 for RAPV, and 0.96 for RPPV. Conclusion: In the future, this research will be extended for real-time multi-label segmentation of extended vasculature in the liver, followed by the translation of our model into the surgical suite. [ABSTRACT FROM AUTHOR]

Simple Summary: Colorectal cancer is one of the most commonly diagnosed cancers worldwide and is the second leading cause of cancer-related deaths. In the last decade, significant advances have been made in the development of cell-based models of disease, including for culturing and expanding cells derived from patient biopsy tissue. These advances have led to the development of intestinal organoids, 3D cellular models of the colon, that more accurately represent the disease state and hold the potential to improve our understanding of disease processes. Furthermore, increasing the complexity of these models to include important components, such as the gut microbiome and the stromal compartment with immune cells and vasculature, will provide deeper insight into individual drug responses and lead to better patient outcomes in the future. Despite improvements in participation in population-based screening programme, colorectal cancer remains a major cause of cancer-related mortality worldwide. Targeted interventions are desirable to reduce the health and economic burden of this disease. Two-dimensional monolayers of colorectal cancer cell lines represent the traditional in vitro models for disease and are often used for diverse purposes, including the delineation of molecular pathways associated with disease aetiology or the gauging of drug efficacy. The lack of complexity in such models, chiefly the limited epithelial cell diversity and differentiation, attenuated mucus production, lack of microbial interactions and mechanical stresses, has driven interest in the development of more holistic and physiologically relevant in vitro model systems. In particular, established ex vivo patient-derived explant and patient-derived tumour xenograft models have been supplemented by progress in organoid and microfluidic organ-on-a-chip cultures. Here, we discuss the applicability of advanced culturing technologies, such as organoid systems, as models for colorectal cancer and for testing chemotherapeutic drug sensitivity and efficacy. We highlight current challenges associated with organoid technologies and discuss their future for more accurate disease modelling and personalized medicine. [ABSTRACT FROM AUTHOR]

In health technology assessment, matching‐adjusted indirect comparison (MAIC) is the most common method for pairwise comparisons that control for imbalances in baseline characteristics across trials. One of the primary challenges in MAIC is the need to properly account for the additional uncertainty introduced by the matching process. Limited evidence and guidance are available on variance estimation in MAICs. Therefore, we conducted a comprehensive Monte Carlo simulation study to evaluate the performance of different statistical methods across 108 scenarios. Four general approaches for variance estimation were compared in both anchored and unanchored MAICs of binary and time‐to‐event outcomes: (1) conventional estimators (CE) using raw weights; (2) CE using weights rescaled to the effective sample size (ESS); (3) robust sandwich estimators; and (4) bootstrapping. Several variants of sandwich estimators and bootstrap methods were tested. Performance was quantified on the basis of empirical coverage probabilities for 95% confidence intervals and variability ratios. Variability was underestimated by CE + raw weights when population overlap was poor or moderate. Despite several theoretical limitations, CE + ESS weights accurately estimated uncertainty across most scenarios. Original implementations of sandwich estimators had a downward bias in MAICs with a small ESS, and finite sample adjustments led to marked improvements. Bootstrapping was unstable if population overlap was poor and the sample size was limited. All methods produced valid coverage probabilities and standard errors in cases of strong population overlap. Our findings indicate that the sample size, population overlap, and outcome type are important considerations for variance estimation in MAICs. [ABSTRACT FROM AUTHOR]

This article presents an in-depth examination of recent advancements in medical and biotechnological sensing technologies, focusing on the forefront of innovation in hydrogel-based sensors within the domains of biomedical engineering and regenerative medicine. It delves into cutting-edge sensing technologies that facilitate non-invasive glucose monitoring, highlights progress in the development of intelligent solutions for wound care, and discusses the application of optical and fluorescence-based sensors for real-time diagnostics within the body. Further, it reviews the latest glucose monitoring devices, alongside wearable and implantable sensors designed for the continuous monitoring of health, including the measurement of physiological strain and stress. The exploration extends to the latest in non-invasive and minimally invasive technologies for ongoing health assessment, and to imaging and visualization techniques critical for medical diagnostics and therapeutic procedures. These advancements mark a pivotal move toward more efficient, precise, and patient-focused healthcare technologies, signaling new avenues for diagnosis, monitoring, and treatment in the healthcare sector. [ABSTRACT FROM AUTHOR]

Background: Symptomatic intracranial atherosclerotic stenosis (ICAS) is a major cause of ischemic stroke worldwide. In patients undergoing endovascular treatment for ICAS, in-stent restenosis (ISR) is associated with ischemic stroke recurrence. Objective: Intracranial drug-eluting self-expanding stent systems (COMETIU; Sinomed Neurovita Technology Inc., CHN) are new devices for treating ICAS. This study evaluated the perioperative experience and medium-term outcomes of COMETIU in 16 patients. Methods: We prospectively analyzed 16 patients with ICAS (≥ 70% stenosis) who underwent intravascular therapy between September 4, 2022, and February 1, 2023. The primary outcome was the incidence of ISR at 6 months postoperatively. The secondary efficacy outcomes were device and technical success rates. The secondary safety outcomes included stroke or death within 30 days after the procedure and the cumulative annual rate of recurrent ischemic stroke in the target-vessel territory from 31 days to 6 months and 1 year. Results: A total of 16 patients with 16 intracranial atherosclerotic lesions were treated with 16 COMETIUs. All procedures were performed under general anesthesia with 100% device and technical success rates, with no cases of periprocedural stroke or death. The mean radiographic follow-up duration was at least 6 months postoperatively, and all patients presented for radiographic and clinical follow-up. There were no reported ischemic or hemorrhagic strokes. Angiographic follow-up for all patients revealed no cases of ISR. Conclusion: COMETIU is safe and effective for treating ICAS, with minimal risk during the procedure and a low rate of ISR during medium-term follow-up. [ABSTRACT FROM AUTHOR]

Health technology assessment bodies advocate capturing the value of interventions in terms of their benefits to health and broader welfare. The wider societal impacts (WSI) framework considers how changes in health alter a person's net contribution to society—that is, what they produce minus what they consume. In this research, we review this framework and explore the scope to differentiate WSI by equity-relevant sociodemographic characteristics. This research updates previous calculations using publicly available data from population-based surveys in the United Kingdom. We then estimate for 199 chronic conditions: (1) WSI for the average person with the condition and (2) gain in WSI for an improvement of 0.1 in health-related quality of life score. The nature and availability of information varied across population-based surveys and precluded analyses to examine WSI by population subgroup. Our updated estimates mirrored earlier findings that consideration of the broader societal impacts of health would reprioritize interventions compared with assessment on health alone. For example, for the same improvement in health, a woman experiencing diseases of the circulatory system has the highest potential gain in WSI (£354/month) whereas a man experiencing HIV has the lowest potential gain (£233/month). The WSI framework provides a simple, indirect method to inform resource allocation decisions. Understanding the equity implications of this approach was hindered by differences in the information collected across population-based surveys. Findings demonstrate the potential reprioritization that may occur if the broader welfare benefits of health interventions were used to inform coverage decisions. • Health technology assessment agencies continue to indicate interest in capturing the value of health interventions in terms of both their benefits to health and societal welfare, but practical, reliable, and routine sources of this information are lacking. • Routine UK data sources inform the wider societal impacts framework, which provides a structured, indirect method to estimate the additional value beyond health in economic evaluation, but the equity implications beyond groups defined by age and sex remain unclear. • This article applies an updated wider societal impacts framework to 199 health conditions to indicate the reprioritization that might occur using a broader perspective for decision making. [ABSTRACT FROM AUTHOR]

Health technology assessment (HTA) of advanced therapy medicinal products (ATMPs), such as high-cost and one-time cell and gene therapies, is particularly challenging. Outcomes-based agreements (OBAs) are a potential solution to mitigate the risks while providing access to patients but are not widely used across Europe. This study aimed to develop policy recommendations to support the acceptability and implementation of OBAs in Europe. A policy sandbox approach was used to engage with stakeholders and explore how HTA organizations can support reimbursement decisions regarding OBAs for ATMPs. A panel of 38 experts from across the European region was convened in 2 workshops, representing payers, HTA organizations, patients, registries, and an industry trade body. Policy recommendations were developed to support the appropriate consideration of OBAs for reimbursing highly uncertain technologies, such as ATMPs. If a positive HTA recommendation cannot be made at the proposed price, then a simple price discount reflecting the uncertainty is preferred over complex solutions such as OBAs. If an OBA is pursued, it should be designed collaboratively with all stakeholders to understand data collection feasibility and minimize burden to patients and providers. Payers are encouraged to approach OBAs as a tool for informed decision making, including a readiness to make negative reimbursement decisions based on unfavorable evidence. The study presents a policy framework for using OBAs in reimbursement decisions. OBAs must be carefully designed, focusing on appropriateness and the burden of implementation. The relevant authorities should be committed to making decisions in light of the resulting evidence. • Outcomes-based agreements (OBAs) with further data collection offer a reimbursement solution for highly uncertain and innovative health technologies, generating evidence and supporting patient access. • However, OBAs are a complex solution that should be considered only if a simpler solution cannot be reached through standard health technology assessment processes and pricing solutions. • Burdens associated with implementing OBAs and data collection should be minimized by designing agreements in collaboration with all the stakeholders involved. [ABSTRACT FROM AUTHOR]

Objective: During the advent of laparoscopy, surgeons directly explored the abdominal cavity with a telescope-like device through a small incision. Since then, numerous technological advances have transformed minimally invasive surgery (MIS). Yet, in our wireless world, various devices crowd the surgical field, with long wires and light sources posing fall and fire risks. The primary objective of this study was to analyze the first reported human use of a novel wireless laparoscopy system or WLS (ArthroFree™, Lazurite®, Cleveland, Ohio). Methods: The utility and convenience of the WLS was assessed via two avenues: (1) by analyzing surgical outcomes from first human use and (2) by surveying healthcare professionals regarding its quality and utility. Results: Eighteen patients (mean age 44.2, 83.3% female, mean BMI 33.4) underwent operations with the WLS. Operations included gynecologic and general surgical procedures. There were no intraoperative or postoperative complications, and no conversions to traditional laparoscopy or laparotomy. Mean operating time was 71.94 ± 20.41 min, and estimated blood loss was minimal. Survey results revealed varied individual experiences. Strengths included adequate illumination, improved ergonomics, and simplicity of setup and ease of operation. One respondent criticized the image resolution. Feedback indicated an overall positive impact, and 67% of respondents supported inclusion of the device at their facility. Moreover, its deployment in resource-limited settings abroad has demonstrated its efficacy in global surgery, indicating its potential in various healthcare environments. Conclusions: This is the first reported human use of a novel WLS. Clinical results supported efficiency and safety of the technology. The successful deployment of the WLS in diverse surgical environments, including resource-limited settings, highlights its potential as a universally adaptable tool in global surgery. This report represents a strong first step toward a wireless operating room with the promise of redefining surgical standards as well as bridging gaps in surgical care worldwide. [ABSTRACT FROM AUTHOR]

Background: With the rapid advancement of medical imaging technologies, precise image analysis and diagnosis play a crucial role in enhancing treatment outcomes and patient care. Computed tomography (CT) and magnetic resonance imaging (MRI), as pivotal technologies in medical imaging, exhibit unique advantages in bone imaging and soft tissue contrast, respectively. However, cross‐domain medical image registration confronts significant challenges due to the substantial differences in contrast, texture, and noise levels between different imaging modalities. Purpose: The purpose of this study is to address the major challenges encountered in the field of cross‐domain medical image registration by proposing a spatial‐aware contrastive learning approach that effectively integrates shared information from CT and MRI images. Our objective is to optimize the feature space representation by employing advanced reconstruction and contrastive loss functions, overcoming the limitations of traditional registration methods when dealing with different imaging modalities. Through this approach, we aim to enhance the model's ability to learn structural similarities across domain images, improve registration accuracy, and provide more precise imaging analysis tools for clinical diagnosis and treatment planning. Methods: With prior knowledge that different domains of images (CT and MRI) share same content‐style information, we extract equivalent feature spaces from both images, enabling accurate cross‐domain point matching. We employ a structure resembling that of an autoencoder, augmented with designed reconstruction and contrastive losses to fulfill our objectives. We also propose region mask to solve the conflict between spatial correlation and distinctiveness, to obtain a better representation space. Results: Our research results demonstrate the significant superiority of the proposed spatial‐aware contrastive learning approach in the domain of cross‐domain medical image registration. Quantitatively, our method achieved an average Dice similarity coefficient (DSC) of 85.68%, target registration error (TRE) of 1.92 mm, and mean Hausdorff distance (MHD) of 1.26 mm, surpassing current state‐of‐the‐art methods. Additionally, the registration processing time was significantly reduced to 2.67 s on a GPU, highlighting the efficiency of our approach. The experimental outcomes not only validate the effectiveness of our method in improving the accuracy of cross‐domain image registration but also prove its adaptability across different medical image analysis scenarios, offering robust support for enhancing diagnostic precision and patient treatment outcomes. Conclusions: The spatial‐aware contrastive learning approach proposed in this paper introduces a new perspective and solution to the domain of cross‐domain medical image registration. By effectively optimizing the feature space representation through carefully designed reconstruction and contrastive loss functions, our method significantly improves the accuracy and stability of registration between CT and MRI images. The experimental results demonstrate the clear advantages of our approach in enhancing the accuracy of cross‐domain image registration, offering significant application value in promoting precise diagnosis and personalized treatment planning. In the future, we look forward to further exploring the application of this method in a broader range of medical imaging datasets and its potential integration with other advanced technologies, contributing more innovations to the field of medical image analysis and processing. [ABSTRACT FROM AUTHOR]

Adaptive radiotherapy (ART) is a recent development in radiotherapy technology and treatment personalization that allows treatment to be tailored to the daily anatomical changes of patients. While it was until recently only performed "offline", i.e. between two radiotherapy sessions, it is now possible during ART to perform a daily online adaptive process for a given patient. Therefore, ART allows a daily customization to ensure optimal coverage of the treatment target volumes with minimized margins, taking into account only the uncertainties related to the adaptive process itself. This optimization appears particularly relevant in case of daily variations in the positioning of the target volume or of the organs at risk (OAR) associated with a proximity of these volumes and a tenuous therapeutic index. ART aims to minimize severe acute and late toxicity and allows tumor dose escalation. These new achievements have been possible thanks to technological development, the contribution of new multimodal and onboard imaging modalities and the integration of artificial intelligence tools for the contouring, planning and delivery of radiation therapy. Online ART is currently available on two types of radiotherapy machines: MR-linear accelerators and recently CBCT-linear accelerators. We will first describe the benefits, advantages, constraints and limitations of each of these two modalities, as well as the online adaptive process itself. We will then evaluate the clinical situations for which online adaptive radiotherapy is particularly indicated on MR- and CBCT-linear accelerators. Finally, we will detail some challenges and possible solutions in the development of online ART in the coming years. [ABSTRACT FROM AUTHOR]

It is important to understand how technology can improve surgical performance – and what we shouldn't expect. [ABSTRACT FROM AUTHOR]

Tetrahedral framework nucleic acids (tFNAs) are renowned for their controllable self‐assembly, exceptional programmability, and excellent biocompatibility, which have led to their widespread application in the biomedical field. Beyond these features, tFNAs demonstrate unique chemical and biological properties including high cellular uptake efficiency, structural bio‐stability, and tissue permeability, which are derived from their distinctive 3D structure. To date, an extensive range of tFNA‐based nanostructures are intelligently designed and developed for various biomedical applications such as drug delivery, gene therapy, biosensing, and tissue engineering, among other emerging fields. In addition to their role in drug delivery systems, tFNAs also possess intrinsic properties that render them highly effective as therapeutic agents in the treatment of complex diseases, including arthritis, neurodegenerative disorders, and cardiovascular diseases. This dual functionality significantly enhances the utility of tFNAs in biomedical research, presenting valuable opportunities for the development of next‐generation medical technologies across diverse therapeutic and diagnostic platforms. Consequently, this review comprehensively introduces the latest advancements of tFNAs in the biomedical field, with a focus on their benefits and applications as drug delivery nanoplatforms, and their inherent capabilities as therapeutic agents. Furthermore, the current limitations, challenges, and future perspectives of tFNAs are explored. [ABSTRACT FROM AUTHOR]

Objectives: Published literature has levied criticism against the cost-minimisation analysis (CMA) approach to economic evaluation over the past two decades, with multiple papers declaring its 'death'. However, since introducing the requirements for economic evaluations as part of health technology (HTA) decision-making in 1992, the cost-minimisation analysis (CMA) approach has been widely used to inform recommendations about the public subsidy of medicines in Australia. This research aimed to highlight the breadth of use of CMA in Australia and assess the influence of preconditions for the approach on subsidy recommendations Methods: Relevant information was extracted from Public Summary Documents of Pharmaceutical Benefits Advisory Committee (PBAC) meetings in Australia considering submissions for the subsidy of medicines that included a CMA and were assessed between July 2005 and December 2022. A generalised linear model was used to explore the relationship between whether medicines were recommended and variables that reflected the primary preconditions for using CMA set out in the published PBAC Methodology Guidelines. Other control variables were selected through the Bolasso Method. Subgroup analysis was undertaken which replicated this modelling process. Results: While the potential for inferior safety or efficacy reduced the likelihood of recommendation (p < 0.01), the effect sizes suggest that the requirements for CMA were not requisite for recommendation. Conclusion: The Australian practice of CMA does not strictly align with the PBAC Methodology Guidelines and the theoretically appropriate application of CMA. However, within the confines of a deliberative HTA decision-making process that balances values and judgement with available evidence, this may be considered acceptable, particularly if stakeholders consider the current approach delivers sufficient clarity of process and enables patients to access medicines at an affordable cost. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this study is to compare different information retrieval methods that can be used to identify utility inputs for health economic models. Methods: The usual practice of using systematic review methods was compared with two alternatives (iterative searching and rapid review), using a health technology assessment (HTA) case study in ulcerative colitis (UC). We analysed whether there were differences in the utility values identified when using the alternative search methods. Success was evaluated in terms of time, burden and relevance of identified information. The identified utility values were tested in an executable health economic model developed for UC, and the model results were compared. Results: The usual practice of using systematic review search approaches identified the most publications but was also the least precise method and took longest to complete. The inclusion of data from the different search methods in the model did not lead to different conclusions across search methods. Conclusions: In this case study, usual practice was less efficient and resulted in the same health economic model conclusions as the alternative search methods. Further case studies are required to examine whether this conclusion might be generalisable. [ABSTRACT FROM AUTHOR]

The advancement of health apps and wearables has garnered substantial academic attention, particularly in examining why individuals decide to use or not use them. In response to the extensive body of research on this topic, we conducted a scoping review of 61 articles published from 2007 to 2022, aiming to examine the dominant theoretical frameworks and antecedents of health apps and wearables use. The findings demonstrated that the dominant theoretical frameworks within this domain were rooted in the human-computer interaction theories, notably the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology. Next, our review identified four levels of antecedents: technological, individual, societal, and policy. At the technological level, emphasis was placed on functionality, reliability, and technological infrastructure. Individual antecedents encompassed socio-demographics, personality traits, cognitive responses to benefits and risks, emotional and affective responses, self-efficacy, and digital literacy. Societal antecedents highlighted the role of social networks and social norms, while policy antecedents elaborated on laws, regulations, and guidelines that encouraged health technology adoption. Our discussion illuminated that the evolving trend of theoretical frameworks in health apps and wearables use research, initially rooted in human-computer interaction, is progressively moving toward more comprehensive perspectives. We further underscored the importance of delving into societal and policy antecedents, which often are overshadowed by the more commonly discussed technological and individual factors. In conclusion, we advocated for a multi-stakeholder collaborative network approach, as this would enable communication researchers to understand the use of health apps and wearables more comprehensively. [ABSTRACT FROM AUTHOR]

Immortality constitutes a very human desire and its pursuit arguably shapes prominent features of human societies. In the twenty-first century, capitalism develops technologies that promise immortality as indefinite survival. Scholars who study immortality often showcase the links between technology, social structures and immortality projects, but a critical inquiry is needed to examine how (techno-)capitalism creates immortality projects that expand the frontiers of capital in contemporary societies. In this article, I highlight how techno-capitalism configures three prominent immortality imaginaries: transhumanist digital immortality, radical biological life-extension, and cryonics. I identify three tendencies of techno-capitalism − 1) expanding commodification to new realms of life, 2) creating new forms of alienation and 3) subordinating life to the private accumulation of capital – and explain how they shape the immortality imaginaries. I argue that pursuing techno-capitalist immortality would induce significant harms for human beings, promising freedom from death but actually sustaining techno-capitalism's exploitative relations. [ABSTRACT FROM AUTHOR]

Aims: The present analysis estimated the cost‐effectiveness of treatment with the Tandem t: slim X2 insulin pump with Control IQ technology (CIQ) in children with type 1 diabetes in Sweden. Methods: A four‐state Markov model and probabilistic sensitivity analyses (PSA) were used to assess the cost‐effectiveness of CIQ use compared with treatment with multiple daily insulin injections (MDI) or continuous subcutaneous insulin infusion (CSII) in conjunction with CGM. Data sources included clinical input data from a recent retrospective, observational study, cost data from local diabetes supply companies and government agencies, and published literature. Outcomes measures were quality adjusted life years (QALYs) at 10, 20 and 30‐year time horizons based on cost per QALY and incremental cost‐effectiveness ratio (ICER). Results: A total of 84 type 1 diabetes children were included (CIQ, n = 37; MDI, n = 19; CSII, n = 28). For all time horizons, the use of CIQ was a dominant strategy (e.g. more effective and less costly) compared with MDI or CSII use: 10‐year ICER, SEK ‐88,010.37 and SEK ‐91,723.92; 20‐year ICER, SEK −72,095.33 and SEK −87,707.79; and 30‐year ICER, SEK −65,573.01 and SEK ‐85,495.68, respectively. PSA confirmed that CIQ use was less costly compared with MDI and CSII. Conclusions: Initiation of CIQ use in children with type 1 diabetes is cost‐saving, besides previously shown improved glycaemic control, and increased quality of life. Further investigations are needed to more fully elucidate the cost‐effectiveness of these technologies in different countries with existing differences in payment models. [ABSTRACT FROM AUTHOR]

Aims: There are marked inequities in clinical outcomes and rates of diabetes technology use among youth with type 1 diabetes (T1D). The quantitative data from our mixed methods cohort study identified significant improvements in glycaemia and quality of life in participants. We aimed to use qualitative methods to provide further insight into our quantitative findings in the setting of underlying health disparities. Methods: Fifteen publicly insured, insulin pump‐naïve non‐Hispanic Black youth aged 6–21 years with T1D and baseline haemoglobin A1c (HbA1c) ≥86 mmol/mol (10%) and their parents participated in a mixed methods cohort study. Semi‐structured interviews were conducted separately with parents and youth after completion of 6 months of HCL use. Three topic areas were explored: (1) Experience using HCL, (2) barriers to HCL and (3) facilitators to accessing HCL. Semantic content analysis and consensus coding involving two team members were used to generate themes. Thematic saturation was achieved. Results: Youth (Medianage 14.9 years, 67% female) and parents (92% female) were interviewed. Youth and their parents reported that access to HCL provides a new outlook on living with T1D, although managing T1D is still hard. They felt that diabetes technology is most helpful for those struggling with management. Participants experienced barriers to access including misconceptions of HCL systems, clinician bias and systemic racism. They suggested these barriers can be overcome by offering diabetes technology education for all people with T1D, increasing awareness of HCL in the community and providing resources to overcome barriers created by social determinants of health. Conclusions: The voices of historically minoritised youth with suboptimal T1D control and their parents provide important, previously unreported experiences and perspectives on barriers and facilitators to using HCL that will shape interventions to improve equity in access to diabetes technology. [ABSTRACT FROM AUTHOR]

The Internet offers help-seekers an abundance of resources that can potentially support their mental health. However, people often have trouble in finding personally relevant resources. Very few specialized mental health help-seeking technologies currently address this need. Instead, help-seeking typically begins with a known website or web-search. To understand how help-seeking technologies could be better designed, we conducted a study comparing existing technologies with two novel help-seeking prototypes. These prototypes were informed by empirically grounded design recommendations, which emphasize the need for connectedness, accessible and creditable information, personalization, and immediacy. Findings show that the resources recommended by current technologies are often experienced as 'overwhelming' or 'overly-medicalized'. The prototypes successfully improved connectedness, immediacy, and personalization, but were less successful in their ability to support symptom-based help-seeking. Based on our findings we contribute recommendations for integrated help-seeking technologies, which can guide people to mental health resources appropriate to both their personal preferences and current level of need. [ABSTRACT FROM AUTHOR]

• STAND is an evidence-based treatment for adolescent ADHD that focuses on engagement. • Provides a summary of the development of a community implementation model for STAND. • Features include use of artificial intelligence to monitor fidelity and digitizing clinician resources. • Pilot work in three community agencies suggests acceptability, feasibility, and fidelity to the model. Implementation of behavior therapy for ADHD faces challenges in community settings. We describe development of a community-based implementation model for adolescent ADHD behavior therapy (Supporting Teens' Autonomy Daily; STAND) blended with Motivational Interviewing (MI). A stakeholder-engaged development approach is used based on the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. Resulting model features include: (a) task-shifting supervision from experts to agency supervisors, (b) holding bi-weekly technical assistance drop-ins to provide training and implementation supports, (c) MI integrity monitoring and feedback by artificial intelligence (AI), (d) AI-generated metrics for STAND content fidelity, (e) digitizing resources (manual, worksheets, tips, videos) on a clinician dashboard, (f) creating visual displays of feedback using badges and graphs, and (g) adding a rapport-building session prior to manualized content. We conducted stakeholder focus groups (N = 32) and two pilot studies to evaluate the new STAND AI measurement tool and revised service-delivery model (N = 6 therapists, 7 youth and parents, 3 agency supervisors). Results revealed advantages and disadvantages of the model, supported the promise of a STAND AI fidelity measurement tool, and indicated initial feasibility, acceptability, and agency engagement in STAND's community-based implementation model. We discuss future directions for continued iterative development and testing. Video examples are included as supplementary material. [ABSTRACT FROM AUTHOR]

In musculoskeletal and sports medicine, pain has traditionally been linked to tissue injury, often assuming a linear correlation between tissue damage and pain intensity. However, modern pain science has illuminated the complexity of the human pain experience, incorporating psychosocial elements, nervous system sensitization, immune responses, and structural changes in the brain as factors. This contemporary understanding of pain has proven highly beneficial for both clinicians treating individuals in pain and those experiencing pain. Pain neuroscience education (PNE) provides individuals in pain with an understanding of the underlying neurobiology and neurophysiology of their pain experience, which has been shown to result in decreased self-reported pain, reduced disability, the alleviation of fear and fear-avoidance behaviors, diminished pain catastrophizing, and improved movement. Currently, research on PNE predominantly focuses on interventions with individuals with persistent or chronic pain conditions. However, those who experience acute, sub-acute, and perioperative pain also have the potential for elevated levels of fear, fear-avoidance, and pain catastrophizing, indicating potential benefits from PNE. This invited commentary seeks to inform readers about the latest advancements in pain science and propose a conceptual model for delivering PNE in acute pain experiences. Level of Evidence 5 [ABSTRACT FROM AUTHOR]

Liquid biopsy techniques using cell-free DNA (cfDNA) play an increasingly important role in the characterization and surveillance of solid tumors. For blood cancers, molecular response assessment techniques using circulating malignant cells or bone marrow aspirates are well established in clinical care. However, cfDNA has an emerging role in hematology as well, with the opportunity for disease assessment and quantification independent of circulating disease burden or invasive biopsies. In this review, we discuss key technologies and clinical data for the utilization of cfDNA in lymphomas, myeloma, and leukemias. Reviewing the emerging role of cell-free DNA liquid biopsies in lymphomas, myelomas, and leukemias. [ABSTRACT FROM AUTHOR]

Background With the rapid advancement of deep learning network technology, the application of facial recognition technology in the medical field has received increasing attention. Objective This study aims to systematically review the literature of the past decade on facial recognition technology based on deep learning networks in the diagnosis of rare dysmorphic diseases and facial paralysis, among other conditions, to determine the effectiveness and applicability of this technology in disease identification. Methods This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for literature search and retrieved relevant literature from multiple databases, including PubMed, on 31 December 2023. The search keywords included deep learning convolutional neural networks, facial recognition, and disease recognition. A total of 208 articles on facial recognition technology based on deep learning networks in disease diagnosis over the past 10 years were screened, and 22 articles were selected for analysis. The meta-analysis was conducted using Stata 14.0 software. Results The study collected 22 articles with a total sample size of 57 539 cases, of which 43 301 were samples with various diseases. The meta-analysis results indicated that the accuracy of deep learning in facial recognition for disease diagnosis was 91.0% [95% CI (87.0%, 95.0%)]. Conclusion The study results suggested that facial recognition technology based on deep learning networks has high accuracy in disease diagnosis, providing a reference for further development and application of this technology. [ABSTRACT FROM AUTHOR]

The adoption of mobile health technology can assist in enhancing the quality of life of elderly people. Over the last few years, though mHealth research has expanded, the usage of mHealth applications among elderly is still minimal. This study aims to evaluate mobile user interfaces in mHealth applications and elicit the key ageing barriers that limit the use of such apps among the elderly. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) technique was used whereby 28 papers were identified and examined out of 742. In addition to the systematic review, an experiment of using 5 existing mHealth apps was conducted with 10 individuals within the age group of 60–79 to determine additional ageing barriers and usage challenges. The Questionnaire for User Interface Satisfaction approach was used to prepare a questionnaire to assess the overall system satisfaction and was provided to participants after one week of usage. In this work, issues with the user interface that impact the elderly have been highlighted. Three important ageing barriers hindering the use of mHealth among the elderly have been identified via PRISMA, namely physical, cognitive and perspective. Empirical findings from the experiment carried out further consolidate the findings obtained from the PRISMA approach. This study's investigation emphasised the performance of older persons with mHealth apps, their needs and the challenges they confront when adopting mHealth technologies. As a result, technology designers will benefit from this information when developing and designing mHealth apps and services that are suitable for older adults. [ABSTRACT FROM AUTHOR]

Introduction: Identifying key differences between manufacturers' submitted analysis and economic reanalysis by the Canadian Agency for Drugs and Technologies in Health (CADTH) is an important step toward understanding reimbursement recommendations. We compared economic values reported in manufacturers' analysis with the CADTH reanalysis and also assessed methodological critiques. Methods: Two reviewers extracted data from the clinical and economic reports in publicly available CADTH reports from 2018 to 2022. We used the Wilcoxon rank-sum test to assess the difference between mean economic values, and the Chi-square test to assess the association between the CADTH critique final recommendations. Results: Of the total submissions, 99.4% included effectiveness critiques, 88.8% included model structure critiques, 69.1% included utility score critiques, and 78.7% included cost critiques. The median incremental cost-utility ratio (ICUR) in the manufacturers' analyses was $138,658/quality-adjusted life-year (QALY), 2.5-fold lower than the CADTH's reanalysis at $380,251/QALY (p < 0.001). The median CADTH reanalysis for 3-year budget impact analysis (BIA) was $4,575,102, which was 27% higher than the manufacturers' submitted 3-year BIA (p < 0.001). CADTH requested a price reduction for 95% of all submissions, and the median price reduction request was 63.5%. In 2021 and 2022, the willingness-to-pay threshold identified in CADTH reports remained constant at $50,000 per QALY gained for all medications. Conclusion: There was high frequency of CADTH critiques on manufacturers' submissions in all four aspects of economic submissions: effectiveness, cost, utility score and structure. We observed a higher median incremental cost and lower median incremental QALYs in the CADTH reanalysis compared with the manufacturers' submissions. The resulting higher ICUR in the CADTH reanalysis often leads to a recommendation that the manufacturer needs to reduce its price. The 3-year budget impact was higher in the CADTH reanalyses compared with manufacturers' submissions. [ABSTRACT FROM AUTHOR]

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Introduction: We developed a mobile phone logbook application called LogMyScan. The study objectives were to evaluate a number of domains as per the technology acceptance model. Methods: A prospective pilot cohort study was conducted using a version of LogMyScan and reported according to Statement on Reporting of Evaluation studies in Health Informatics. Prospective participants were directed to a website which provided information and links. Participants were recruited via email distribution lists and social media. A task list was provided, which included entering simulated scan data and navigating the application, prior to completing a questionnaire. Questions were aligned to technology acceptance model domains, and Cronbach's alpha was calculated to assess reliability. Results: There were 49 participants and 7 were unable to complete the image upload and editing step. Levels of agreement for usability, educational and governance/security aspects were generally good. The desirability of existing and proposed features was high for images and video clip handling, feedback and reflection and less for motivational notifications and benchmarking. Comments identified some limitations and issues on the Android platform. Cronbach's alpha demonstrated good to excellent reliability: 0.87 (95% confidence interval, 0.79–0.91) for usability, educational and governance/security and 0.86 (95% confidence interval, 0.78–0.91) for desirability of existing and proposed features. Conclusion: There was generally high perceived ease of use, usefulness, attitude towards use and behavioural intention to use LogMyScan. Further research is needed to evaluate LogMyScan for future iterations, allowing recruitment of a larger number of participants and evaluating user expectations prior to using, and at timed intervals after using. [ABSTRACT FROM AUTHOR]

Predicting abnormalities of wearable medical devices plays a very important role in saving the lives and health of patients. This importance has opened new horizons for researchers with the development of newer algorithms. The long-term memory algorithm (LSTM) is one of the most important methods that are a special type of recurrent neural network (RNN) that has a high ability in this field and greatly increases the accuracy of correct and incorrect prediction of these abnormalities. In the current research, by using this algorithm and taking into account different parameters, the anomalies related to the sensors of the research field were determined. The results showed that there are influential parameters in the construction of this architecture, which include 3 very important factors: the number of neurons in the LSTM layer, the batch size, and the activation function. Also, the LSTM architecture together with the Dropout layer, with parameters Batch size = N = 128 and Tanh activation function shows a better performance and the lowest amount of error (MAPE) as well as the amount of the calculated mean square error (RMSE) in determining the anomaly. have sensors in the medical field. Investigations related to the results of 16 repetitions of optimization also showed that the process of reducing errors in the correct and incorrect identification of anomalies in the training phase has reached its lowest level with the increase in the number of tests, which shows the optimality and appropriateness of the work process. Therefore, this algorithm has a very good ability to identify errors in sensors and medical wearables, and it will be of great help in identifying the possible failure of sensors, and critical conditions of the patient, informing and finally helping patients in time. [ABSTRACT FROM AUTHOR]

Background: This study introduces a novel surgical instrument to reduce iatrogenic nerve injuries during procedures such as carpal tunnel and ulnar nerve decompression surgery. These injuries often result from direct damage to surrounding tissues by surgical instruments, whose designs have remained largely unchanged over the past decades. The novel device is a modified surgical forceps that has a deployable surgical scalpel that runs along a groove on the forceps. This design protects important anatomical structures while allowing fast dissection and cutting of fascial layers. Methods: The process used to develop a novel instrument included computer-aided design (CAD) modeling, 3D printing for prototyping, and the fabrication of an aluminum prototype. Biomechanical testing was performed with the novel device, iris scissors, bandage scissors, and a scalpel on an MTS Static Materials Test System. The peak force to slide-cut, number of cut attempts, and percentage cut on first attempt were compared between the prototype and traditional surgical tools. The materials cut in testing were Ace™ bandage, stockinette, and gauze. Statistical analyses were performed using Welch's t-tests and Fisher's exact tests. Results: Compared to conventional bandage and iris scissors, the novel surgical instrument required significantly less force to cut through an Ace™ bandage, stockinette, and gauze (p < 0.01). The number of cuts required to transect those same materials with the novel device was comparable to that of the scalpel and bandage scissors. Additionally, while there were no differences between the novel device and the other devices for an Ace™ bandage and stockinette, the novel device tended to cut a greater percentage of gauze in one pass than did the iris scissors. Conclusion: The novel surgical instrument designed in this study required less force compared to conventional scissors, demonstrated cutting efficiency similar to that of a scalpel blade, and had more safety features than either instrument. This study highlights the value of collaboration between biomedical engineering and orthopedic surgery departments on innovation in medical technology, through which new technologies with improved design and functionality demonstrate the potential to reduce iatrogenic injuries. [ABSTRACT FROM AUTHOR]

Health authorities using cost-effectiveness analysis (CEA) for informing reimbursement decisions on health technologies increasingly require economic evaluations encompassing both CEA and budget impact analysis (BIA). Good Research Practices advocate that the economic and clinical assumptions underlying these analyses are aligned and consistently applied. Nonetheless, CEAs and BIAs often are stand-alone analyses used in different stages of the decision-making process. This article used policy reports and Ministerial correspondence to discuss and elucidate the role of budget impact and its relationship with cost-effectiveness in reimbursement decisions in the Netherlands. The results indicate that CEAs and BIAs are both considered important for informing these decisions. While the requirements regarding CEAs—and application of the associated decision rule—are consistent across the different stages, the same does not hold for BIAs. Importantly, the definition of and evidence on budget impact differs between stages. Some important aspects (e.g. substitution and saving effects) typically are considered in the assessment and appraisal stages but are seemingly not considered in price negotiations and the final reimbursement decision. Further research is warranted to better understand why BIAs are not aligned with CEAs (e.g. in terms of underlying assumptions), vary in form and importance between stages, and do not have a clear relationship with the results of CEAs in the decision-making framework. Improving the understanding of the circumstances under which decision-makers attach a relatively larger or smaller weight to (different aspects of) budget impact may contribute to increasing the transparency, consistency, and optimality of reimbursement decisions in the Netherlands. [ABSTRACT FROM AUTHOR]

Our objective was to compare AOTMiT (Polish: Agencja Oceny Technologii Medycznych i Taryfikacji) recommendations to other HTA (Health Technology Assessment) agencies for newly registered drugs and new registration indications issued by the European Medicines Agency between 2014 and 2019. The study aims to assess the consistency and justifications of AOTMiT recommendations compared to that of other HTA agencies in 11 countries. A total of 2496 reimbursement recommendations published by 12 HTA agencies for 464 medicinal products and 525 indications were analyzed. Our analysis confirmed that the Polish AOTMiT agency seems to bear the closest resemblance to the corresponding HTA agencies from Canada (CADTH) and New Zealand (PHARMAC), when it comes to the outcome of HTA recommendations (positive or negative). Poland had a general scheme for justifying recommendations, similar to that of Ireland—four aspects (i.e., clinical efficacy, safety profile, cost-effectiveness, and impact on the payer's budget) are important for Poland when formulating the final decision. Compared to other countries, Poland shows a noticeably different pattern of justifying reimbursement recommendations, as revealed primarily in terms of budget impact and somewhat less so for cost-effectiveness rationales. [ABSTRACT FROM AUTHOR]

Aim: This paper reports the first estimation of an SF-6D value set based on the SF-12 for Spain. Methods: A representative sample (n = 1020) of the Spanish general population valued a selection of 56 hypothetical SF-6D health states by means of a probability lottery equivalent (PLE) method. The value set was derived using both random effects and mean models estimated by ordinary least squares (OLS). The best model was chosen on the basis of its predictive ability assessed in terms of mean absolute error (MAE). Results: The model yielding the lowest MAE (0.075) was that based on main effects using OLS. Pain was the most significant dimension in predicting health state severity. Comparison with the previous SF-6D (SF-36) model estimated for Spain revealed no significant differences, with a similar MAE (0.081). Nevertheless, the new SF-6D (SF-12) model predicted higher utilities than those generated by the SF-6D (SF-36) scoring algorithm (minimum value − 0.071 vs − 0.357). Conclusion: A value set for the SF-6D (SF-12) based on Spanish general population preferences elicited by means of a PLE technique is successfully estimated. The new estimated SF-6D (SF-12) preference-based measure provides a valuable tool for researchers and policymakers to assess the cost-effectiveness of new health technologies in Spain. [ABSTRACT FROM AUTHOR]

Motion artifacts can be a significant noise source in biomagnetic measurements when magnetic sensors are not separated from the signal source. In ambient environments, motion artifacts can be up to ten times stronger than the desired signals, varying with environmental conditions. This study evaluates the variability of these artifacts and the effectiveness of a gradiometer in reducing them in such settings. To achieve these objectives, we first measured the single channel output in varying magnetic field conditions to observe the effect of homogeneous and gradient background fields. Our analysis revealed that the variability in motion artifact within an ambient environment is primarily influenced by the gradient magnetic field rather than the homogeneous one. Subsequently, we configured a gradiometer in parallel and vertical alignment with the direction of vibration (X-axis). Our findings indicated that in a gradient background magnetic field ranging from 1 nT/mm to 10 nT/mm, the single-channel sensor output exhibited a change of 164.97 pT per mm unit increase, while the gradiometer output showed a change of only 0.75 pT/mm within the same range. Upon repositioning the gradiometer vertically (Y direction), perpendicular to the direction of vibration, the singlechannel output slope increased to 196.85 pT, whereas the gradiometer output only increased by 1.06 pT/mm for the same range. Our findings highlight the influence of ambient environments on motion artifacts and demonstrate the potential of gradiometers to mitigate these effects. In the future, we plan to record biomagnetic signals both inside and outside the shielded room to compare the efficacy of different gradiometer designs under varying environmental conditions. [ABSTRACT FROM AUTHOR]

Background: Advanced health technologies that emerge with the development of technology have an impact on health systems. This study aimed to determine the effects of these technologies on Türkiye's health system and present policy recommendations to reshape Türkiye's health system and policies accordingly. Methods: Interviews were conducted with senior managers, bureaucrats, policy-makers and decision-makers from seven different institutions on the subject. Content analysis was performed on the data obtained and evaluative categories were established. Results: It was concluded that these technologies would not have a positive impact on two identified themes, a negative impact on seven themes and a predominant impact on five themes in Türkiye. Conclusions: To adapt to the new health ecosystem in Türkiye, it is recommended to increase digital literacy, conduct economic evaluations of technologies, promote domestic production, ensure up-to-date follow-up, collaborate with the engineering field, enhance health technology evaluation practices, improve access to technologies and ensure that the infrastructures of health institutions are compatible with technologies. Various policy suggestions have been presented for the development of Türkiye's health system. Highlights: We believe that the healthcare ecosystem of the future will revolve around three main elements. Challenges in health, health technologies and health policies. Our focus is on understanding how future health technologies will impact health systems. We provide policy recommendations to address these impacts. [ABSTRACT FROM AUTHOR]

For successful health technology innovation and implementation it is key to, in an early phase, understand the problem and whether a proposed innovation is the best way to solve the problem. This review performed an initial exploration of published tools that support innovators in academic research and early stage development with awareness and guidance along the end-to-end process of development, evaluation and implementation of health technology innovations. Tools were identified from scientific literature as well as in grey literature by non-systematic searches in public research databases and search engines, and based on expert referral. A total number of 14 tools were included. Tools were classified as either readiness level tool (n = 6), questionnaire/checklist tool (n=5) or guidance tool (n = 3). A qualitative analysis of the tools identified 5 key domains, 5 innovation phases and 3 implementation principles. All tools were mapped for (partially) addressing the identified domains, phases, and principles. The present review provides awareness of available tools and of important aspects of health technology innovation and implementation (vs. nontechnological or non-health related technological innovations). Considerations for tool selection include for example the purpose of use (awareness or guidance) and the type of health technology innovation. Considerations for novel tool development include the specific challenges in academic and early stage development settings, the translation of implementation to early innovation phases, and the importance of multi-disciplinary strategic decisionmaking. A remaining attention point for future studies is the validation and effectiveness of (self-assessment) tools, especially in the context of support preferences and available support alternatives. [ABSTRACT FROM AUTHOR]

Background: Mobile health technologies are increasingly acknowledged as a cost-effective and convenient means of delivering top-notch healthcare services to patients in low- and middle-income countries. This research explores the utilization of mobile health applications in managing, monitoring, and self-care for adult diabetes mellitus (DM) patients. The objective is to gain insight into how diabetic patients currently utilize Mobile health applications for selfmanagement and their inclination to use them in the future. Methods: The authors conducted a systematic review and meta-analysis using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. They included articles that reported on the use of mobile/smartphone applications for diabetic mellitus disorders, focusing on ownership, application use, future interest in use, and use patterns. The search was conducted in the PubMed, Web of Science, Embase, and SCOPUS electronic databases, with various published articles from January 2016 up to February 2024. The methodological quality was evaluated using the Joanna Briggs Institute critical appraisal tool. Statistical techniques were applied, including the heterogeneity test, publication bias assessment, Egger's test, and funnel plots. The pooled prevalence was calculated using meta-analysis proportion with a random-effects model. Results: Thirteen studies were included, out of 4568 recognized articles. The pooled prevalence of mobile health application use for current diabetic management self-management, future interest in using the application for diabetic disorder self-management, and lack of belief in mobile health application users for self-management was 35%, 57%, and 39%, respectively. We observed significant heterogeneity (I2 = 97.7, p=<0.001), but no significant publication bias was detected on Egger's test. Conclusions: Our meta-analysis results show that over one-third of individuals use mobile health applications for diabetic self-management, and more than half of individuals would like to manage their diabetes mellitus in the future by using mobile health applications. These mobile health apps may be promising in future diabetes mellitus self-management. However, we still need to study the effectiveness of these apps. In addition, adopting mobile health apps based on the cultural context makes this self-management more achievable, practical, and impactful for individuals with diabetes. [ABSTRACT FROM AUTHOR]

Novel products applying artificial intelligence (AI)-based methods to digital pathology images are touted to have many uses and benefits. However, publicly available information for products can be variable, with few sources of independent evidence. This review aimed to identify public evidence for AI-based products for digital pathology. Key features of products on the European Economic Area/Great Britain (EEA/GB) markets were examined, including their regulatory approval, intended use, and published validation studies. There were 26 AI-based products that met the inclusion criteria and, of these, 24 had received regulatory approval via the self-certification route as General in vitro diagnostic (IVD) medical devices. Only 10 of the products (38%) had peer-reviewed internal validation studies and 11 products (42%) had peer-reviewed external validation studies. To support transparency an online register was developed using identified public evidence (https://osf.io/gb84r/), which we anticipate will provide an accessible resource on novel devices and support decision making. [ABSTRACT FROM AUTHOR]

Background: Telemedicine holds immense potential to revolutionise healthcare delivery, particularly in resource-limited settings and for patients with chronic diseases. Despite proven benefits and policy reforms, the use of telemedicine remains low due to several patient, technology, and system-level barriers. Assisted telemedicine employs trained health professionals to connect patients with physicians, which can improve access and scope of telemedicine. The study aims to describe the design, service utilisation and chronic disease outcomes following the implementation of an assisted telemedicine initiative. Methods: This is an observational implementation study. Barriers and potential solutions to the implementation of telemedicine were identified through interviews with key stakeholders. The assisted telemedicine solution using an interoperable platform integrating electronic health records, point-of-care diagnostics, and electronic clinical decision support systems was designed and piloted at three telemedicine clinics in Tamil Nadu, India. Nurses were trained in platform use and facilitation of tele-consultations. Health records of all patients from March 2021 to June 2023 were included in the analysis. Data were analysed to assess the utilisation of clinic services and improvements in health outcomes in patients with diabetes mellitus and hypertension. Results: Over 2.4 years, 11,388 patients with a mean age of 45 (± 20) years and median age of 48 years, predominantly female (59.3%), accessed the clinics. The team completed 15,437 lab investigations and 26,998 consultations. Among 5542 (48.6%) patients that reported chronic conditions, diabetes mellitus (61%) and hypertension (45%) were the most frequent. In patients with diabetes mellitus and hypertension, 43% and 75.3% were newly diagnosed, respectively. Diabetes mellitus and hypertension patients had significant reductions in fasting blood sugar (-33.0 mg/dL (95% CI (-42.4, -23.7, P < 0.001)), and systolic (-9.6 mmHg (95% CI (-12.1, -7.0), P < 0.0001)) and diastolic blood pressure (-5.5 mmHg (95% CI (-7.0, -4.08), P < 0.0001)) at nine months from first visit, respectively. Conclusions: The 'Digisahayam' model demonstrated feasibility in enhancing healthcare accessibility and quality by bridging healthcare gaps, diagnosing chronic conditions, and improving patient outcomes. The model presents a scalable and sustainable approach to revolutionising patient care and achieving digital health equity, with the potential for adaptation in similar settings worldwide. [ABSTRACT FROM AUTHOR]