Search Results (765)

The agony of medical negligence for all involved is well documented. Health practitioners involved in harm events are described in the literature as “second victims”. Injured patients report that clinical negligence litigation is traumatic, slow, expensive, and does not meet their needs. Clinical negligence lawyers have complained that healthcare injury cases are so complex and expensive that many firms do not accept these cases. This article uses a qualitative case study research design to analyse two cases from the United States of America (US) to explore the promise of an alternative resolution process: the communication-and-resolution program (CRP). CRPs involve the hospital disclosing the healthcare injury, investigating and explaining what happened, apologising and, sometimes, offering compensation to injured patients and families. In the US, CRPs have not replaced tort law. The two case studies analysed in this article offer a rare insight into the accounts of those who have experienced clinical negligence and an alternative non-litigation approach. The case study approach delves into the detail, providing an in-depth glimpse into the complexity of healthcare injuries in their real-life context. The case studies provide valuable lessons for reshaping resolution processes to better meet injured patients’ needs. Full article

Background: Cardiac implantable electronic device (CIED) infections necessitate extraction and subsequent pacing interventions. Conventional methods after removing the infected CIED system involve temporary or semi-permanent pacing followed by delayed permanent pacemaker (PPM) implantation. Leadless pacemakers (LPs) may offer an alternative, allowing immediate PPM implantation without increasing infection risks. Our objective is to evaluate the safety and cost-effectiveness of LP implantation during the same procedure of CIED extraction, compared to conventional two-stage approaches. Methods: Pacemaker-dependent patients with systemic or pocket infection undergoing device extraction and LP implantation during the same procedure at Sheba Medical Center, Israel, were compared to a historical group of patients undergoing a semi-permanent (SP) pacemaker implantation during the procedure, followed by a permanent pacemaker implantation. Results: The cohort included 87 patients, 45 undergoing LP implantation and 42 SP implantation during the extraction procedure. The LP group demonstrated shorter intensive care unit stay (1 ± 3 days vs. 7 ± 12 days, p < 0.001) and overall hospital days (11 ± 24 days vs. 17 ± 17 days, p < 0.001). Rates of infection relapse and one-year mortality were comparable between groups. Economic analysis revealed comparable total costs, despite the higher initial expense of LPs. Conclusions: LP implantation during CIED extraction offers significant clinical and logistical advantages, including reduced hospital stays and streamlined treatment, with comparable safety and cost-effectiveness to conventional approaches. Full article

Antimicrobial resistance is an escalating global threat that undermines the efficacy of modern antibiotics and places a substantial economic burden on healthcare systems—costing Europe alone over EUR 11.7 billion each year due to rising medical expenses and productivity losses. While genomics and transcriptomics have significantly advanced our understanding of the genetic foundations of resistance, they often fail to capture the dynamic, real-time adaptations that enable bacterial survival. Proteomics, particularly mass spectrometry-based strategies, bridges this gap by uncovering the functional protein-level changes that drive resistance, persistence, and tolerance under antibiotic pressure. In this review, we examine how proteomic approaches provide new insights into resistance mechanisms across various antibiotic classes, with a particular focus on β-lactams, aminoglycosides, and fluoroquinolones, highlighting clinically relevant pathogens, especially members of the ESKAPE group. Finally, we examine future directions, including the integration of proteomics with other omic technologies and the growing role of artificial intelligence in resistance prediction, paving the way for more predictive, personalized, and effective solutions to combat antimicrobial resistance. Full article

The research presented in this paper is based on the need for personalized medical implants, whose serial production is impossible, so the need for production process adjustments is inevitable. Conventional production technologies usually set geometrical limitations and generate a lot of waste material, which leads to great expenses, especially when the material used for production is an expensive Ti alloy. Additive technologies offer the possibility to produce a product almost without waste material and geometrical limitations. Nevertheless, the methods developed for additive production using metal powder are not significantly used in biomedicine because there is insufficient data published regarding the properties of additively produced parts, especially from the fatigue and fracture standpoint. The aim of this research is the experimental determination of fracture mechanics properties of additively produced parts and their comparison with the properties of parts produced by conventional technologies. Drawing is the first production process in the comparison, and the second one is selective laser melting (SLM). The Ti-alloy Ti-6Al-7Nb, used for medical implants, was selected for this research. Experimental testing was performed in order to determine ΔK_th fracture mechanics parameters and resistance curves according to ASTM E1820. Test specimen dimensioning and the experiments were carried out according to the respective standards. For the drawn test specimen, the value obtained was ΔK_th = 3.84 MPam^0.5, and the fracture toughness was K_c = 84 MPam^0.5, while for SLM produced test specimens the values were ΔK_th = 4.53 MPam^0.5, and K_c = 21.9 MPam^0.5. Full article

There are numerous methods available for evaluating leg length discrepancy (LLD), ranging from classic clinical techniques to advanced systems based on sophisticated and expensive equipment, as well as rudimentary manual adjustment mechanisms for the prosthesis by specialists. However, unilateral amputee patients often face difficulties in accessing these solutions. They either lack the necessary equipment or do not have a medical specialist available to assist them in preventing postural imbalances. This study proposes the first smartphone-based computer vision system that evaluates and automatically compensates for leg length discrepancy in transtibial prostheses, offering a low-cost, accessible, and fully autonomous alternative to existing solutions. The method was tested using complex metrological systems. The application of the proposed method demonstrated its effectiveness in correcting simulated LLD for various values. Experimental validation demonstrated the system’s ability to restore symmetry in simulated LLD cases within the 1–10 mm range, achieving a relative compensation error of 2.44%. The proposed method for correcting LLD, based on computer vision and integrated into a smartphone, represents a significant advancement in restoring symmetry for unilaterally amputated patients. This technology could provide an accessible, efficient solution, thereby reducing the need for frequent prosthetist visits and enhancing user autonomy. Full article

Purpose: Despite increasing rates of prostate cancer among men, prostate cancer risk assessments continue to rely on invasive laboratory tests like prostate-specific antigen and Gleason score tests. This study aimed to develop a noninvasive, data-driven risk model for patients to evaluate themselves before deciding whether to visit a hospital. Materials and Methods: To train the model, data from the National Health Insurance Sharing Service cohort datasets, comprising 347,575 individuals, including 1928 with malignant neoplasms of the prostate, 5 with malignant neoplasms of the penis, 18 with malignant neoplasms of the testis, and 14 with malignant neoplasms of the epididymis, were used. The risk model harnessed easily accessible inputs, such as history of treatment for diseases including stroke, heart disease, and cancer; height; weight; exercise days per week; and duration of smoking. An additional 286,727 public datasets were obtained from the National Health Insurance Sharing Service, which included 434 (0.15%) prostate cancer incidences. Results: The risk calculator was built based on Cox proportional hazards regression, and I validated the model by calibration using predictions and observations. The concordance index was 0.573. Additional calibration of the risk calculator was performed to ensure confidence in accuracy verification. Ultimately, the actual proof showed a sensitivity of 60 (60.5) for identifying a high-risk population. Conclusions: The feasibility of the model to evaluate prostate cancer risk without invasive tests was demonstrated using a public dataset. As a tool for individuals to use before hospital visits, this model could improve public health and reduce social expenses for medical treatment. Full article

Background: Clinical imaging is an important part of health care providing physicians with great assistance in patients treatment. In fact, segmentation and grading of tumors can help doctors assess the severity of the cancer at an early stage and increase the chances of cure. Despite that Deep Learning for cancer diagnosis has achieved clinically acceptable accuracy, there still remains challenging tasks, especially in the context of insufficient labeled data and the subsequent need for expensive computational ressources. Objective: This paper presents a lightweight classification and segmentation deep learning model to assist in the identification of cancerous tumors with high accuracy despite the scarcity of medical data. Methods: We propose a multi-task architecture for classification and segmentation of cancerous tumors in the Brain, Skin, Prostate and lungs. The model is based on the UNet architecture with different pre-trained deep learning models (VGG 16 and MobileNetv2) as a backbone. The multi-task model is validated on relatively small datasets (slightly exceed 1200 images) that are diverse in terms of modalities (IRM, X-Ray, Dermoscopic and Digital Histopathology), number of classes, shapes, and sizes of cancer pathologies using the accuracy and dice coefficient as statistical metrics. Results: Experiments show that the multi-task approach improve the learning efficiency and the prediction accuracy for the segmentation and classification tasks, compared to training the individual models separately. The multi-task architecture reached a classification accuracy of 86%, 90%, 88%, and 87% respectively for Skin Lesion, Brain Tumor, Prostate Cancer and Pneumothorax. For the segmentation tasks we were able to achieve high precisions respectively 95%, 98% for the Skin Lesion and Brain Tumor segmentation and a 99% precise segmentation for both Prostate cancer and Pneumothorax. Proving that the multi-task solution is more efficient than single-task networks. Full article

Explainable Artificial Intelligence (XAI) is increasingly important in computer vision, aiming to connect complex model outputs with human understanding. This review provides a focused comparative analysis of representative XAI methods in four main categories, attribution-based, activation-based, perturbation-based, and transformer-based approaches, selected from a broader literature landscape. Attribution-based methods like Grad-CAM highlight key input regions using gradients and feature activation. Activation-based methods analyze the responses of internal neurons or feature maps to identify which parts of the input activate specific layers or units, helping to reveal hierarchical feature representations. Perturbation-based techniques, such as RISE, assess feature importance through input modifications without accessing internal model details. Transformer-based methods, which use self-attention, offer global interpretability by tracing information flow across layers. We evaluate these methods using metrics such as faithfulness, localization accuracy, efficiency, and overlap with medical annotations. We also propose a hierarchical taxonomy to classify these methods, reflecting the diversity of XAI techniques. Results show that RISE has the highest faithfulness but is computationally expensive, limiting its use in real-time scenarios. Transformer-based methods perform well in medical imaging, with high IoU scores, though interpreting attention maps requires care. These findings emphasize the need for context-aware evaluation and hybrid XAI methods balancing interpretability and efficiency. The review ends by discussing ethical and practical challenges, stressing the need for standard benchmarks and domain-specific tuning. Full article

Background: Delirium frequently manifests in hospitalized geriatric patients and is associated with negative health outcomes. Available large-scale data regarding its prevalence rate and impact on older Thai patients are limited. This study aimed to analyze trends in the prevalence rate, its consequences, and the factors contributing to death at discharge among this population. Methods: A retrospective study of inpatients over the age of 60 who received a diagnosis of delirium was conducted, utilizing inpatient medical expense documentation for the fiscal years 2019–2023. The identification of delirium was conducted by the National Health Security Office using the International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Thai Modification (ICD-10-TM) code F05. Results: The 5-year prevalence rate and mortality rate of delirium were 215.1 and 18.7/100,000 population, respectively, and tended to rise over the studied periods. The average hospitalization was 10 days, and the average healthcare expenditure was about 1470 USD/visit. Respiratory disease emerged as the most common primary diagnosis in delirious patients (23.5%). Factors associated with mortality were individuals aged >80 years when juxtaposed with the cohort aged 61–70 years (adjusted odds ratio [AOD] 1.07), being female (AOR 1.13), and a primary diagnosis of respiratory disease (AOR 2.72), cardiovascular disease (AOR 1.68), musculoskeletal disease (AOR 0.61), systemic infection/septicemia (AOR 2.08); or malignancy (AOR 2.97). Conclusions: There was an upward trend in rates of both prevalence and mortality associated with delirium among hospitalized geriatric patients. Advancing age, gender, and particular primary diagnoses were associated with mortality at hospital discharge. Full article

Background/Objectives: The economic impact of adverse events following COVID-19 immunisation (AEFIs) in Australia is underexplored. This study aimed to assess the economic burden of AEFIs on both healthcare systems and societal productivity. Methods: A cross-sectional survey was conducted in Queensland residents aged ≥18 years who had received at least one dose of a COVID-19 vaccine in the preceding 12 months. Overall, 6964 participants were recruited from July to September 2022 via email and broad social media campaigns. The survey collected data on the incidence, type and duration of AEFIs; healthcare utilisation; and work-related absenteeism. Healthcare costs were estimated using national healthcare reimbursement data, and productivity costs were estimated using Australian Bureau of Statistics Average Weekly Earnings. Results: Of the 6797 eligible respondents (predominantly female [62%]; median age: 52 years), AEFIs were reported by 53.4%, 44.1%, 40.7%, and 40.9% following doses 1 to 4, respectively. Pain and tenderness were predominant local AEFIs, while tiredness and headaches were the most frequent systemic AEFIs, generally resolving within three days. Relatively few participants reporting AEFIs consulted medical professionals: 7.0%, 7.3%, 5.1%, and 1.9% following each dose, respectively. The mean healthcare cost per person reporting AEFIs was AUD 24, AUD 88, AUD 22, and AUD 4 following each respective dose. Work absenteeism was recorded in 16.5%, 18.2%, 15.2%, and 11.2% following each dose with mean absenteeism days per person of 4.7, 7.4, 3.6 and 2.1, respectively, and mean productivity costs per person reporting AEFIs amounting to AUD 1494, AUD 2388, AUD 1136, and AUD 690, respectively. Conclusions: Participants reported mostly mild AEFIs with only a small proportion of individuals seeking medical services. Productivity costs attributable to these AEFIs exceeded direct healthcare expenses incurred. Full article

The increasing demand for accurate and accessible medical imaging has driven efforts to develop technologies that overcome limitations associated with conventional imaging techniques, such as MRI and CT scans. This study presents the design and implementation of an electronic interface for acquiring signals from a piezoelectric ultrasound sensor with the aim of improving image reconstruction quality by addressing electromagnetic interference and speckle noise, two major factors that degrade image fidelity. The proposed interface is installed between the ultrasound transducer and acquisition system, allowing real-time signal capture without altering the medical equipment’s operation. Using a printed circuit board with 110-pin connectors, signals from individual piezoelectric elements were analyzed using an oscilloscope. Results show that noise amplitudes occasionally exceed those of the acoustic echoes, potentially compromising image quality. By enabling direct observation of these signals, the interface facilitates the future development of analog filtering solutions to mitigate high-frequency noise before digital processing. This approach reduces reliance on computationally expensive digital filtering, offering a low-cost, real-time alternative. The findings underscore the potential of the interface to enhance diagnostic accuracy and support further innovation in medical imaging technologies. Full article

The current paper examines the sustainable possibility for recycling unused or expired Metoprolol (MET), a benzodiazepine derivative, as an effective corrosion inhibitor for carbon steel in saline solutions. Repurposing expired medicinal drugs aligns with green chemistry concepts and supports circular economy initiatives by reducing pharmaceutical waste and averting the production of new synthetic inhibitors. The technical benefit of recycling expired MET drugs pertains to the elimination of costs associated with organic inhibitor manufacturing and the decrease in disposal expenses for the expired medication. A combination of electrochemical techniques (potentiodynamic polarization and electrochemical impedance spectroscopy) and quantum chemical calculations was employed to evaluate the inhibitory mechanism and efficacy of MET. At a concentration of 10⁻³ M, MET reduced the corrosion current density from 19.38 to 5.97 μA cm⁻², achieving a maximum IE of 69.1%. Adsorption Gibbs free energy, determined using different adsorption isotherms, revealed that interactions between metal atoms and MET adsorbed molecules have a chemical character with a ∆G^o_ads value of −50.7 kJ·mol⁻¹. Furthermore, quantum chemistry calculations indicate that the investigated drug, owing to its molecular structure (E_HOMO = −9.12 eV, E_LUMO = 0.21 eV, µ = 3.95 D), possesses the capacity to establish an adsorption layer on the metal surface, thereby impeding the diffusion of molecules and ions involved in the overall corrosion process. The results obtained using the different techniques were in good agreement and highlighted the effectiveness of MET in the corrosion inhibition of carbon steel. Full article

Background/Objectives: This study aimed to develop a reliable scoring system combining clinical and radiological parameters to distinguish atypical lipomatous tumours (ALTs) from lipomas, improving diagnostic accuracy and reducing expensive molecular pathology testing. Methods: A retrospective analysis of 188 patients who underwent surgery for lipomatous tumours was conducted. Patient data, including medical history, pathology, and MRI imaging results, were reviewed. Four predictive models were developed using various clinical and imaging parameters, including age, tumour size, location, and MRI characteristics (homogeneity, contrast enhancement). Statistical analysis, including ROC curve analysis and logistic regression, was performed to assess the accuracy of these models. Results: The highest predictive accuracy was achieved with Model 1, which included seven parameters, yielding an AUC of 0.952. This model achieved a sensitivity of 96.4% and a negative predictive value (NPV) of 97.2%. Reducing the number of parameters lowered the accuracy, with contrast enhancement playing a significant role in Model 1. A risk calculator based on the optimal model was developed, offering an effective tool for clinical use that can be provided. Notably, 21 out of 37 ALTs lacked atypia and would have been missed without molecular testing. Conclusions: The developed scoring system, based on clinical and imaging parameters, accurately distinguishes ALTs from lipomas, offering a practical alternative to molecular pathology testing. This multi-parameter approach significantly improves diagnostic reliability, reducing the risk of misclassification and false negatives, while also potentially lowering healthcare costs. Full article

Expensive medical devices, especially in the areas of orthopedics, and cardiology, have a significant impact on hospital costs and the delivery of high-quality services. These medical supplies are known as physician preference items (PPIs), as they act as “surrogate buyers”—impacting the selection and sourcing of products. There is a gap between the purchasing strategy and the adoption of tactical activities for these complex medical supplies. In the context of the healthcare exceptionalism thesis, this research investigates how healthcare organizations can successfully adopt suitable sourcing levers aiming to achieve different purchasing results. This research conducts a multi-case study in 15 healthcare organizations in nine countries. Three new sourcing levers specific to the healthcare sector emerged, based on the healthcare exceptionalism thesis. It was possible to identify five main sourcing levers clusters. The fit between strategy and tactical level can be allowed by the implementation of suitable sourcing levers—facilitating the achievement of the desired objectives. Healthcare procurement practitioners should assess the fit between strategy and the tactical level by employing suitable sourcing levers. Organizations wishing to move towards a value-based procurement approach should adopt a set of supporting sourcing levers to enable this transition. Full article

Background/Objectives: Migraine is a common neurological disease with a high disability burden. Despite this, many people with migraine do not take medication to prevent attacks, even when this is indicated. Many non-pharmacological approaches to migraine treatment exist, but little is known about how people with migraine use these options. Methods: The online Migraine in Aotearoa New Zealand (NZ) Survey collected responses from 530 people with migraine from August to October 2022. Questions included current and previous use of preventive medications, supplements (e.g., magnesium, riboflavin, coenzyme Q10) and complementary therapies (e.g., meditation, biofeedback, yoga, acupuncture). Results: Around half of the respondents were currently using a supplement, 58% were currently using complementary therapy and half were taking preventive medication. One in five were using all three approaches for migraine prevention. Of those not taking preventive medication, 44% were taking a supplement, and 53% were using complementary therapy. For commonly used non-pharmacological approaches, 20–30% of people had tried them in the past but stopped due to lack of efficacy or other reasons. A high proportion of people would like to try approaches that are not readily available or expensive in NZ (e.g., neurostimulation devices and biofeedback). Conclusions: The use of supplements and complementary therapies for migraine prevention is common, often in combination with medication. There are high levels of discontinuation due to ineffectiveness and cost prohibits use for many. Improved access to non-pharmacological therapies, particularly as an adjunct, has the potential to improve outcomes for people with migraine, but more and better migraine treatment options are also needed. Full article