learning organization or learning organizations or learning healthcare

Cancer-related deadly diseases affect both developed and underdeveloped nations worldwide. Effective network learning is crucial to more reliably identify and categorize breast carcinoma in vast and unbalanced image datasets. The absence of early cancer symptoms makes the early identification process challenging. Therefore, from the perspectives of diagnosis, prevention, and therapy, cancer continues to be among the healthcare concerns that numerous researchers work to advance. It is highly essential to design an innovative breast cancer detection model by considering the complications presented in the classical techniques...

INTRODUCTION: Strengthening primary care services with a focus on comprehensive Primary Health Care principles necessitates collaborative work practices within interprofessional teams. In Italy, the Local Health District of Florence embodies a comprehensive Primary Health Care -inspired model of primary care, prominently featuring the House of Community concept. This work presents findings and insights from a multidisciplinary, interprofessional education activity tailored for healthcare professionals, researchers, and students actively participating in the primary care reorganization...

Project Extension for Community Healthcare Outcomes (ECHO) enables healthcare providers to share knowledge and best practices via telementoring. The ECHO model builds provider capacity and improves care for patients with a variety of health conditions. This study describes a Canada-wide National ECHO pilot project in the area of geriatric mental health and reports on the program's impact on providers' care practices. A mixed-methods approach was used to analyze surveys completed by participating healthcare providers...

The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health...

INTRODUCTION: Machine learning (ML) techniques have gained increasing attention in the field of healthcare, including predicting outcomes in patients with lung cancer. ML has the potential to enhance prognostication in lung cancer patients and improve clinical decision-making. In this systematic review and meta-analysis, we aimed to evaluate the performance of ML models compared to logistic regression (LR) models in predicting overall survival in patients with lung cancer. METHODS: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement...

The rapid evolution of emerging technologies in healthcare is reshaping the field of medical practices and patient outcomes, ushering in an era of unprecedented innovation. This narrative review touches upon the transformative impacts of various technologies, including virtual reality (VR), augmented reality (AR), the internet of medical things (IoMT), remote patient monitoring (RPM), financial technology (fintech) integration, cloud migration, and the pivotal role of machine learning (ML). It emphasizes the collaborative impact of these technologies, which is reshaping the healthcare landscape...

Artificial intelligence (AI) has come to play a pivotal role in revolutionizing medical practices, particularly in the field of pancreatic cancer detection and management. As a leading cause of cancer-related deaths, pancreatic cancer warrants innovative approaches due to its typically advanced stage at diagnosis and dismal survival rates. Present detection methods, constrained by limitations in accuracy and efficiency, underscore the necessity for novel solutions. AI-driven methodologies present promising avenues for enhancing early detection and prognosis forecasting...

Enhanced recovery after surgery (ERAS) protocols have transformed perioperative care by implementing evidence-based strategies to hasten patient recovery, decrease complications, and shorten hospital stays. However, challenges such as inconsistent adherence and the need for personalized adjustments persist, prompting exploration into innovative solutions. The emergence of artificial intelligence (AI) and machine learning (ML) offers a promising avenue for optimizing ERAS protocols. While ERAS emphasizes preoperative optimization, minimally invasive surgery (MIS), and standardized postoperative care, challenges such as adherence variability and resource constraints impede its effectiveness...

INTRODUCTION: Cancer multi-disciplinary team (MDT) meetings are an important component of consultant workload, however previous literature has suggested trainees are not satisfied with their current curriculum in preparing for MDT working. METHODS: This educational pilot assessed whether multi-speciality simulated scenarios with pre-defined learning objectives, could prepare specialist registrars for interacting within an MDT. Participants completed pre- and post-questionnaires assessing a number of areas including: current experience of training, confidence presenting patients and whether the course would alter future practice...

Venous thromboembolism is the third most common cause of cardiovascular death globally and many diagnoses are preventable. The UK NHS has led international efforts to reduce VTE, particularly hospital-associated VTE, through coordinated national policy action and world-leading research. Despite this, VTE remains an important cause of morbidity and mortality in the UK, as underlined by the recent COVID-19 pandemic. Future reductions in VTE incidence/deaths will require progress on several fronts: a better understanding of case mix; revisiting VTE risk assessment, focussing on thromboprophylaxis failure and improving awareness of VTE amongst clinicians and the public...

BACKGROUND: Interpretation of ultrasound findings of thyroid nodules is subjective and labor-intensive for radiologists. Artificial intelligence (AI) is a relatively objective and efficient technology. We aimed to establish a fully automatic detection and diagnosis system for thyroid nodules based on AI technology by analyzing ultrasound video sequences. PATIENTS AND METHODS: We prospectively acquired dynamic ultrasound videos of 1067 thyroid nodules (804 for training and 263 for validation) from December 2018 to January 2021...

BACKGROUND: Although there are guidelines and ideas on how to improve public health education, translating innovative approaches into actual training programs remains challenging. In this article, we provide an overview of some initiatives that tried to put this into action in different parts of the world, and present the Emerging Health Care Leader (EHCL), a novel training program developed in Switzerland. POLICY OPTIONS AND RECOMMENDATIONS: Looking at the experience of the EHCL, we propose policymakers and other interested stakeholders who wish to help reform public health education to support these initiatives not only through funding, but by valuing them through the integration of early career healthcare leaders in projects where their developing expertise can be practically applied...

Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health problem. Due to the rapid development of deep learning, it has become a common practice to apply deep learning to the healthcare industry to improve the effectiveness and accuracy of disease identification. In order to improve the effectiveness and accuracy of hepatitis C detection, this study proposes an improved denoising autoencoder (IDAE) and applies it to hepatitis C disease detection...

BACKGROUND: The objective of this research was to create and validate an interpretable prediction model for drug-induced liver injury (DILI) during tuberculosis (TB) treatment. METHODS: A dataset of TB patients from Ningbo City was used to develop models employing the eXtreme Gradient Boosting (XGBoost), random forest (RF), and the least absolute shrinkage and selection operator (LASSO) logistic algorithms. The model's performance was evaluated through various metrics, including the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR) alongside the decision curve...

BACKGROUND: PRiVENT (PRevention of invasive VENTilation) is an evaluation of a bundle of interventions aimed at the prevention of long-term invasive mechanical ventilation. One of these elements is an e-learning course for healthcare professionals to improve weaning expertise. The aim of our analysis is to examine the implementation of the course in cooperating intensive care units. METHODS: The course has been developed through a peer review process by pulmonary and critical care physicians in collaboration with respiratory therapists, supported by health services researchers and a professional e-learning agency...

BACKGROUND: The use of virtual reality (VR) in healthcare education is on the increase. In disaster medicine, it could be a solution to the cost and logistic constraints for a "full-scale" scenarios. However, VR is mainly designed for single players, which is not appropriate for the objectives pursued in disaster medicine. We decided to evaluate the educational value of using individual VR simulation in disaster medicine on a group of learners. METHODS: The VR scenario used was a reproduction of a major train crash, with 21 victims and whose objectives were START triage and first aid techniques...

BACKGROUND: Care of older adults requires comprehensive management and control of systemic diseases, which can be effectively managed by family physicians. Complicated medical conditions in older patients admitted to orthopedic departments (orthopedic patients) necessitate interprofessional collaboration. Nutrition is one of the essential components of management involved in improving the systemic condition of older patients. Nutrition support teams play an important role in nutrition management and can be supported by family physicians...

Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models often struggle to balance high performance with computational efficiency. This study introduces an innovative computational framework that combines transfer learning with traditional machine learning to optimize ECG classification. We use a pre-trained Stacked Convolutional Neural Network (SCNN) to generate high-dimensional feature embeddings, which are then evaluated by an array of machine learning classifiers...

PURPOSE OF REVIEW: Access to evidence-based pharmacotherapy (EBP) is crucial in effectively managing allergies. Allergy conditions, including rhinitis, asthma, and dermatitis, require treatment guided by scientific evidence. However, healthcare professionals face challenges in accessing relevant information. RECENT FINDINGS: The dynamic nature of allergy research, coupled with limited resources and variability in practice guidelines, complicates decision-making...

BACKGROUND: The digitalization in the healthcare sector promises a secondary use of patient data in the sense of a learning healthcare system. For this, the Medical Informatics Initiative's (MII) Consent Working Group has created an ethical and legal basis with standardized consent documents. This paper describes the systematically monitored introduction of these documents at the MII sites. METHODS: The monitoring of the introduction included regular online surveys, an in-depth analysis of the introduction processes at selected sites, and an assessment of the documents in use...