Clinical informatics

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions...

BACKGROUND: Artificial intelligence (AI)-based medical devices have garnered attention due to their ability to revolutionize medicine. Their health technology assessment framework is lacking. OBJECTIVE: This study aims to analyze the suitability of each health technology assessment (HTA) domain for the assessment of AI-based medical devices. METHODS: We conducted a scoping literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology...

Accurate segmentation of the fetal head and pubic symphysis in intrapartum ultrasound images and measurement of fetal angle of progression (AoP) are critical to both outcome prediction and complication prevention in delivery. However, due to poor quality of perinatal ultrasound imaging with blurred target boundaries and the relatively small target of the public symphysis, fully automated and accurate segmentation remains challenging. In this paper, we propse a dual-path boundary-guided residual network (DBRN), which is a novel approach to tackle these challenges...

Hepatocellular carcinoma (HCC), the most common type of liver cancer, poses significant challenges in detection and diagnosis. Medical imaging, especially computed tomography (CT), is pivotal in non-invasively identifying this disease, requiring substantial expertise for interpretation. This research introduces an innovative strategy that integrates two-dimensional (2D) and three-dimensional (3D) deep learning models within a federated learning (FL) framework for precise segmentation of liver and tumor regions in medical images...

The nutritional status of cancer patients is closely associated with the clinical progression of the disease. A survival analysis model combined with a neural network can predict future disease trends in patients, facilitating early prevention and assisting physicians in making diagnoses. However, the complexity of neural networks and their incompatibility with medical tabular data can reduce the interpretability of the model. To address this issue, thr paper propose a novel survival analysis model called Tab-Cox, which combines TabNet and Cox models...

BACKGROUND: A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications...

BACKGROUND: While text messaging has proven effective for smoking cessation (SC), engagement in the intervention remains suboptimal. OBJECTIVE: This study aims to evaluate whether using more interactive and adaptive instant messaging (IM) apps on smartphones, which enable personalization and chatting with SC advisors, can enhance SC outcomes beyond the provision of brief SC advice and active referral (AR) to SC services. METHODS: From December 2018 to November 2019, we proactively recruited 700 adult Chinese daily cigarette users in Hong Kong...

The collaborative project Personalized Medicine for Oncology (PM4Onco) was launched in 2023 as part of the National Decade against Cancer (NKD) and is executed within the Medical Informatics Initiative (MII). Its aim is to establish a sustainable infrastructure for the integration and use of data from clinical and biomedical research and therefore combines the experience and preliminary work of all four consortia of the MII and the leading oncology centers in Germany. The data provided by PM4Onco will be prepared in a suitable form to support decision making in molecular tumor boards...

INTRODUCTION: Home healthcare (HHC) enables patients to receive healthcare services within their homes to manage chronic conditions and recover from illnesses. Recent research has identified disparities in HHC based on race or ethnicity. Social determinants of health (SDOH) describe the external factors influencing a patient's health, such as access to care and social support. Individuals from racially or ethnically minoritized communities are known to be disproportionately affected by SDOH...

Background Pericardial diseases manifest in various clinical forms, including acute pericarditis, constrictive pericarditis, pericardial effusion, and cardiac tamponade, with acute pericarditis being the most prevalent. These conditions significantly contribute to mortality rates. Therefore, this article aimed to analyze mortality trends in the Brazilian population based on age and sex, shedding light on the impact of pericardial diseases on public health outcomes. Methods  This is a retrospective time-series analysis of pericardial disease mortality rates in Brazil (2000-2022)...

BACKGROUND: Gender-affirming hormone therapy with either estradiol or testosterone for transgender persons can significantly impact chemistry and hematology laboratory tests. The sex used for assignment of reference intervals (RIs) in the electronic health record (EHR) will influence normal/abnormal flagging of test results. OBJECTIVE: To analyze common non-hormonal laboratory tests with sex-specific RIs ordered in patients with sexual orientation/gender identify (SOGI) field differences (one or more differences between legal sex, sex assigned at birth, and gender identity) in the EHR at an academic medical center in midwestern United States...

OBJECTIVE: To evaluate Phenotype Execution and Modelling Architecture (PhEMA), to express sharable phenotypes using Clinical Quality Language (CQL) and intensional Systematised Nomenclature of Medicine (SNOMED) Clinical Terms (CT) Fast Healthcare Interoperability Resources (FHIR) valuesets, for exemplar chronic disease, sociodemographic risk factor, and surveillance phenotypes. METHOD: We curated 3 phenotypes: Type 2 diabetes mellitus (T2DM), excessive alcohol use, and incident influenza-like illness (ILI) using CQL to define clinical and administrative logic...

Against the backdrop of rapid social economy and scientific and technological development, intelligent medical technology expanded based on the Internet plays a crucial role in the innovation and development of the modern medical industry. Intelligent medical technology has completely changed the fixed medical methods of the past, and it can solve the isolated defects between various unit systems, greatly improving the overall informatization level of hospitals. This article analyzed the clinical diagnosis, prevention, and treatment of neurodyspepsia syndrome (NDS) in intelligent medicine...

BACKGROUND: The adoption of digital pathology has transformed the field of pathology, however, the economic impact and cost analysis of implementing digital pathology solutions remain a critical consideration for institutions to justify. Digital pathology implementation requires a thorough evaluation of associated costs and should identify and optimize resource allocation to facilitate informed decision-making. A dynamic cost calculator to estimate the financial implications of deploying digital pathology systems was needed to estimate the financial effects on transitioning to a digital workflow...

INTRODUCTION: There are very few scholastic studies applying a theory-driven methodology to analyse the employment of teledentistry in clinical practice by the Saudi dental community. The objective of this research was to predict the employment of teledentistry in clinical practice by the Saudi dental community using the UTAUT (Unified Theory of Acceptance and Use of Technology) model. METHODS: A countrywide survey was executed from November 2022 to April 2023 among the dental community (pre-graduate students, graduates, post-graduate students, general dentists, and specialist dentists) involved in clinical practice...

BACKGROUND: Self-scheduling of medical visits is becoming more common but the complexity of applying multiple requirements for self-scheduling has hampered implementation. Mayo Clinic implemented self-scheduling in 2019 and has been increasing its portfolio of self-schedulable visits since then. Our aim was to show measures quantifying the complexity associated with medical visit scheduling and to describe how opportunities and challenges of scheduling complexity apply in self-scheduling...

BACKGROUND: Due to the rapid advancement in information technology, changes to communication modalities are increasingly implemented in healthcare. One such modality is Computerised Provider Order Entry (CPOE) systems which replace paper, verbal or telephone orders with electronic booking of requests. We aimed to understand the uptake, and user acceptability, of CPOE in a large National Health Service hospital system. METHODS: This retrospective single-centre study investigates the longitudinal uptake of communications through the Prescribing, Information and Communication System (PICS)...

The continued demand for digital health requires that providers adapt thought processes to enable sound clinical decision making in digital settings. Providers report that lack of training is a barrier to providing digital healthcare. Physical exam techniques and hands-on interventions must be adjusted in safe, reliable and feasible ways to digital care and decision making may be impacted by modifications made to these techniques. We have proposed a framework for determining if a procedure can be modified to obtain a comparable result in a digital environment or if a referral to in-person care is required...

BACKGROUND: Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction. OBJECTIVES: This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC)...

BACKGROUND: Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM...