bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning with Clinical and Intraoperative Biosignal Data for Predicting Cardiac Surgery-Associated Acute Kidney Injury
Authors: Han Changho , Soh Sarah , Kim Hyun Il , Song Jong Wook , Yoon Dukyong
Published: August 2024

Early identification of patients at high risk of cardiac surgery-associated acute kidney injury (CSA-AKI) is crucial for its prevention. We aimed to leverage perioperative clinical and intraoperative biosignal data to develop machine learning models that predict CSA-AKI. We introduced a novel approach for extracting relevant features from high-resolution intraoperative biosignals to reflect the patient’s baseline status, the extent of unfavorable conditions encountered intraoperatively, and data variability. We developed XGBoost models from 2,003 patients across three consecutive perioperative phases using: 1) only preoperative, 2) pre- and intraoperative, and 3) pre-, intra-, and postoperative variables. The predictive performance progressively improved throughout the three consecutive perioperative phases (e.g., AUROC of 0.767 to 0.797 and 0.840), all surpassing the Thakar Score’s performance. According to the SHAP method, intraoperative perfusion pressure was most important in the prediction, highlighting the importance of intraoperative patient management and the use of high-resolution biosignal data in predictive modeling to analyze hemodynamic fluctuations during surgery. Early postoperative biomarkers were also important predictors, highlighting the importance of intensified monitoring early after surgery.
Cybersecurity Frameworks in Healthcare Data: Short Literature Review
Authors: Adnan Muhammad , Kutafina Ekaterina , Beyan Oya
Published: August 2024

The importance of cybersecurity in healthcare, with a focus on safeguarding sensitive patient information from unauthorized access, use, or disclosure, cannot be overstated Security breaches in this sector can have significant consequences due to the widespread use of electronic health records (EHRs) and interconnected medical devices, creating opportunities for exploitation. This work presents a first step to analyzing and organizing healthcare-specific cybersecurity problems and existing security frameworks. Special focus is put on the security risks associated with data integration centers while recognizing their role as hubs for innovation.
How Do Cancer Patients Keep Track of Their Patient Journeys?
Authors: Larsen Anna Grøndahl , Halvorsrud Ragnhild , Solem Ingrid Konstanse Ledel , Melby Line
Published: August 2024

Cancer patients undergo long periods of treatment and follow-up, and it is challenging to keep track of appointments, treatment plans etc. This paper report from a study involving 41 patients and next-of-kin focusing on their strategies for managing the patient journey. Most patients take an active role, employing a variety of tools. The national patient portal is seen as useful for accessing information and keeping an overview but does not alone meet their information needs.
Data Integration for a Global Deep-Phenotyping Registry for Pulmonary Hypertension – Lessons Learned
Authors: Fuenderich Meike T. , Krieb Philipp , Seeger Werner , Majeed Raphael W.
Published: August 2024

The integration of data from various healthcare centers into disease registries is pivotal for facilitating collaborative research and enhancing clinical insights. In this study, we investigate the integration process of existing registries into the PVRI GoDeep meta-registry, focusing on the complexities and challenges encountered. We detail the integration process, including data transformation, mapping updates, and feedback mechanisms. Our findings underscore the importance of standardized processes and proactive communication in addressing data quality issues, ultimately enhancing the reliability and trustworthiness of meta-registry data. Through careful harmonization of the data and transparent documentation of data processing, we pave the way for leveraging registry data to drive advancements in pulmonary hypertension research and patient care.
Tracking Usage Patterns of a Mobile Application for Dementia Risk Assessment in Elderly Individuals
Authors: Hilberger Hannes , Bödenler Markus , Hanke Sten
Published: August 2024

Among elderly individuals, dementia stands as a prominent contributor to disability. However, multidomain lifestyle interventions have emerged as a promising approach for enhancing cognition. Taking a progressive step, integrating digital elements such as a mobile intervention application for elderly participants becomes crucial. Hence, it’s crucial to identify the usage patterns of intervention features. Our aim was to implement a usage tracking mechanism and analyze usage trends and feature preferences over the initial six months of a clinical trial across various clinical sites, comparing intervention and control groups. Our findings reveal the differences in usage between the groups and highlight the most used features.
Empowering Citizens Through Translated Patient Summary Access and Sharing in Digital Health Ecosystems
Authors: Solomou Theodoros , Canciu Ionut-Cristian , Christodoulou Marios , Savva Panayiotis , Yiasemi Constantinos , Antoniou Zinonas , Constantinou Ioannis , Schizas Christos N. , Pattichis Constantinos S.
Published: August 2024

This paper introduces a mobile framework designed to enhance citizen access to and sharing of health data, aiming to empower individuals with greater control over their personal health information. Accessing and sharing health-related data is essential in everyday scenarios, from routine doctor visits or viewing your health on your own to emergencies where swift access can save lives. It addresses the challenges posed by the fragmented nature of healthcare services and the barriers of language differences in patient records. The framework utilizes the EU eHealth Digital Service Infrastructure (eHDSI) OpenNCP for translating patient summaries and the FHIR Smart Health Links Protocol for secure sharing. A pilot study with 40 participants was conducted to assess the usability and effectiveness of the app, revealing a strong demand among citizens for such innovative health services.
User-Centered Development of Explanation User Interfaces for AI-Based CDSS: Lessons Learned from Early Phases
Authors: Jung Ian-C. , Zerlik Maria , Schuler Katharina , Sedlmayr Martin , Sedlmayr Brita
Published: August 2024

This paper reports lessons learned during the early phases of the user-centered design process for an explanation user interface for an AI-based clinical decision support system for the intensive care unit. This paper focuses on identifying and verifying physicians’ explanation needs in a multi-center, multi-country project. The explanation needs identified through context analysis and user requirements prioritization in an initial center differed from those identified through questionnaire responses from N= 9 physicians after a multi-center project workshop. These results highlight the caution that should be taken when eliciting explanation needs during the user-centered design process.
Assessing the Reliability of Machine Learning Explanations in ECG Analysis Through Feature Attribution
Authors: Plagwitz Lucas , Bickmann Lucas , Büscher Antonius , Varghese Julian
Published: August 2024

Feature attribution methods stand as a popular approach for explaining the decisions made by convolutional neural networks. Given their nature as local explainability tools, these methods fall short in providing a systematic evaluation of their global meaningfulness. This limitation often gives rise to confirmation bias, where explanations are crafted after the fact. Consequently, we conducted a systematic investigation of feature attribution methods within the realm of electrocardiogram time series, focusing on R-peak, T-wave, and P-wave. Using a simulated dataset with modifications limited to the R-peak and T-wave, we evaluated the performance of various feature attribution techniques across two CNN architectures and explainability frameworks. Extending our analysis to real-world data revealed that, while feature attribution maps effectively highlight significant regions, their clarity is lacking, even under the simulated ideal conditions, resulting in blurry representations.
Artificial Intelligence System for Automated Breast Cancer Detection in Pathology in Burkina Faso: Methodology Overview
Authors: Ouattara Thomas Alassane , Barro Seydou Golo , Staccini Pascal
Published: August 2024

The introduction of artificial intelligence (AI) in breast cancer diagnosis in Burkina Faso represents a significant advancement in the field of healthcare. Faced with the public health issue posed by breast cancer, this study focuses on the use of AI to improve early and accurate detection of this disease from histopathological images. For the implementation of the system, we utilized a customized architecture tailored to our context where image quality is low, based on the convolutional neural networks algorithm from the Keras library of TensorFlow. Subsequently, we developed a platform to facilitate its use. This article aims to present the methodology that was used and the results obtained.
PubMed Retrieval with RAG Techniques
Authors: Thomo Alex
Published: August 2024

This study explores the application of Retriever-Augmented Generation (RAG) in enhancing medical information retrieval from the PubMed database. By integrating RAG with Large Language Models (LLMs), we aim to improve the accuracy and relevance of medical information provided to healthcare professionals. Our evaluation on a labeled dataset of 1,000 queries demonstrates promising results in answer relevance, while highlighting areas for improvement in groundedness and context relevance.
Monitoring of Artificial Intelligence in Hospitals
Authors: Ranjbar Arian , Ravn Jesper
Published: August 2024

Monitoring of artificial intelligence (AI)-based algorithms is necessary for safe implementation and will be required in upcoming regulations. This study investigates the potential for monitoring of AI in hospitals. First, by reviewing regulatory requirements and state of the art of monitoring. Then, by conducting a gap analysis of ISO42001, containing industry agnostic requirements harmonized with the EU AI Act. The analysis illustrates the need for comprehensive monitoring capable of capturing deviations in input, performance drifts and unintended interactions. However, hospitals often suffer from a technical debt, and the gap analysis provides qualitative indications on implementation challenges, including data quality, infrastructure and limitations in continuous improvement.
Machine Learning to Predict the Risk of Malnutrition in Hospitalized Patients with Pneumonia and Analysis of Related Prognostic Factor
Authors: Liu Mei-Yuan , Sung May-I , Liu Chung-Feng
Published: August 2024

This study explored machine learning’s potential in predicting the nutritional status and outcomes for pneumonia patients. It focused on 4,368 patients in a Taiwan medical center from Jan 2016 to Feb 2022, excluding ICU cases. The average age was 77.6 years, with 10.2% well-nourished, 76.3% at-risk, and 13.5% malnourished. Machine learning models, particularly LightGBM and XGBoost, showed high accuracy in predicting hospital stays, mortality rates, and readmissions. These findings emphasize the role of data-driven methods in enhancing patient care and managing conditions like pneumonia more effectively.
A Governance Framework for the Implementation and Operation of AI Applications in a University Hospital
Authors: Apfelbacher Timo , Koçman Sude Eda , Prokosch Hans-Ulrich , Christoph Jan
Published: August 2024

Background: Artificial intelligence (AI) is becoming increasingly important in everyday life and medical care with a notable gap between AI development in medicine there and its practical implementation in university hospitals. Objective: The aim was to develop a governance framework to guide the procurement and implementation of AI applications in university hospitals. Methods: Seven role-play expert interviews were conducted with interviewees from two university hospitals, simulating realistic AI system implementation scenarios. Results: A detailed governance framework was developed, divided into the sections general considerations, system selection criteria, and implementation. Further, a condensed version of the checklist was also derived. Conclusion: Many aspects of AI applications in medical care, such as the establishment of an AI board, remain, along with numerous concerns about the technology. These findings provide valuable insights into the topic.
Applying Large Language Models to Interpret Qualitative Interviews in Healthcare
Authors: Wosny Marie , Hastings Janna
Published: August 2024

To address the persistent challenges in healthcare, it is crucial to incorporate firsthand experiences and perspectives from stakeholders such as patients and healthcare professionals. However, the current process of collecting, analyzing and interpreting qualitative data, such as interviews, is slow and labor-intensive. To expedite this process and enhance efficiency, automated approaches aim to extract meaningful themes and accelerate interpretation, but current approaches such as topic modeling reduce the richness of the raw data. Here, we evaluate whether Large Language Models can be used to support the semi-automated interpretation of qualitative interview data. We compare a novel approach based on LLMs to topic modeling approaches and to manually identified themes across two different qualitative interview datasets. This exploratory study finds that LLMs have the potential to support incorporating human perspectives more widely in the advancement of sustainable healthcare systems.
Causal Deep Learning for the Detection of Adverse Drug Reactions: Drug-Induced Acute Kidney Injury as a Case Study
Authors: Dimitsaki Stella , Natsiavas Pantelis , Jaulent Marie-Christine
Published: August 2024

Causal Deep/Machine Learning (CDL/CML) is an emerging Artificial Intelligence (AI) paradigm. The combination of causal inference and AI could mine explainable causal relationships between data features, providing useful insights for various applications, e.g. Pharmacovigilance (PV) signal detection upon Real-World Data. The objective of this study is to demonstrate the use of CDL for potential PV signal validation using Electronic Health Records as input data source.
An Overview of Explainable AI Studies in the Prediction of Sepsis Onset and Sepsis Mortality
Authors: Nicolaou Andria , Stylianides Charithea , Sulaiman Waqar A. , Antoniou Zinonas , Palazis Lakis , Vavlitou Anna , Pattichis Constantinos S. , Panayides Andreas S.
Published: August 2024

Explainable artificial intelligence (AI) focuses on developing models and algorithms that provide transparent and interpretable insights into decision-making processes. By elucidating the reasoning behind AI-driven diagnoses and treatment recommendations, explainability can gain the trust of healthcare experts and assist them in difficult diagnostic tasks. Sepsis is characterized as a serious condition that happens when the immune system of the body has an extreme response to an infection, causing tissue and organ damage and leading to death. Physicians face challenges in diagnosing and treating sepsis due to its complex pathogenesis. This work aims to provide an overview of the recent studies that propose explainable AI models in the prediction of sepsis onset and sepsis mortality using intensive care data. The general findings showed that explainable AI can provide the most significant features guiding the decision-making process of the model. Future research will investigate explainability through argumentation theory using intensive care data focused on sepsis patients.
Classifiers of Data Sharing Statements in Clinical Trial Records
Authors: Jelodari Mamaghani Saber , Strantz Cosima , Toddenroth Dennis
Published: August 2024

Digital individual participant data (IPD) from clinical trials are increasingly distributed for potential scientific reuse. The identification of available IPD, however, requires interpretations of textual data-sharing statements (DSS) in large databases. Recent advancements in computational linguistics include pre-trained language models that promise to simplify the implementation of effective classifiers based on textual inputs. In a subset of 5,000 textual DSS from ClinicalTrials.gov, we evaluate how well classifiers based on domain-specific pre-trained language models reproduce original availability categories as well as manually annotated labels. Typical metrics indicate that classifiers that predicted manual annotations outperformed those that learned to output the original availability categories. This suggests that the textual DSS descriptions contain applicable information that the availability categories do not, and that such classifiers could thus aid the automatic identification of available IPD in large trial databases.
Measured Performance and Healthcare Professional Perception of Large Language Models Used as Clinical Decision Support Systems: A Scoping Review
Authors: Delourme Solène , Redjdal Akram , Bouaud Jacques , Seroussi Brigitte
Published: August 2024

The healthcare sector confronts challenges from overloaded tumor board meetings, reduced discussion durations, and care quality concerns, necessitating innovative solutions. Integrating Clinical Decision Support Systems (CDSSs) has a potential in supporting clinicians to reduce the cancer burden, but CDSSs remain poorly used in clinical practice. The emergence of OpenAI’s ChatGPT in 2022 has prompted the evaluation of Large Language Models (LLMs) as potential CDSSs for diagnosis and therapeutic management. We conducted a scoping review to evaluate the utility of LLMs like ChatGPT as CDSSs in several medical specialties, particularly in oncology, and compared users’ perception of LLMs with the actually measured performance of these systems.
Exploring Explainable AI Techniques for Text Classification in Healthcare: A Scoping Review
Authors: Madi Ibrahim Alaa Eddine , Redjdal Akram , Bouaud Jacques , Seroussi Brigitte
Published: August 2024

Text classification plays an essential role in the medical domain by organizing and categorizing vast amounts of textual data through machine learning (ML) and deep learning (DL). The adoption of Artificial Intelligence (AI) technologies in healthcare has raised concerns about the interpretability of AI models, often perceived as “black boxes.” Explainable AI (XAI) techniques aim to mitigate this issue by elucidating AI model decision-making process. In this paper, we present a scoping review exploring the application of different XAI techniques in medical text classification, identifying two main types: model-specific and model-agnostic methods. Despite some positive feedback from developers, formal evaluations with medical end users of these techniques remain limited. The review highlights the necessity for further research in XAI to enhance trust and transparency in AI-driven decision-making processes in healthcare.
A Novel Method and Python Library for ECG Signal Quality Assessment
Authors: Berger Charles , Turbé Hugues , Bjelogrlic Mina , Lovis Christian
Published: August 2024

Electrocardiogram (ECG) is one of the reference cardiovascular diagnostic exams. However, the ECG signal is very prone to being distorted through different sources of artifacts that can later interfere with the diagnostic. For this reason, signal quality assessment (SQA) methods that identify corrupted signals are critical to improve the robustness of automatic ECG diagnostic methods. This work presents a review and open-source implementation of different available indices for SQA as well as introducing an index that considers the ECG as a dynamical system. These indices are then used to develop machine learning models which evaluate the quality of the signals. The proposed index along the designed ML models are shown to improve SQA for ECG signals.
Design of a Digital Twin of the Heart for the Management of Heart Failure Patients
Authors: Scotto Andrea , Giordano Noemi , Rosati Samanta , Balestra Gabriella
Published: August 2024

Heart failure poses a significant global health burden with high prevalence and mortality rates. A promising possibility in this context is the constant monitoring of the patients through telemedicine. The aim of this work is to present a digital twin of a patient at risk of heart failure. Applying machine learning to the recorded data of the patient, the system is able to early detect potential issues and improve the outcome.
Generative AI Analysis of Topics from IMIA Historical Narratives in Medical Informatics
Authors: Kulikowski Casimir , Yarava ASHRIT
Published: August 2024

This describes a project that analyzes and compares the research topics in medical and health informatics emphasized by authors of autobiographical narratives contained in the online IMIA History eBook published in 2021, as the field advanced over the past half century in its international and interdisciplinary dimensions from its early days until the present.
Exploring Offline Large Language Models for Clinical Information Extraction: A Study of Renal Histopathological Reports of Lupus Nephritis Patients
Authors: Tai Isaac C.Y. , Wong Emmanual C.K. , Wu Joseph T. , Leung Kathy , Yap Desmond Y.H. , Wong Zoie S.Y.
Published: August 2024

Open source, lightweight and offline generative large language models (LLMs) hold promise for clinical information extraction due to their suitability to operate in secured environments using commodity hardware without token cost. By creating a simple lupus nephritis (LN) renal histopathology annotation schema and generating gold standard data, this study investigates prompt-based strategies using three state-of-the-art lightweight LLMs, namely BioMistral-DARE-7B (BioMistral), Llama-2-13B (Llama 2), and Mistral-7B-instruct-v0.2 (Mistral). We examine the performance of these LLMs within a zero-shot learning environment for renal histopathology report information extraction. Incorporating four prompting strategies, including combinations of batch prompt (BP), single task prompt (SP), chain of thought (CoT) and standard simple prompt (SSP), our findings indicate that both Mistral and BioMistral consistently demonstrated higher performance compared to Llama 2. Mistral recorded the highest performance, achieving an F1-score of 0.996 [95% CI: 0.993, 0.999] for extracting the numbers of various subtypes of glomeruli across all BP settings and 0.898 [95% CI: 0.871, 0.921] in extracting relational values of immune markers under the BP+SSP setting. This study underscores the capability of offline LLMs to provide accurate and secure clinical information extraction, which can serve as a promising alternative to their heavy-weight online counterparts.
Speech Emotion Recognition Using openSMILE and GPT 3.5 Transformer
Authors: Turcian Darius , Stoicu-Tivadar Vasile
Published: August 2024

In recent years, artificial intelligence, and machine learning (ML) models have advanced significantly, offering transformative solutions across diverse sectors. Emotion recognition in speech has particularly benefited from ML techniques, revolutionizing its accuracy and applicability. This article proposes a method for emotion detection in Romanian speech analysis by combining two distinct approaches: semantic analysis using GPT Transformer and acoustic analysis using openSMILE. The results showed an accuracy of 74% and a precision of almost 82%. Several system limitations were observed due to the limited and low-quality dataset. However, it also opened a new horizon in our research by analyzing emotions to identify mental health disorders.
Towards a Chatbot for Medical Diagnosis Based on Patient Symptoms
Authors: Kabore Kisito , Sawadogo Omar , Traore Yaya , Thiombiano Julie
Published: August 2024

The advent of artificial intelligence has positively transformed many areas of our lives, including the medical field. In this article, we propose the development of a medical diagnosis chatbot based on patients’ symptoms, using artificial intelligence as an innovative solution. The aim of this tool is to provide doctors with a preliminary diagnosis based on the symptoms presented by patients. Our proposal involves first building models to be used for predicting diseases, and then using a llama-inspired model to generate the medical report and prescription. The chatbot system was implemented using Python, HTML, CSS and JavaScript, with FastAPI for the RESTful API and PostgreSQL for data management. The results highlight the potential of AI to improve healthcare services, especially in areas where resources are limited.
Explainable AI Modeling in the Prediction of Cardiovascular Disease Risk
Authors: Skouteli Chara , Prenzas Nicoletta , Kakas Antonis , Pattichis Constantinos S.
Published: August 2024

The objective of this study was to develop explainable AI modeling in the prediction of cardiovascular disease. The XGBoost algorithm was used followed by rule extraction and argumentation theory that provides interpretability, explainability and accuracy in scenarios with low confidence results or dilemmas. Our findings are in agreement with previous research utilizing the XGBoost machine learning algorithm for prediction of cardiovascular risk, however it is supported by rule based explainability, offering significant advantages in terms of providing both global and local explainability. Further work is needed to enhance the argumentation-based rule interpretability, explainability and accuracy in scenarios with low confidence results or dilemmas.
Using Retrieval-Augmented Generation to Capture Molecularly-Driven Treatment Relationships for Precision Oncology
Authors: Kreimeyer Kory , Canzoniero Jenna V. , Fatteh Maria , Anagnostou Valsamo , Botsis Taxiarchis
Published: August 2024

Modern generative artificial intelligence techniques like retrieval-augmented generation (RAG) may be applied in support of precision oncology treatment discussions. Experts routinely review published literature for evidence and recommendations of treatments in a labor-intensive process. A RAG pipeline may help reduce this effort by providing chunks of text from these publications to an off-the-shelf large language model (LLM), allowing it to answer related questions without any fine-tuning. This potential application is demonstrated by retrieving treatment relationships from a trusted data source (OncoKB) and reproducing over 80% of them by asking simple questions to an untrained Llama 2 model with access to relevant abstracts.
Robust In-Vehicle Signal Quality Assessment Using Multimodal Signal Fusion
Authors: Senthilkumar Laushya , Warnecke Joana M. , Bollmann Julian , Deserno Thomas M.
Published: August 2024

Continuous monitoring of physiological signals such as electrocardiogram (ECG) in driving environments has the potential to reduce the need for frequent health check-ups by providing real-time information on cardiovascular health. However, capturing ECG from sensors mounted on steering wheels creates difficulties due to motion artifacts, noise, and dropouts. To address this, we propose a novel method for reliable and accurate detection of heartbeats using sensor fusion with a bidirectional long short-term memory (BiLSTM) model. Our dataset contains reference ECG, steering wheel ECG, photoplethysmogram (PPG), and imaging PPG (iPPG) signals, which are more feasible to capture in driving scenarios. We combine these signals for R-wave detection. We conduct experiments with individual signals and signal fusion techniques to evaluate the performance of detected heartbeat positions. The BiLSTMs model achieves a performance of 62.69% in the driving scenario city. The model can be integrated into the system to detect heartbeat positions for further analysis.
Nurses’ Experience of Using an Electronic Medical Records – OpenMRS Module for the Management of Hypertension and Diabetes in Rwanda: A Qualitative Study
Authors: Ntakirutimana Isaac , Kayiranga Dieudonne , Muhirwa Augustine , Nkurunziza Emmanuel , Sibomana Emmanuel , Uwamahoro Angelique , Igihozo Theophila , Ndoli Amelie , Niyonshuti Emmanuel , Rwubaka Eugene , Tuyishime Isdore , Umutoniwase Emma Marie , Mugisha Michael
Published: August 2024

The management of NCDs require a secure and continuous collection and use of patient data to improve care and treatment. The OpenMRS NCDs module was developed and piloted in three districts to ascertain the possibility of using it in early detection and management of Hypertension and Diabetes in Rwanda. This paper explored the user experience NCDs module of OpenMRS, an open sources EMR used in health centers of Rwanda. We used two methods to explore the user experience of the system among the nurses: Key informant interviews and observations. We analysed the data using thematic content analysis and drawn upon the views and expectations of the users to experience effective use of the system. We collected the data using the developed and piloted tools. In this study we interviewed 10 nurses and observed how they complete tasks in the system. In general, the nurses found that the system was useful because it simplified patient care and reporting. Some barriers related to the use of the system were slowness of the system, and turnover of trained users. We concluded that during the first 12 months of the pilot of the OpenMRS in 5 hospitals located in three districts, nurses were able to use the system with limited and manageable challenges. However, some challenges related to system design and navigations should be addressed before wider implementation.
Use of E-Consent in Healthcare Settings: A Scoping Review
Authors: Obaidi Haris , Elkhyatt Youssef , Alzubaidi Mahmood , Househ Mowafa
Published: August 2024

Electronic consent is a technology-driven approach that remains challenging in various healthcare settings. Transitioning from paper-based to electronic consent (e-consent) has streamlined the consent process. This scoping review explores patients’ electronic consent in different healthcare settings. We searched four databases and selected 14 studies that met our inclusion criteria. Our results show that E-consent is associated with key measures such as sufficient information, accuracy, enhanced shared decision-making, and efficiency. The majority of studies used a comparative design model to contrast paper-based consent with E-consent. Our findings provide an overview of the current state of E-consent use in healthcare settings.
A Model for Context-Sensitive Digital Integrators
Authors: Kuziemsky Craig , Knudsen Casper , Nøhr Christian , Villumsen Sidsel , Baysari Melissa
Published: August 2024

The realization of benefits from health information technology (HIT) implementation takes place in the long tail of implementation that must integrate technology, work practices and contextual factors. While formal health informatics education programs exist, they tend to be focused at the strategic management or specialized implementation level. HIT support at the local level often falls to clinical care staff that have little or no formal training in HIT implementation. This paper will expand on the term context-sensitive digital integrators (CSDI) as a role that could support local implementation. We discuss the CSDI role, including terminology, competencies and the role context sensitivity plays in implementation, and how CSDIs could be better supported and trained in different contexts.
Augmented Reality-Based Exergames for Persons with Intellectual Disability
Authors: Martinez-Millana Antonio , Gomez-Noe Alejandro , Hartvigsen Gunnar , Michalsen Henriette , Anke Audny , Stellander Magnus , Dybwad Dorthe , Luzi Thomas , Henriksen André
Published: August 2024

This paper explores the significance of physical activity for individuals with intellectual disabilities and proposes an innovative approach using augmented reality exergames to promote adherence. An augmented reality-based app for sorting elements while walking was specifically designed and implemented. Exergames hold promise as effective interventions for promoting physical activity and improving the overall well-being of individuals with intellectual disabilities.
Advancing CPR Training with Virtual Reality: A Review
Authors: Nikiema Serge Lebian Wilfried , Barro Seydou G. , Kantagba Yves Marie Kevin , Staccini Pascal
Published: August 2024

This literature review assesses the effectiveness of virtual reality (VR) serious games in cardiopulmonary resuscitation training, focusing on its impact on skill acquisition, confidence, and knowledge retention.
Leveraging Clinical Data Treasures: Integration of an AI Platform into Clinical IT
Authors: Arshad Kfeel , Ardalan Saman , Schreiweis Björn , Bergh Björn
Published: August 2024

In recent years, there has been a rapid growth in the use of AI in the clinical domain. In order to keep pace with this development, a framework should be created in which clinical AI models can be easily trained, managed and applied. In our study, we propose a clinical AI platform that supports the development cycle and application of clinical AI models. We consider not only the development of an isolated clinical AI platform, but also its integration into clinical IT. This includes the consideration of so-called medical data integration centers. We evaluate our approach with the aid of a clinical AI use case to demonstrate the functionality of our clinical AI platform.
Opportunities, Risks, Strengths and Weaknesses of Robotic Systems in Early Neurological Rehabilitation
Authors: Waibel Ann-Kathrin , Mayer Melanie , Merkle Matteo , Fink Lisa , Maucher Helene , Breimaier Helga Elisabeth , Lünnemann Lars , Swoboda Walter
Published: August 2024

Given the conference’s focus on innovative healthcare solutions, our investigation into robotic assistance systems highlights crucial advancements in early motor rehabilitation, aligning closely with emerging healthcare priorities. In combination with conventional therapy, the assistance systems offer new possible therapy programs. They can be used to mobilize and move patients as early as possible. The paper discusses the possibilities that arise from their use and considers the obstacles that arise. As part of a qualitative survey, nine expert interviews from different fields were conducted to guide them on robotic assisted living systems. The results obtained were summarized by coding into categories and evaluated. Our analysis of 148 coding points from nine expert interviews reveals significant insights into the strengths and weaknesses of robotic systems in neurorehabilitation. Each point was meticulously categorized to reflect its impact on both practice and patient outcomes, highlighting the practical implications of our findings. The results of the survey and the literature indicate a positive effect of robotic assistance systems in early rehabilitation. Their use requires intensive monitoring and studies on the long-term application of the systems.
Use of TAM Questionnaire for Telemedicine and IT Healthcare Systems
Authors: Tsirintani Maria
Published: August 2024

A quality systematic literature review was conducted about the use of TAM questionnaire for Telemedicine and IT healthcare systems worldwide by health professionals to investigate most valuable predictors for IT systems acceptance and possible parameters that influence them. The results highlight that perceived usefulness and perceived ease of use are the most important predictors and the development of the tool should also involve individual, social and organizational factors.
Reuse of Adverse Effect Reports from the French National Agency of Medicines: A Visual Analytic Tool to Improve Patient Safety
Authors: Saint-Dizier Chloé , Dufrenois Florian , David Emeric , Bubrovszky Maxime , Lamer Antoine
Published: August 2024

Adverse drug reaction are defined as “harmful or unpleasant reaction, resulting from an intervention related to the use of a medicinal product”. In France, adverse effects due to medicines are reported to the French National Agency of Medicines (ANSM) by the healthcare professionals or consumers. The objective of this study was to implement a tool that facilitates the utilization of ANSM reports by synthesizing information to effectively inform prescribers and users. We focused on 3 psychotropic classes: antidepressants, antipsychotics and anxiolytics. We extracted relevant data from the ANSM website through a webscraping process, based on the names of molecules in these 3 classes: antidepressants, antipsychotics, and anxiolytics. We implemented a web interface with R Shiny that provides three panels: (i) a presentation of the active ingredient with the fewest reports for a selected adverse effect category, (ii) the adverse reactions for a selected active ingredient ranked in descending order, and (iii) a comparison of two active ingredients where, for each adverse effect, the active ingredient with the fewest reported adverse drug events (ADEs) is displayed. Our application allows for synthesizing information to effectively inform prescribers and users. In the ANSM existing interface, molecules can only be viewed one by one, and the ratio needs to be calculated manually, making it difficult to compare molecules. It is important to note that this is not a prescription assistance device but rather for informational purposes. In the future, the application may be expanded to include other categories of molecules. Finally, the indicators provided by our tool could be compared to those from other pharmacovigilance databases.
Distributed Ledger-Based System to Support Health Data Integrity and Transparency
Authors: Kask Marten , Piho Gunnar , Ross Peeter
Published: August 2024

This article addresses critical health data integrity by proposing an HF (Hyperledger Fabric)-based architecture with integration into the global health data architecture based on distributed content-addressable storage networks.
Developing a Generative AI-Powered Chatbot for Analyzing MAUDE Database
Authors: Yu Yue , Shi Yuheng , Feng Yubo , Gong Yang
Published: August 2024

This paper presents a chatbot that simplifies accessing and understanding the open-access records of adverse events related to medical devices in the MAUDE database. The chatbot is powered by generative AI technology, enabling count and search queries. The chatbot uses the openFDA API and GPT-4 model to interpret users’ natural language queries, generate appropriate API calls, and summarize adverse event reports. The chatbot also provides a downloadable link to the original reports. The model’s performance in generating accurate API calls was assessed and improved by training it with few-shot examples of query-URL pairs. Additionally, the quality of content-based summaries was evaluated by human expert ratings. This initiative is a significant step towards making patient safety data accessible, replicable, and easily manageable by a broader range of researchers.
What Technologies Based on Unique Patient Identifiers Are Used for Patient Identification in Healthcare?
Authors: Svandova Katerina , Smutny Zdenek
Published: August 2024

Ensuring the correct identification of the patient is key to matching the correct patients with the proper care (e.g. correct administration of medications and treatments), but it is also applied, for example, to monitoring the patient’s movement in the hospital environment. This scoping review aims to find out what technologies based on unique patient identifiers are used to identify patients in healthcare facilities to increase patient safety and to identify future research trends. PRISMA-ScR guidelines were used, and the search focused on Web of Science and Scopus citation databases from 2000 to February 2024. Thirty-two papers dealing with patient identification methods from the point of view of person identification were found. The solutions found were built on the technologies (linear or 2D) of barcodes, RFID and NFC tags. None of the patient identification solutions found offer complete accuracy due to the human factor, and each solution targets a different problem context associated with a particular type of health facility. Future research can focus on the combination of multiple technologies, including biometric methods, to improve identification and tools to support decisions about the use of technology in a particular context and health facility (e.g. hospitals, medical nursing homes).
The Problem of Sequential Entry of Nurses as a Disincentive to Medical Safety and a Factor Increasing Overtime
Authors: Tsuru Satoko , Tamamoto Tetsuro , Nakao Akihiro , Hara Tatsunori , Yasui Seiichi
Published: August 2024

The purpose of this study was to visualize the nurses’ approach to recording and to identify the causes that lead to overtime. The data used were those related to the performance and entry of nursing tasks (observation and care acts). Data from Hospital A, a provider of acute care with approximately 400 beds, was used. One month of work data were obtained from 12 nurses who were judged by the chief nurse to be excellent and efficient in their work. 12 nurses were divided into three groups: a high-level sequential entry rate group (>0.6), a medium-level sequential entry rate group (0.4 to <0.6), and a low-level sequential entry rate group (<0.4). The high-level sequential input rate group had data input every time they performed their work, so that the work implementation and input implementation were completed at 5:00 p.m. The medium-level sequential entry rate group tended to enter data about an hour late when they started work, which tended to result in overtime. The low-level sequential input rate group tended to input work in the afternoon, resulting in significant overtime. The data of approximately 400 nurses in a 900-bed University Hospital B, where Team Compass is implemented, were used to analyze the actual sequential input rate. 884 nurses in University Hospital B, of whom 397 had input data for 19 consecutive months. The sequential input rate in November 2020, one and a half years after the start of operation, was divided into five groups by 20%: 0-20%: 27 (7%), 20-40%: 30 (7%), 40-60%: 37 (9%), 60-80%: 69 (17%), and over 80%: 234 (59%). The number of employees who have been working overtime hours is also high. Although the importance of sequential entry to achieve results in reducing overtime hours continued to be strongly presented, there were 23% of nurses who indicated a sequential entry rate of less than 60%.
Integration of Oncological Data into openEHR: A Path Towards Improved Cancer Care and Research
Authors: Schladetzky Jan , Reimer Niklas , Nicolaus Herwig , Busch Hauke , Schreiweis Björn , Kock-Schoppenhauer Ann-Kristin
Published: August 2024

The integration of tumor-related diagnosis and therapy data is a key factor for cancer-related collaborative projects and research projects on-site. The Medical Data Integration Center (MeDIC) of the University Hospital Schleswig-Holstein, resulting from the Medical Informatics Initiative and Network University Medicine in Germany, has agreed on an openEHR-based data management based on a centralized repository with harmonized annotated data. Consequently, the oncological data should be integrated into the MeDIC to interconnect the information and thus gain added value. A uniform national data set for tumor-related reports is already defined for the cancer registries. Therefore, this work aims to transform the national oncological basis data set for tumor documentation (oBDS) so that it can be stored and utilized properly in the openEHR repository of the MeDIC. In a previous work openEHR templates representing the oncological basis data set were modeled. These templates were used to implement a processing pipeline including a metadata repository, which defines the mappings between the elements, a FHIR terminology service for annotation and validation, resulting in a tool to automatically build openEHR compositions from oBDS data. The prototype proved the feasibility of the referred mapping, integration into the MeDIC is straightforward and the architecture introduced is adaptable to future needs by design.
Real-World Data in Greece: Mapping the Papageorgiou General Hospital Data to the OMOP Common Data Model
Authors: Papapostolou Grigoris , Chytas Achilleas , Rekkas Alexandros , Bigaki Maria , Zeimpekis Demetrios , Dermentzoglou Lampros , Tortopidis George , Natsiavas Pantelis
Published: August 2024

This paper showcases the results of the Extract-Transform-Load process mapping the Electronic Health Record of Papageorgiou General Hospital in Thessaloniki, Greece, to the Observational Medical Outcomes Partnership Common Data Model. We describe the staged process utilized to account for the intricate structure of the database, along with some general findings from the mapping. Finally, we investigate potential directions for future research.
Active Learning Pipeline to Identify Candidate Terms for a CDSS Ontology
Authors: Jing Xia , Goli Rohan , Komatineni Keerthana , Alluri Shailesh , Hubig Nina , Min Hua , Gong Yang , Sittig Dean F. , Biondich Paul , Robinson David , Nøhr Christian , Faxvaag Arild , Wright Adam , Law Timothy , Rennert Lior , Gimbel Ronald
Published: August 2024

Ontology is essential for achieving health information and information technology application interoperability in the biomedical fields and beyond. Traditionally, ontology construction is carried out manually by human domain experts (HDE). Here, we explore an active learning approach to automatically identify candidate terms from publications, with manual verification later as a part of a deep learning model training and learning process. We introduce the overall architecture of the active learning pipeline and present some preliminary results. This work is a critical and complementary component in addition to manually building the ontology, especially during the long-term maintenance stage.
Data Standardization in the Medical Field Through FHIR and FAIR Implementation: A Systematic Review
Authors: Niţulescu Adina , Stoicu-Tivadar Lăcrămioara
Published: August 2024

The authors investigate in this paper the current situation of the FHIR resources adoption in order to FAIRify data in the medical research field. By aligning with the FAIR data principles, data becomes easier to share and reuse. This review aims to analyze how integrating the FHIR resources improved the findability, accessibility, interoperability, and reusability of datasets. By searching for the state-of-art situation in this field, we want to emphasize the significant role that FAIR data occupies in the medical research community, by also providing directions for further development and improved interoperability.
Interoperable Integration of Automatic ECG Processing Using DICOMweb and the AcuWave Software Suite
Authors: Graf Lennart , Krefting Dagmar , Kesztyüs Tibor , Oremek Maximilian , Zenker Sven , Spicher Nicolai
Published: August 2024

Established cardiovascular risk scores are typically based on items from structured clinical data such as age, sex, or smoking status. Cardiovascular risk is also assessed from physiological measurements such as electrocardiography (ECG). Although ECGs are standard diagnostic tools in clinical care, they are scarcely integrated into clinical information systems. To overcome this roadblock, we propose the integration of an automatic workflow for ECG processing using the DICOMweb interface to transfer ECGs in a standardised way. We implemented the workflow using non-commercial software and tested it with about 150,000 resting ECGs acquired in a maximum-care hospital. We employed Orthanc as DICOM server and AcuWave as signal processing application and implemented a fully-automated workflow which reads the ECG data and computes heart rate-related parameters. The workflow is evaluated on off-the-shelf hardware and results in an average run time of approximately 40 ms for processing a single ECG.