bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Clinical Variations in Asthma Care Documented in Electronic Health Records Between Staff and Resident Physicians
Authors: Sohn Sunghwan  , Wi Chung-Il  , Juhn Young J.  , Liu Hongfang
Published: February 2025

Clinical documentation using free text to describe a patient's medical status is an essential component of electronic health records (EHRs), and the quality of information in documents plays a critical role in clinical practice and translational research. Physicians are the primary creators of EHRs, but their clinical practices vary substantially, resulting in variations in clinical documentation. These variations can represent a source for potential bias in clinical outcomes and downstream applications using EHRs. Asthma is one example, presenting an inconsistent ascertainment process and criteria. A recent study revealed that resident physicians' knowledge of asthma diagnosis and management is relatively limited. In this study, we examined clinical documentation variations in asthma care between staff and resident physicians using individual words, topics, and asthma-related concepts in EHR clinical narratives. Additionally, we discuss potential biases in building an informatics model and further compare asthma diagnosis and outcomes between two physician groups.
A Study on Saudi Diabetic Patients' Readiness to Use Mobile Health
Authors: Alenazi Hanan  , Alghamdi Mariam  , Alradhi Sarah  , Househ Mowafa  , Zakaria Nasriah
Published: February 2025

The acceptance and feasibility for using mobile health technologies for diabetes management in Saudi Arabia remains to be explored. We created the Mobile Health Effectiveness and Readiness Questionnaire (MHERQ) to measure its validity and reliability in assessing the readiness of Saudi diabetic patients use of mobile health technologies for diabetes self-management. The study was able to measure the validity and reliability of MHERQ and found that MHERQ provides promising results for Saudi diabetic patients' use of mobile health technologies.
Design of Rehabilitation Treatment Coach Robot
Authors: Han Seung-Ho  , Kim Han-Gyu  , Park Chan-Yong  , Choi Ho-Jin
Published: February 2025

In the rehabilitation treatment for at-home patients, it is not only costly for a patient to hire a professional therapist or personal trainer, but also time consuming for a therapist or trainer to visit all the patients. To improve the situation, we propose the rehabilitation treatment coach robot which helps the patient to do rehabilitation exercises alone. Designed economically with cheap parts, our robot provides multiple functions: rehabilitation program suggestion, rehabilitation posture correction, and emergency detection. A brief plan is presented for data collection and performance evaluation.
Developing Mobile Health Management System for Patients with Musculoskeletal Tumor
Authors: Liang Nai-Wen  , Liu Hung-Yi  , Chien Chun-En  , Chen Qi-Guang  , Chang Po-Lun  , Chen Wei-Ming  , Wu Bo-Gui
Published: February 2025

This research provided a postoperative system for osteosarcoma which is seperated into two parts, Bone Patient and Bone Care. We focus on using smartphone features and proper interface design to facilitate self-monitoring, health analysis, and to make appointments. It achieves the effects of shared decision making (SDM). During the two weeks of research, we achieved satisfied with 4.03 ± 0.67 TAM score.
A Deep Learning Approach to Neuroanatomical Characterisation of Alzheimer's Disease
Authors: Ambastha Abhinit Kumar  , Leong Tze-Yun  , The Alzheimer's Disease Neuroimaging Initiative
Published: February 2025

Alzheimer's disease (AD) is a neurological degenerative disorder that leads to progressive mental deterioration. This work introduces a computational approach to improve our understanding of the progression of AD. We use ensemble learning methods and deep neural networks to identify salient structural correlations among brain regions that degenerate together in AD; this provides an understanding of how AD progresses in the brain. The proposed technique has a classification accuracy of 81.79% for AD against healthy subjects using a single modality imaging dataset.
MetaMap Lite in Excel: Biomedical Named-Entity Recognition for Non-Technical Users
Authors: Bhupatiraju Ravi Teja  , Fung Kin Wah  , Bodenreider Olivier
Published: February 2025

We developed an easy-to-use tool for non-technical biomedical researchers to conduct Named-Entity Recognition (NER) on biomedical text, in a familiar spreadsheet environment. The system is a simple, offline, easy to install, end-user front-end to the new MetaMap Lite. Early adopters found it to be a quick starting-point to incorporate NER in their investigations.
Classifying Clinical Notes with Pain Assessment
Authors: Fodeh Samah J.  , Finch Dezon  , Bouayad Lina  , Luther Stephen  , Kerns Robert D.  , Brandt Cynthia
Published: February 2025

Pain is a significant public health problem, affecting an estimated 100 million Americans. Evidence has highlighted that patients with chronic pain often suffer from deficits in pain care quality (PCQ). Efforts to improve PCQ hinge on the identification of reliable PCQ indicators such as pain assessment. In this study, we developed a classifier that leverages narratives in clinical notes to derive indicators of pain assessment for patients with chronic pain.
Authenticating Unknown Doctors for Access to EHRs Based on Societal Trust
Authors: Morris Kensuke  , Kume Naoto  , Yamamoto Goshiro  , Kobayashi Shinji  , Okamoto Kazuya  , Tamura Hiroshi  , Kuroda Tomohiro
Published: February 2025

Many isolated EHRs in Japan hinders the availability of patients' clinical data who need treatment in a region outside the scope of their local EHR. We propose a timelier approach that increases availability of patient clinical data to unknown doctors and has the patient and their representative involved; they can grant access to doctors who are anonymous to the patients' local EHR domain.
Developing the First Generally-Available openEHR Archetypes and Templates for Physiotherapy: An Example of Building Clinical Models and Modelling Capacity via Student-Led Academic–Industrial Collaboration
Authors: Chihab Jamila  , Franke Hildegard  , McNicoll Ian  , Darlison Matthew W.
Published: February 2025

We present the first public openEHR archetypes and templates for physiotherapy, and the context of multidisciplinary academic-industry partnership that has enabled their production by a team led by a clinically trained student on the UCL health informatics MSc programme.
Information Model Construction of EBM Resources
Authors: Zhu Yanxin  , Xu Wei  , Wang Xia
Published: February 2025

To build evidence-based medicine (EBM) databases in China, we developed a semantic structure of EBM database information model by top-level, middle-level, and bottom-level structure. Top-level structure was composed of three modules including Literature Characteristics, Treatment Process and Evidence Levels. We further specified each top-level module based on international and national metadata and health related standards. Finally, we developed a complete information semantic model of EBM resources by an ontology tool. It can provide a reference for semantic construction of Chinese EBM database.
Using Modified-ISS Model to Evaluate Medication Administration Safety During Bar Code Medication Administration Implementation in Taiwan Regional Teaching Hospital
Authors: Ma Pei-Luen  , Jheng Yan-Wun  , Jheng Bi-Wei  , Hou I-Ching
Published: February 2025

Bar code medication administration (BCMA) could reduce medical errors and promote patient safety. This research uses modified information systems success model (M-ISS model) to evaluate nurses' acceptance to BCMA. The result showed moderate correlation between medication administration safety (MAS) to system quality, information quality, service quality, user satisfaction, and limited satisfaction.
Core Curriculum Comparisons in Master Degree Programs for Patient Safety
Authors: Zhang QiYan  , Takeda Hiroshi
Published: February 2025

A content analysis was performed to investigate core curriculum in master degree programs for patient safety. Twenty programs from six countries and 179 core curriculum/modules were extracted and reviewed. The curricula were compared to the topics recommended in WHO's Multi-professional Patient Safety Curriculum Guide and the core content relevant to patient safety issues in IMIA/AMIA recommendations for health informatics education. The differences between the identified curricula with those addressed in the aforementioned Guide/Recommendations were examined.
Looking for the Best WOW: Understanding the Nurses' Needs
Authors: Schachner Bibiana  , González Zulma  , Cano Rodrigo  , Luna Daniel  , Benítez Sonia
Published: February 2025

The effective use of nurses' time for providing increasingly safe, efficient, and patient-centered care is a major concern for healthcare managers as well as for nurses themselves. Different solutions have been used aimed at improving those times by providing nurses with mobile and ‘on wheels’ alternatives for bedside care. Nevertheless, the selection of solutions is still a complicated organizational decision. This paper describes the evaluation of ergonomic characteristics of five local mobile carts for the bedside nursing care at Hospital Italiano de Buenos Aires. Cornell University's checklist was used for data collection according to five domains: car handling, work surface and data load, documents and screen reading, storage and dimensions, and energy and cleaning. Considering the scarcity of literature on needs for WOWs assessment, the findings of this paper represent a valuable approach to the requirements of nurses in real work environments and a support for decision-making based on nursing observations.
Sharing Health Big Data for Research – A Design by Use Cases: The INSHARE Platform Approach
Authors: Bouzillé Guillaume  , Westerlynck Richard  , Defossez Gautier  , Bouslimi Dalel  , Bayat Sahar  , Riou Christine  , Busnel Yann  , Le Guillou Clara  , Cauvin Jean-Michel  , Jacquelinet Christian  , Pladys Patrick  , Oger Emmanuel  , Stindel Eric  , Ingrand Pierre  , Coatrieux Gouenou  , Cuggia Marc
Published: February 2025

Sharing and exploiting Health Big Data (HBD) allow tackling challenges: data protection/governance taking into account legal, ethical, and deontological aspects enables trust, transparent and win-win relationship between researchers, citizens, and data providers. Lack of interoperability: compartmentalized and syntactically/semantica heterogeneous data. INSHARE project using experimental proof of concept explores how recent technologies overcome such issues. Using 6 data providers, platform is designed via 3 steps to: (1) analyze use cases, needs, and requirements; (2) define data sharing governance, secure access to platform; and (3) define platform specifications. Three use cases – from 5 studies and 11 data sources – were analyzed for platform design. Governance derived from SCANNER model was adapted to data sharing. Platform architecture integrates: data repository and hosting, semantic integration services, data processing, aggregate computing, data quality and integrity monitoring, Id linking, multisource query builder, visualization and data export services, data governance, study management service and security including data watermarking.
What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks
Authors: Bruland Philipp  , Doods Justin  , Storck Michael  , Dugas Martin
Published: February 2025

Data dictionaries provide structural meta-information about data definitions in health information technology (HIT) systems. In this regard, reusing healthcare data for secondary purposes offers several advantages (e.g. reduce documentation times or increased data quality). Prerequisites for data reuse are its quality, availability and identical meaning of data. In diverse projects, research data warehouses serve as core components between heterogeneous clinical databases and various research applications. Given the complexity (high number of data elements) and dynamics (regular updates) of electronic health record (EHR) data structures, we propose a clinical metadata warehouse (CMDW) based on a metadata registry standard. Metadata of two large hospitals were automatically inserted into two CMDWs containing 16,230 forms and 310,519 data elements. Automatic updates of metadata are possible as well as semantic annotations. A CMDW allows metadata discovery, data quality assessment and similarity analyses. Common data models for distributed research networks can be established based on similarity analyses.
Detecting Signals of Interactions Between Warfarin and Dietary Supplements in Electronic Health Records
Authors: Fan Yadan  , Adam Terrence J.  , McEwan Reed  , Pakhomov Serguei V.  , Melton Genevieve B.  , Zhang Rui
Published: February 2025

Drug and supplement interactions (DSIs) have drawn widespread attention due to their potential to affect therapeutic response and adverse event risk. Electronic health records provide a valuable source where the signals of DSIs can be identified and characterized. We detected signals of interactions between warfarin and seven dietary supplements, viz., alfalfa, garlic, ginger, ginkgo, ginseng, St. John's Wort, and Vitamin E by analyzing structured clinical data and unstructured clinical notes from the University of Minnesota Clinical Data Repository. A machine learning-based natural language processing module was further developed to classify supplement use status and applied to filter out irrelevant clinical notes. Cox proportional hazards models were fitted, controlling for a set of confounding factors: age, gender, and Charlson Index of Comorbidity. There was a statistically significant association of warfarin concurrently used with supplements which can potentially increase the risk of adverse events, such as gastrointestinal bleeding.
Extracting Eligibility Criteria from the Narrative Text of Scientific Research Articles
Authors: Lin Ching-Yun  , Liou Der-Ming  , Pan Mei-Lien
Published: February 2025

Eligibility criteria among hundreds of National Health Insurance Research Database (NHIRD) research papers have similar constituent elements, such as demographic characteristics or diagnostic codes. The study results of the same disease could vary among different research due to the variation of the criteria statements, therefore the narrative patterns analysis tool would be helpful for summarizing the knowledge implicitly contained in the eligibility criteria. In this study, we developed a series of R-based text processing methods to extract the narrative eligibility criteria in NHIRD papers by simplifying the article titles and content paragraphs, identifying medical concepts and abbreviations, then detecting basic demographic characteristics and ICD-9-CM diagnosis codes. Although there is still room for improvement on study type identifying, the high performance in classifying the study type, detecting age restrictions and extracting ICD-9-CM codes still shows the system usefulness for the analysis of eligibility criteria.
Contrast Mining for Pattern Discovery and Descriptive Analytics to Tailor Sub-Groups of Patients Using Big Data Solutions
Authors: Phinney Michael A.  , Zhuang Yan  , Lander Sean  , Sheets Lincoln  , Parker Jerry C.  , Shyu Chi-Ren
Published: February 2025

The shift to electronic health records has created a plethora of information ready to be examined and acted upon by those in the medical and computational fields. While this allows for novel research on a scale unthinkable in the past, all discoveries still rely on some initial insight leading to a hypothesis. As the size and variety of data grows so do the number of potential findings, making it necessary to optimize hypothesis generation to increase the rate and importance of discoveries produced from the data. By using distributed Association Rule Mining and Contrast Mining in a big data ecosystem, it is possible to discover discrepancies within large, complex populations which are inaccessible using traditional methods. These discrepancies, when used as hypotheses, can help improve patient care through decision support, population health analytics, and other areas of healthcare.
Phenotypic Analysis of Clinical Narratives Using Human Phenotype Ontology
Authors: Shen Feichen  , Wang Liwei  , Liu Hongfang
Published: February 2025

Phenotypes are defined as observable characteristics and clinical traits of diseases and organisms. As connectors between medical experimental findings and clinical practices, phenotypes play vital roles in translational medicine. To facilitate the translation between genotype and phenotype, Human Phenotype Ontology (HPO) was developed as a semantically computable vocabulary to capture phenotypic abnormalities found in human diseases discovered through biomedical research. The use of HPO in annotating phenotypic information in clinical practice remains unexplored. In this study, we investigated the use of HPO to annotate phenotypic information in clinical domain by leveraging a corpus of 12.8 million clinical notes created from 2010 to 2015 for 729 thousand patients at Mayo Clinic Rochester campus and assessed the distribution information of HPO terms in the corpus. We also analyzed the distributional difference of HPO terms among demographic groups. We further demonstrated the potential application of the annotated corpus to support knowledge discovery in precision medicine through Wilson's Disease.
A Collaborative Evaluation Framework for Biometric Connected Devices in Healthcare
Authors: Farnia Troskah  , Jaulent Marie Christine  , Marchand Guillaume  , Yasini Mobin
Published: February 2025

A large number of biometric connected devices are currently available with a variety of designs. Healthcare users cannot easily choose the reliable ones that correspond the best to their healthcare problems. The existing evaluation methods do not consider at the same time aspects of connectivity and healthcare usage. In this study, a collaborative evaluation framework for biometric connected devices in healthcare usage is proposed. This framework contains six dimensions: medical validity, technical reliability, usability, ergonomy, legal compliance, and accuracy of measurements. In a first step, these dimensions were assessed by designing a self administered questionnaire answered by the stakeholders (patients, health professionals, payers, and manufacturers). A case study was then carried out in a second step to test this framework in a project of telemonitoring for heart failure patients. The results are in favor of the efficiency of the proposed framework as a decision making tool in healthcare usage.
Patients' Online Access to Electronic Health Records: Current Status and Experiences from the Implementation in Sweden
Authors: Hägglund Maria  , Scandurra Isabella
Published: February 2025

The number of eHealth services for patients is rapidly increasing worldwide. This paper describes the status of a very important eHealth service for patients in Sweden, the Patient Accessible Electronic Health Record (PAEHR). As many countries are facing an introduction of national eHealth services providing health information to the patients, lessons learned from Sweden may improve the deployment and use of PAEHRs and similar eHealth services. Challenges that remain in Sweden relate to local differences in the implementation that lead to fragmentation and unequal access to information. Initiatives have been taken to reconcile some of the problems, e.g. an updated national regulatory framework for PAEHR. To date, evaluations are often performed from a healthcare provider perspective, focusing on aspects that are considered important by healthcare professionals and decision makers. Based on experiences of this nation-wide implementation we argue for the need to also base evaluations of eHealth on the perspective of the patients.
Supporting Regional Aged Care Nursing Staff to Manage Residents' Behavioural and Psychological Symptoms of Dementia, in Real Time, Using the Nurses' Behavioural Assistant (NBA): A Pilot Site ‘End-User Attitudes’ Trial
Authors: Klein Britt  , Clinnick Lisa  , Chesler Jessica  , Stranieri Andrew  , Bignold Adam  , Dazeley Richard  , McLaren Suzanne  , Lauder Sue  , Balasubramanian Venki
Published: February 2025

Background: This regional pilot site ‘end-user attitudes’ study explored nurses' experiences and impressions of using the Nurses' Behavioural Assistant (NBA) (a knowledge-based, interactive ehealth system) to assist them to better respond to behavioural and psychological symptoms of dementia (BPSD) and will be reported here.
Supporting Diverse Challenges of Ageing with Digital Enhanced Living Solutions
Authors: Grundy John  , Mouzakis Kon  , Vasa Rajesh  , Cain Andrew  , Curumsing Maheswaree  , Abdelrazek Mohamed  , Fernando Niroshine
Published: February 2025

By the 2050, it is estimated that the proportion of people over the age of 80 will have risen from 3.9% to 9.1% of population of Organisation for Economic Cooperation and Development countries. A large proportion of these people will need significant help to manage various chronic illnesses, including dementia, heart disease, diabetes, limited physical movement and many others. Current approaches typically focus on acute episodes of illness and are not well designed to provide adequately for daily living care support. In our rapidly ageing society, a critical need exists for effective, affordable, scalable and safe in-home and in-residential care solutions leveraging a range of current and emerging sensor, interaction and integration technologies. Key aims are to support the ageing to live longer in their own homes; make daily challenges associated with ageing less limiting through use of technology supports; better support carers – both professional and family – in providing monitoring, proactive intervention, and community connectedness; enable in-home and in-residential care organisations to scale their support services and better use their workforces; and ultimately provide better quality of life. Deakin University researchers have been investigating a range of emerging technologies and platforms to realise this vision, which we in broad terms coin Digital Enhanced Living, in the ageing space but also supporting those with anxiety and depression, sleep disorders, various chronic diseases, recovery from injury, and various predictive analytics. A Smart Home solution, carried out in conjunction with a local start-up, has produced and trialled a novel sensor, interaction, and AI-based technology. Virtual Reality (VR) solutions have been used to support carers in the set-up of dementia-friendly homes, in conjunction with Alzheimers Australia. Activity and nutrition solutions, including the use of conversational agents, have been used to build dialogue to engage and change behaviour. Predictive analytics, in conjunction with major hospitals, have been applied to large medical datasets to better support professionals making judgements around discharge outcomes. A set of lessons have been learned from the design, deployment and trialing of these diverse solutions and new development approaches have been crafted to address the challenges faced. In particular, we found that there is a need to consider user emotional expectations as first-class citizens and create methodologies that consider the user needs during the creation of the software solutions. We find that quality and emotional aspects have to be engineered into the solution, rather than added after a technical solution is deployed.
How Patients Talk About Care? Identifying Patient Experience Expressions from Online Discussions
Authors: Lahti Lauri  , Tenhunen Henni  , Nieminen Marko
Published: February 2025

Patient experience is an emerging concept that supports the improvement of healthcare services through identified patient expectations and experiences. In addition to structured feedback through official channels, experiences about healthcare appear increasingly in digital services and social media. We explore a new patient experience harvesting process based on linguistic patterns to identify relevant expressions in online discussions about children's health. Our results from the analysis of 98 229 unique sentences suggests that the 7-step process can be useful in discovering patients' evaluations of their care experiences. We propose ways to extend the process to other care contexts by adjusting the semantic reference models.
Insights into Pharmacotherapy Management for Parkinson's Disease Patients Using Wearables Activity Data
Authors: Nguyen Vincent  , Kunz Holger  , Taylor Paul  , Acosta Dionisio
Published: February 2025

We investigate what supervised classification models using clinical and wearables data are best suited to address two important questions about the management of Parkinson's Disease (PD) patients: 1) does a PD patient require pharmacotherapy or not, and 2) whether therapies are having an effect. Currently, patient management is suboptimal due to using subjective patient reported episodes to answer these questions.
Uptake of a Dashboard Designed to Give Realtime Feedback to a Sentinel Network About Key Data Required for Influenza Vaccine Effectiveness Studies
Authors: Pathirannehelage Sameera  , Kumarapeli Pushpa  , Byford Rachel  , Yonova Ivelina  , Ferreira Filipa  , de Lusignan Simon
Published: February 2025

Dashboards are technologies that bringing together a range of data sources for observational or analytical purposes. We have created a customised dashboard that includes all the key data elements required for monitoring flu vaccine effectiveness (FVE). This delivers a unique dashboard for each primary care provider (general practice) providing data to the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC), one of the oldest European surveillance systems. These FVE studies use a test negative case control (TNCC) design. TNCC requires knowledge of practice denominator; vaccine exposure, and results of influenza virology swabs carried out to identify in an influenza-like-illness (ILI), a clinical diagnosis, really is influenza. The dashboard displays the denominator uploaded each week into the surveillance system, compared with the nationally known practice size (providing face-validity for the denominator); it identifies those exposed to the vaccine (by age group and risk category) and virology specimens taken and missed opportunities for surveillance (again by category). All sentinel practices can access in near real time (4 working days in areas) their rates of vaccine exposure and swabs conducted. Initial feedback is positive; 80% (32/40) practices responded positively.
Applications of Machine Learning in Fatty Live Disease Prediction
Authors: Islam Md. Mohaimenul  , Wu Chieh-Chen  , Poly Tahmina Nasrin  , Yang Hsuan-Chia  , Li Yu-Chuan (Jack)
Published: February 2025

: Fatty liver disease (FLD) is considered the most prevalent form of chronic liver disease worldwide. The prediction of fatty liver disease is an important factor for effective treatment and reduce serious health consequences. We, therefore construct a prediction model based on machine learning algorithms. A dataset was developed with ten attributes that included 994 liver patients in which 533 patients were females and others were male. Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Logistic Regression (RF) data mining technique with 10-fold cross-validation was used in the proposed model for the prediction of fatty liver disease. The performances were evaluated with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. In this proposed model, logistic regression technique provides a better result (Accuracy 76.30%, sensitivity 74.10%, and specificity 64.90%) among all other techniques. This study demonstrates that machine learning models particularly logistic regression model provides a higher accurate prediction for fatty liver diseases based on medical data from electronic medical. This model can be used as a valuable tool for clinical decision making.
Health Professionals' Expanding eHealth Competences for Supporting Patients' Self-Management
Authors: Kujala Sari  , Rajalahti Elina  , Heponiemi Tarja  , Hilama Pirjo
Published: February 2025

An increasing number of new eHealth services that support patients' self-management has changed health professionals' work and has created a need for a new eHealth competence. In this study, we evaluated the health professionals' eHealth competences and training needs in a public health organization in Finland. The target organization's goal was to increase the number of eHealth services provided to patients, and health professionals and their competences were seen as critical for the adoption of services. Data was collected through an online survey of 701 health professionals working in the target organization. Professionals perceived their basic computer skills as good and they were mostly willing to use eHealth services in patient work. However, health professionals need guidance, especially in their patient work in the new eHealth-enabled environment. They were less confident about their competence to motivate and advise patients to use eHealth services and how to communicate with patients using eHealth solutions. The results also imply that eHealth competence is not merely about an individual's skills but that organizations need to develop new working processes, work practices and distribution of work. We suggest that the training and support needs identified be considered in curricula and lifelong learning.
Enhancing Safety of Artificially Ventilated Patients Using Ambient Process Analysis
Authors: Lins Christian  , Gerka Alexander  , Lüpkes Christian  , Röhrig Rainer  , Hein Andreas
Published: February 2025

In this paper, we present an approach for enhancing the safety of artificially ventilated patients using ambient process analysis. We propose to use an analysis system consisting of low-cost ambient sensors such as power sensor, RGB-D sensor, passage detector, and matrix infrared temperature sensor to reduce risks for artificially ventilated patients in both home and clinical environments. We describe the system concept and our implementation and show how the system can contribute to patient safety.
The Evaluation of Decision Support Tools Requires a Measure of Decision Quality That Has Content and Construct Validity in Person-Centred Care
Authors: Kaltoft Mette Kjer  , Nielsen Jesper Bo  , Dowie Jack
Published: February 2025

The evaluation of decision aids and support interventions requires a multi-attribute index which can be calculated for both aid and comparator/s. The Decision Conflict Scale (DCS) is such an index and has been widely used in this context, the recent Cochrane review Decision aids for people facing health treatment or screening decisions reporting its use in 63 of the 105 studies included. However, while the DCS may be a valid measure for the eponymous construct – decision conflict - it lacks both content and construct validity for the evaluation of decision aids. It lacks content validity for this task because of the 3 items which make up its Uncertainty subscale, which penalize an aid that correctly reports the situation is one of decisional equipoise or near equipoise. A ‘false clarity’ bias in aid presentation is encouraged by being rewarded. In this paper we confirm that the inclusion of the Uncertainty subscale in the DCS has inappropriate empirical consequences for decision aid evaluation. Excluding the Uncertainty items would address this content invalidity, but the DCS would still lack construct validity, since the construct being measured is being treated, and inappropriately psychometrically validated, as a reflective rather than formative one. The component scales (items) of an index for a formative construct need to be preference-weighted, in ethical person-centred care by the individual at the point of decision. We argue that the most appropriate formative construct for use in decision aid evaluation is decision quality, and suggest MyDecisionQuality (MDQ) as the first formative index that can claim to both possess content and construct validity for the measurement of decision quality. However, like all multi-criterial formative metrics, the construct that MDQ measures is constructed by the measure and has no existence independent of it.
Understanding Study Participants Views on Co-Creation of Data and Use of EHR in Clinical Studies
Authors: Scott Duncan Therese  , Hägglund Maria
Published: February 2025

In order to increase clinical trial participation, the reasons for participating need to be observed. Since there is rather inadequate information concerning how individuals such as patients, decides to participate in clinical trials semi-structured interviews have been done. Examining the use of EHR in clinical trials and co-creation of data, the result showed that it is important for the researches to have access to the patients' EHR and for the patients to contribute with their own ideas of research. Important aspects of further participation in clinical trials were that it should be fun and informative. The patients agreed on that the effort of participating could decrease with the use of electronically collection and self-reporting of data, e.g. through a patient portal.
Implementation and Effect of a Novel Electronic Medical Record Format for Patient Allergy Information
Authors: Nakayama Masaharu  , Inoue Ryusuke
Published: February 2025

Adverse drug events (ADEs) are critical. Approximately 10% of fatal ADEs are believed to be allergic reactions. Therefore, sharing patient allergy information is beneficial to medical staff members in avoiding potentially lethal complications. We previously performed a nationwide study of patient allergy information in Japanese hospitals. The report showed that most of the responding hospitals needed a standard format for reporting the information. To establish this, we implemented a novel format for recording patient allergy information into the hospital information system at Tohoku University Hospital; this format was created through vigorous discussion among medical staff members with a variety of specialties, including doctors, nurses, pharmacists, nutritionists, and medical safety managers. In this study, we followed the amount of inputted allergy information and the number of incidents involving medication after implementation. The amount of allergy information inputted increased slightly. Although incidents involving medication also increased slightly, ADEs due to allergy significantly decreased. We believe that our findings will be useful in helping to determine the optimal characteristics of drug allergy information and to improve the dissemination of information regarding potential allergens and subsequent ADEs.
Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data
Authors: van Soest Johan  , Sun Chang  , Mussmann Ole  , Puts Marco  , van den Berg Bob  , Malic Alexander  , van Oppen Claudia  , Towend David  , Dekker Andre  , Dumontier Michel
Published: February 2025

Conventional data mining algorithms are unable to satisfy the current requirements on analyzing big data in some fields such as medicine, policy making, judicial, and tax records. However, applying diverse datasets from different institutes (both healthcare and non-healthcare related) can enrich information and insights. So far, analyzing this data in an automated, privacy-preserving manner does not exist to our knowledge. In this work, we propose an infrastructure, and proof-of-concept for privacy-preserving analytics on vertically partitioned data.
Profiling Databases to Facilitate Comparison of Child Health Systems Across Europe Using Standardised Quality Markers
Authors: Liyanage Harshana  , Shinneman Stacy  , Hoang Uy  , Ferreira Filipa  , Alexander Denise  , Rigby Michael  , Blair Mitch  , de Lusignan Simon
Published: February 2025

Models of child primary health care vary across Europe. There are three categories, primary care paediatricians, general practitioner based, or mixed. This paper describes the metadata schema used in the profiling process of candidate data sources for appraisal for the Models of Child Health Appraised (MOCHA) project using the MOCHA International Research Opportunity Instrument (MIROI). The ten clinical indicators included: asthma, antibiotic stewardship, immunisation, rickets, diarrhea, epilepsy, depression, ADHD, enuresis and care of women during pregnancy. Our metadata allows us to identify data within included data sources concerning any of the 10 clinical indicators identified for comparative analysis within the MOCHA project. From the 30 countries we found a minimum of 5 and a maximum of 36 different databases for each indicator.
Assessing the Satisfaction of Citizens Using Teleconsent in Clinical Research
Authors: Newlin Thomas  , McCall Terika  , Ottmar Paige  , Welch Brandon  , Khairat Saif
Published: February 2025

Researchers tested the functionality, and evaluated the feasibility of a telemedicine software, Doxy.me, to complete informed teleconsent sessions remotely with prospective research participants. Twenty healthy volunteers completed a teleconsent session, and web survey assessing overall experience and satisfaction with the Doxy.me software. There was a statistically significant correlation found between age and mean response for the overall reaction category (r = 0.398, p = 0.091) and the systems capabilities category (r = 0.380, p = 0.099). Results suggested that younger users are more satisfied than older users with using teleconsent as a modality for informed consenting. This information will be used to improve the software design and functionality of the Doxy.me software to make the teleconsent experience as simple and intuitive as possible.
Interoperability Assets for Patient Summary Components: A Gap Analysis
Authors: Heitmann Kai U.  , Cangioli Giorgio  , Melgara Marcello  , Chronaki Catherine
Published: February 2025

The International Patient Summary (IPS) standards aim to define the specifications for a minimal and non-exhaustive Patient Summary, which is specialty-agnostic and condition-independent, but still clinically relevant. Meanwhile, health systems are developing and implementing their own variation of a patient summary while, the eHealth Digital Services Infrastructure (eHDSI) initiative is deploying patient summary services across countries in the Europe. In the spirit of co-creation, flexible governance, and continuous alignment advocated by eStandards, the Trillum-II initiative promotes adoption of the patient summary by engaging standards organizations, and interoperability practitioners in a community of practice for digital health to share best practices, tools, data, specifications, and experiences. This paper compares operational aspects of patient summaries in 14 case studies in Europe, the United States, and across the world, focusing on how patient summary components are used in practice, to promote alignment and joint understanding that will improve quality of standards and lower costs of interoperability.
Designing an Authorization System Based on Patient Privacy Preferences in Japan
Authors: Morris Kensuke  , Yamamoto Goshiro  , Hiragi Shusuke  , Ohtera Shosuke  , Sakai Michi  , Sugiyama Osamu  , Okamoto Kazuya  , Nambu Masayuki  , Kuroda Tomohiro
Published: February 2025

High accessibility of Electronic Health Record systems can increase usability but creates simultaneously patients' anxieties about privacy issues. In order to reduce the privacy concerns, we focused on control and awareness, and designed an approach that can provide availability of patient's clinical data to doctors in two scenarios; (S1) direct control by the patient when they are conscious, (S2) control by a trusted representative when the patient is unconscious. In this paper, we show further analysis in a survey (n = 310, age range: 19-91) done to test the acceptability of our concept of a using a trusted representative and to further understand the concerns of Japanese citizens to improve our system design. These results in S1 suggest that patients concerned about control have a stronger inclination to also choose full awareness. We found also that patients tended to choose the same level of awareness for the representative as they did for themselves in S2. In addition, patients who chose awareness in S1 tended to choose the same for their representative in S2 and themselves after recovery from unconsciousness. We also discuss the significant differences found between the age-groups 20-39 and 60-79. We conclude that the system design of privacy aware EHR systems must be improved to consider patients who want to preserve their choice of control in the event they become unconscious but do not want to use a representative to maintain control.
Building a Knowledge-Based Tool for Auto-Assessing the Cardiovascular Risk
Authors: Ugon Adrien  , Hadj Bouzid Amel Imene  , Jaulent Marie-Christine  , Favre Madeleine  , Duclos Catherine  , Jobez Emmanuel  , Falcoff Hector  , Lamy Jean-Baptiste  , Tsopra Rosy
Published: February 2025

The prevention of cardiovascular diseases needs first to quantify the cardiovascular risk. To estimate this risk, French national health authorities provided clinical practice guidelines extending the existing European SCORE, which doesn't include all the cardiovascular risk factors (e.g. diabetes). Hence, French national clinical practice guidelines to quantify the cardiovascular risk is able to deal with more clinical situations than the SCORE. The goal of this paper is to formalize knowledge extracted from these guidelines and implement the rules so that they can be used into an auto-assessing tool of cardiovascular risk. Formalization followed five steps and was conducted under the guidance of medical experts. It resulted into a decision tree fed by eight decision variables. Evaluation of the accuracy of the decision tree showed 80% of agreement with an expert in medical informatics in predicting the cardiovascular risk level for 15 different clinical situations. Discrepancies correspond to the knowledge gaps within Clinical Practice Guidelines. We intend to extend the implementation of the decision tree to a complete tool, for allowing patient to auto-assess their cardiovascular risk. This tool will be integrated into a platform providing recommendations adapted to the calculated level of cardiovascular risk.
Building Bayesian Networks from Causal Rules
Authors: Sedki Karima  , Tsopra Rosy
Published: February 2025

Bayesian Networks (BNs) are often used for designing diagnosis decision support systems. They are a well-established method for reasoning under uncertainty and making inferences. But, eliciting the probabilities can be tedious and time-consuming especially in medical domain where variables are often related by qualitative terms rather than probabilities. The goal of this paper is to propose a method for eliciting the probabilities required in BNs by using and transforming causal rules which are often used in medicine. The method consists in first constructing the structure of BNs by reporting medical expert's knowledge in the form of causal rules, and then constructing the parameters of the BNs by transforming the terms used for qualified causal rules into probabilities. Example is given in obesity domain. Further works are needed to reinforce our method like the consideration of circular causal rules.
Ready for HIT Innovations? Developing a Tool to Assess the Professionalism of Information Management in Hospitals
Authors: Liebe Jan-David  , Esdar Moritz  , Jahn Franziska  , Hübner Ursula
Published: February 2025

Current frameworks postulate the success of health IT innovations to be determined by the professionalism of the information management (PIM). Still, empirical knowledge about PIM is scarce up until today. This study seeks to answer three research questions: (1.) How can PIM be measured in a reliable and valid way, (2.) how pronounced is PIM in German hospitals and (3.) do hospital characteristics have an impact on the degree of PIM? Based on the results of an expert workshop and frameworks for information management (IM) items for a PIM inventory were developed and the inventory sent to 1349 chief information officers of German hospitals. A principle component analysis based on the responses of 196 hospitals confirmed the three components that had been proposed by the frameworks: the strategic, the tactical and the operational level. The full inventory implied satisfying reliability and allowed a PIM composite-score to be calculated. The PIM scores for strategic and tactical IM were found to be far lower than for operational IM which hints at strong deficits in these areas. A stepwise regression model indicated that the degree of PIM significantly increased with the size of the hospital, which had been expected and hints the validity of the PIM inventory. This tool offers potentials for hospitals to classify and improve their IM.
Learning Healthcare Systems: Scaling-Up Integrated Care Programs
Authors: Gaveikaite Violeta  , Filos Dimitris  , Schonenberg Helen  , van der Heijden Remco  , Maglaveras Nicos  , Chouvarda Ioanna
Published: February 2025

Current healthcare systems are struggling with rising costs and unbalanced quality of care. Integrated care (IC) is a worldwide trend in healthcare reforms designed to tackle these problems. ACT@Scale is a partnership of leading European regions, industry and academia which aims to identify, transfer and scale-up existing integrated healthcare practices. In this context, participating programs are applying iterative process improvement cycles using collaborative methodologies. The vision of learning health systems (LHS) is similar to IC, but it focuses on IT means as a change enabler for rapid and continuous knowledge integration into better outcomes. In this paper, we present the ACT@Scale program as an example of an LHS that monitors integrated care performance and effects of process improvement cycles.
The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching
Authors: Szekér Szabolcs  , Vathy-Fogarassy Ágnes
Published: February 2025

Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calculations, the quality of the control group is uncertain. In this paper, we present a statistics-based research in which we try to determine the relationship between the accuracy of the logistic regression model and the uncertainty of the dependent variable of the control group defined by propensity score matching. Our analyses show that there is a linear correlation between the fit of the logistic regression model and the uncertainty of the output variable. In certain cases, a latent binary explanatory variable can result in a relative error of up to 70% in the prediction of the outcome variable. The observed phenomenon calls the attention of analysts to an important point, which must be taken into account when deducting conclusions.