bluuebunny/crossref_metadata_2025 huggingface dataset:
Designing an Informatics Infrastructure for a National Aged Care Medication Roundtable
Authors: Westbrook Johanna I. , Seaman Karla , Wabe Nasir , Raban Magdalena Z. , Urwin Rachel , Badgery-Parker Tim , Mecardo Crisostomo , Mumford Virginia , Nguyen Amy D. , Root Jo , Balmer Sarah , Waugh Karen , Pinto Sonali , Burge Birgit , Aldeguer Eric , Dunstan Travis , Jorgensen Mikaela , Gray Len , Bucknall Tracey , Etherton-Beer Christopher , Newell Ben , Caughey Gillian , Beattie Elizabeth , Xenos Kristin , Cumming Anne
Published: January 2024

In the residential aged care sector medication management has been identified as a major area of concern contributing to poor outcomes and quality of life for residents. Monitoring medication management in residential aged care in Australia has been highly reliant on small, internal audits. The introduction of electronic medication administration systems provides new opportunities to establish improved methods for ongoing, timely and efficient monitoring of a range of medication indicators, made more meaningful by linking medication data with resident characteristics and outcomes. Benchmarking contemporary medication indicators provides a further opportunity for improvement and is most effective when indicator data are adjusted to take account of confounding factors, such as residents’ characteristics and health conditions. Roundtables provide a structure for sharing and discussing indicator data in a trusted and supportive environment and encourage the identification of strategies which may be effective in improving medication management. This paper describes a new project to establish, implement and evaluate a National Aged Care Medication Roundtable.
Utilising Mobile Health Apps – A Comparison of GP Perceptions Across Australia and Germany
Authors: Schroeder Tanja , Nguyen Amy D. , Seaman Karla , Gewald Heiko , Georgiou Andrew
Published: January 2024

Germany became the first country to accept certain mobile health (mHealth) apps for prescription with costs covered by statutory health insurance. Yet, this option has only been used to a limited extent. To develop an international comparison, this study investigates GPs’ perceptions of mHealth apps with a medical purpose in Germany and Australia. We conducted semi-structured interviews to examine their perspective on introducing and using mHealth apps and their awareness of their impact on patient adherence, empowerment, and health literacy. The results show that prescribing mHealth apps in general practice seems feasible in Australia and doctors are highly receptive to it.
Oncology Patient Portal: Understanding User’s Needs and Expectations
Authors: Colussi Giuliana , Descalzo Juan , Paoloni Agustín , Obregón Oscar , Cassarino Melanie , Jaca Pablo
Published: January 2024

The objective of this work was to explore the needs and expectations of the users of the Alexander Fleming Institute (IAF) regarding patient-oriented information systems, with the purpose of planning and prioritizing the implementation of improvements in the patient portal. The textual comments of patients who were treated at the institution between November 2021 and April 2022 and answered the satisfaction survey sent by IAF were analyzed using the content analysis technique. The results showed 6 main emerging issues: (1) the need for patients to have their clinical and administrative information in a single platform; (2) to have their clinical documentation available at the appropriate time; (3) to have clearer and more friendly information on preparation before diagnostic studies and (4) chemotherapy first time talks; (5) more accessible information on complementary treatments in the oncological context; (6) in-app appointment queue functionalities. The findings reported in this work served as an input to design an improvement plan for the patient portal and the institution’s digital ecosystem.
Post-Implementation Outcomes of a Remote Patient Monitoring Program After Emergency Department Discharge
Authors: Maurer Eric W. , Adam Terrence , Eberly Lynn E. , Alsharit Ahmed , Billecke Stephanie , Annis Tucker , Badlani Sameer , Pleasants Susan , Melton Genevieve B.
Published: January 2024

To better communicate and improve post-visit outcomes, a remote patient monitoring (RPM) program was implemented for patients discharged from emergency departments (ED) across 10 hospitals. The solution was offered to patients at the time of ED discharge and staffed by a group of care coordinators to respond to questions/urgent needs. Of 107,477 consecutive patients offered RPM, 28,425 patients (26.4%) engaged with the program. Activated patients with RPM were less likely to return to the ED within 90 days of their index visit [19.8% compared to 23.6%, p<.001]. While activation rates were modest, we observed fewer return visits to the ED in patients using RPM, with a 16.2% lower hazard of returning in the next year. Future research is needed to understand methods to improve RPM activation, any causal effects of RPM activation on return ED visits, and external validation of these findings.
NLP-Assisted Differential Diagnosis of Chronic Obstructive Pulmonary Disease Exacerbation
Authors: Shah-Mohammadi Fatemeh , Finkelstein Joseph
Published: January 2024

Chronic Obstructive Pulmonary Disease (COPD) frequently coincides with other comorbidities such as congestive heart failure, hypertension, coronary artery disease, or atrial fibrillation. The exhibition of overlapping sets of symptoms associated with these conditions prevents early identification of an acute exacerbation upon admission to a hospital. Early identification of the underlying cause of exacerbation allows timely prescription of an optimal treatment plan as well as allows avoiding unnecessary clinical tests and specialist consultations. The aim of this study was to develop a predictive model for early identification of COPD exacerbation by using the clinical notes generated within 24 hours of admission to the hospital. The study cohort included patients with a prior diagnosis of COPD. Four predictive models have been developed, among which the support vector machine showed the best performance based on the resulting 80% F1 score.
Automating the Identification of Safety Events Involving Machine Learning-Enabled Medical Devices
Authors: Wang Ying , Lyell David , Coiera Enrico , Magrabi Farah
Published: January 2024

With growing use of machine learning (ML)-enabled medical devices by clinicians and consumers safety events involving these systems are emerging. Current analysis of safety events heavily relies on retrospective review by experts, which is time consuming and cost ineffective. This study develops automated text classifiers and evaluates their potential to identify rare ML safety events from the US FDA’s MAUDE. Four stratified classifiers were evaluated using a real-world data distribution with different feature sets: report text; text and device brand name; text and generic device type; and all information combined. We found that stratified classifiers using the generic type of devices were the most effective technique when tested on both stratified (F1-score=85%) and external datasets (precision=100%). All true positives on the external dataset were consistently identified by the three stratified classifiers, indicating the ensemble results from them can be used directly to monitor ML events reported to MAUDE.
Detection of Medication Mentions and Medication Change Events in Clinical Notes Using Transformer-Based Models
Authors: Guo Yuting , Ge Yao , Sarker Abeed
Published: January 2024

In this paper, we address the related tasks of medication extraction, event classification, and context classification from clinical text. The data for the tasks were obtained from the National Natural Language Processing (NLP) Clinical Challenges (n2c2) Track 1. We developed a named entity recognition (NER) model based on BioClinicalBERT and applied a dictionary-based fuzzy matching mechanism to identify the medication mentions in clinical notes. We developed a unified model architecture for event classification and context classification. The model used two pre-trained models—BioClinicalBERT and RoBERTa to predict the class, separately. Additionally, we applied an ensemble mechanism to combine the predictions of BioClinicalBERT and RoBERTa. For event classification, our best model achieved 0.926 micro-averaged F1-score, 5% higher than the baseline model. The shared task released the data in different stages during the evaluation phase. Our system consistently ranked among the top 10 for Releases 1 and 2.
Automated Process Mining and Learning of Therapeutic Actions in the Intensive Care Unit
Authors: Romanov Anna , Shahar Yuval
Published: January 2024

In this study, we implemented a hybrid approach, incorporating temporal data mining, machine learning, and process mining for modeling and predicting the course of treatment of Intensive Care Unit (ICU) patients. We used process mining algorithms to construct models of management of ICU patients. Then, we extracted the decision points from the mined models and used temporal data mining of the periods preceding the decision points to create temporal-pattern features. We trained classifiers to predict the next actions expected for each point. The methodology was evaluated on medical ICU data from the hypokalemia and hypoglycemia domains. The study’s contributions include the representation of medical treatment trajectories of ICU patients using process models, and the integration of Temporal Data Mining and Machine Learning with Process Mining, to predict the next therapeutic actions in the ICU.
Multimodality Fusion Method Based on Multiview Subspace Clustering for Pulmonary Embolism Diagnosis
Authors: Hu Peijun , Qi Qianqian , Zhao Yanxia , Fu Miaomiao , Li Jingsong
Published: January 2024

Pulmonary embolism (PE) is an important clinical disorder that will result in lung tissue damage or low blood oxygen levels, which need early diagnosis and timely treatment. While computed tomographic pulmonary angiography (CTPA) is the gold standard to diagnose PE, previous studies have verified the effectiveness of combing CTPA and EMR data in computer-aided PE detection or diagnosis. In this paper, we proposed a multimodality fusion method based on multi-view subspace clustering guided feature selection (MSCUFS). The extracted high-dimensional image and EMR features are firstly selected and fused by the MSCUFS, and then are feed into different machine learning models with different fusion strategy to construct the PE classifier. The experiment results showed that the joint fusion strategy with MSCUFS achieved best AUROC of 0.947, surpassing other early fusion and late fusion models. The comparison between single modality and multimodality also illustrated the effectiveness of the proposed method.
Dual-Attention Model Fusing CNN and Transformer for Pancreas Segmentation
Authors: Zhu Yan , Hu Peijun , Tian Yu , Dong Kaiqi , Li Jingsong
Published: January 2024

Pancreatic cancer is a highly malignant cancer of the digestive tract and is rapidly progressing and spreading clinically. Automatic and accurate pancreatic tissue segmentation in abdominal CT images is essential for the early diagnosis of pancreatic-related diseases. It is challenging that the pancreas is small in size and complex in morphology. To address this problem, we propose a dual-attention model fusing CNN and Transformer to effectively activate pancreas-related features expression. The CNN structure weights the importance of pancreas-related features at the channel level and weakens the background information. Transformer feature aggregation module constructs spatial correlations among long-distance pixels from a global perspective. This study is validated on the NIH-TCIA dataset and achieved a mean Dice Similarity Coefficient of 85.82%, which is outperforming than the state-of-the-art methods. The visualization of surface distance also demonstrates the effective segmentation of pancreas boundary details by the proposed model.
Reshaping Wound Care: Evaluation of an Artificial Intelligence App to Improve Wound Assessment and Management
Authors: Barakat-Johnson Michelle , Jones Aaron , Burger Mitch , Leong Thomas , Frotjold Astrid , Randall Sue , Fethney Judith , Coyer Fiona
Published: January 2024

This study evaluated the usability and effectiveness of an artificial intelligence application for wound assessment and management from a clinician-and-patient perspective. A quasi-experimental design was conducted in four settings in an Australian health service. Data were collected from patients in the standard (n=166,243 wounds) and intervention (n=124,184 wounds) group, at baseline and post-intervention. Clinicians completed a survey (n=10) and focus group (n=13) and patients were interviewed (n=4). Wound documentation were analysed descriptively, bivariate statistics determined between-group differences, and interviews were thematically analysed. Compared with the standard group, wound documentation in the intervention group improved significantly (<2 items documented 24% vs 70%, P < .001). During the intervention, 101/132 wounds improved (mean wound size reduction=53.99%). Positive evaluations included instantaneous objective wound assessment, shared wound plans increased patient adherence and enhanced efficiency in providing virtual care. Application use facilitated remote patient monitoring and reduced patient travel time while maintaining optimal wound care.
Position Prior Attention Network for Pancreas Tumor Segmentation
Authors: Dong Kaiqi , Hu Peijun , Li Xiang , Tian Yu , Zhu Yan , Bai Xueli , Liang Tingbo , Li Jingsong
Published: January 2024

Segmentation of pancreatic tumors on CT images is essential for the diagnosis and treatment of pancreatic cancer. However, low contrast between the pancreas and the tumor, as well as variable tumor shape and position, makes segmentation challenging. To solve the problem, we propose a Position Prior Attention Network (PPANet) with a pseudo segmentation generation module (PSGM) and a position prior attention module (PPAM). PSGM and PPAM maps pancreatic and tumor pseudo segmentation to latent space to generate position prior attention map and supervises location classification. The proposed method is evaluated on pancreatic patient data collected from local hospital and the experimental results demonstrate that our method can significantly improve the tumor segmentation results by introducing the position information in the training phase.
Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research
Authors: Schuemie Martijn , Reps Jenna , Black Adam , Defalco Frank , Evans Lee , Fridgeirsson Egill , Gilbert James P. , Knoll Chris , Lavallee Martin , Rao Gowtham A. , Rijnbeek Peter , Sadowski Katy , Sena Anthony , Swerdel Joel , Williams Ross D. , Suchard Marc
Published: January 2024

The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It executes directly against healthcare data such as electronic health records and administrative claims, that have been converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Using advanced analytics, HADES performs characterization, population-level causal effect estimation, and patient-level prediction, potentially across a federated data network, allowing patient-level data to remain locally while only aggregated statistics are shared. Designed to run across a wide array of technical environments, including different operating systems and database platforms, HADES uses continuous integration with a large set of unit tests to maintain reliability. HADES implements OHDSI best practices, and is used in almost all published OHDSI studies, including some that have directly informed regulatory decisions.
Bioinformatics Architecture for Integrating Genomics Data into Electronic Health Records
Authors: Brunner Mauricio , Butti Matias , Menazzi Sebastián , Chanfreau Hernan , Tajerian Matias , Quiroga Alfonso , Otero Paula , Luna Daniel , Benitez Sonia
Published: January 2024

The adequate management of patients’ genomic information is essential for any health institution pursuing the Precision Medicine model. Here we approach a bioinformatic architecture that allows the Institution to store its whole genetic test data in a scalable database, and also the integration of that genetic data with the Electronic Health Record through a Clinical Decision Support System. The system complements patient care by suggesting referral to genetic counseling for patients who are potentially at risk of hereditary breast/ovarian cancer, and allowing for proper follow-up of patients with pathogenic variants in BRCA1 or BRCA2 genes. The implemented solution uses the FHIR standard and genetic nomenclatures from the Human Genome Variation Society and the HUGO Gene Nomenclature Committee. The architecture is flexible enough to allow any other health institution to integrate -to their information ecosystem- the whole solution or some of the modules according to its degree of digitization progress.
Initial User-Centred Design of an AI-Based Clinical Decision Support System for Primary Care
Authors: Neff Michaela Christina , Schaaf Jannik , Noll Richard , Holtz Svea , Schütze Dania , Köhler Susanne Maria , Müller Beate , Ahmadi Najia , von Wagner Michael , Storf Holger
Published: January 2024

A clinical decision support system based on different methods of artificial intelligence (AI) can support the diagnosis of patients with unclear diseases by providing tentative diagnoses as well as proposals for further steps. In a user-centred-design process, we aim to find out how general practitioners envision the user interface of an AI-based clinical decision support system for primary care. A first user-interface prototype was developed using the task model based on user requirements from preliminary work. Five general practitioners evaluated the prototype in two workshops. The discussion of the prototype resulted in categorized suggestions with key messages for further development of the AI-based clinical decision support system, such as the integration of intelligent parameter requests. The early inclusion of different user feedback facilitated the implementation of a user interface for a user-friendly decision support system.
Towards an Environmentally Robust Speech Assistant System for Emergency Medical Services
Authors: Zhang Zhenchuan , Tian Yu , Zhou Tianshu , Zhao Yinghao , Zhang Jungen , Li Jingsong
Published: January 2024

Automated speech recognition technology with robust performance in various environments is highly needed by emergency clinicians, but there are few successful cases. One main challenge is the wide variety of environmental interference involved during a typical prehospital care emergency service such as background noises and overlapping speech. To solve this problem, we try to establish an environmentally robust speech assistant system with the help of the proposed personalized speech enhancement (PSE) method, which utilizes the target physician’s voiceprint feature to suppress non-target signal components. We demonstrate its potential value using both general public test set and our real EMS test set by evaluating the objective speech quality metrics, DNSMOS, and the recognition accuracy. Hopefully, the proposed method will raise EMS efficiency and security against non-target speech.
Clinical Evaluation of a Lay-Language Radiology Glossary Integrated into an Electronic Health Record System
Authors: Kahn, Jr. Charles E.
Published: January 2024

Radiology reports typically contain complex, technical language that can pose a barrier to patients, especially to those with limited educational attainment or literacy skills. PORTER—the Patient-Oriented Radiology Reporter system—incorporates a lay-language glossary to help patients understand radiology report content. The system displays illustrations and definitions of radiology terms when viewing a report. A large U.S. health system integrated PORTER into the patient portal of its electronic health record (EHR) system and evaluated its use during a 7-month study period. Of 4,664 concepts defined in its glossary, 4,607 (98%) were readable at the 8th-grade level. Of 10,859 patients who completed a single-question survey, 6,947 (64%) found the system to be helpful or very helpful. A system that integrates illustrations and lay-language definitions of medical terms into a patient portal can help patients better understand the reports of their radiology examinations. Such a system has potential to improve communication for patients with limited health literacy.
Predictors of the Use of Physician’s Televisits During the COVID-19 Pandemic in Poland
Authors: Duplaga Mariusz
Published: January 2024

Telemedicine-based contacts between patients and physicians were allowed in Poland only in 2016. However, using such services was not common due to the lack of a relevant reimbursement scheme. During the COVID-19 pandemic, the use of telemedicine and e-health systems increased radically. The aim of this study was the assessment of factors predicting the use of physician’s televisit in the Polish general adult population. The analysis was based on data from an online survey of 2410 Internet users performed in October 2020. The logistic regression modeling revealed that both health (HL) and e-health literacy (eHL), the use of e-health services before the pandemic, the use of social media, and from sociodemographic variables, age, gender, vocational status, and marital status were significant predictors of obtaining televisit from a physician during the pandemic. Pre-pandemic experience with ehealth applications strongly predicted utilizing televisits during the pandemic. Respondents with higher levels of HL and eHL were more likely to use such telemedicine services than those with lower levels.
Exploration of the Voice of the Patient in Learning Health Systems: A Socio-Technical Perspective
Authors: Cassidy Sonja , Skeidsvoll Solvang Øivind , Solvoll Terje , Lintvedt Ove
Published: January 2024

Despite learning health systems’ focus on including the patients in improving healthcare services, research shows they are still considered participants, not partners. This article aims to provide practical guidance for recognizing and including the Voice of the Patient (VoP) as data in a continuous LHS by describing how the VoP can present itself, how it can be incorporated into the LHS and the barriers and enablers for doing so. Five key domains were identified to consider when including the patient perspective. The use of technology could be a facilitator for patients to provide their perspectives. However, there is a risk of increased health inequity by reducing the VoP of patients with low health or digital literacy.
Insights on the Future of Digital Health: An Analysis of Twitter Posts of IMIA Fellows
Authors: Marc David T. , Lalani Karima , Fenton Susan H. , Butler-Henderson Kerryn
Published: January 2024

The COVID-19 pandemic has reshaped technology-enhanced services in health and care organizations globally. As the world pivots towards a post-COVID-19 environment, it is essential to examine emerging trends amongst thought leaders in the health information technology sector. This study queried Twitter feeds of IMIA Fellows from 2013 through 2022, utilizing combinations of sentiment analysis, latent dirichlet allocation, and document analysis methods. The results provided a glimpse of positive sentiment year upon year, with the most negative sentiment prevalent in 2020, due to the onset of the pandemic. The findings from this study can be strategically used to analyze emerging trends in digital health, as well as to shape health IT thought leadership in the post-pandemic landscape.
Teledermatology: Simulating Hybrid Workflows for Telemedicine Education
Authors: Lesselroth Blake , Monkman Helen , Palmer Ryan , Liew Andrew , Kendrick Christina , Kollaja Liz , Ijams Shannon , Homco Juell , Soo Elizabeth , Foulks Kristen , Wen Frances
Published: January 2024

Given the importance of telemedicine in improving healthcare access for underserved patients, professional students need experience using virtual clinical workflows. We developed an educational workshop with (1) readings, (2) a knowledge assessment test, (3) dermatology and teledermatology lectures, (5) a telemedicine simulation with a standardized patient, and (6) a debriefing session. The simulation included a “hybrid” workflow with live videoconferencing and store-and-forward image review. We measured student performance using three American Association of Medical Colleges (AAMC) Telemedicine Competencies for medical education. Ninety-eight medical and physician assistant students completed this workshop between 2021 and 2022, and 80% were entrustable or approaching entrustment in each competency. Some students struggled with data collection and technology use. Our results suggest that this workshop offers a practical and generalizable way to teach about multiple virtual workflows and strengthen students’ telemedicine competencies.
Nursing Informatics: Competency Challenges for Nursing Faculty
Authors: Reid Lisa , Button Didy , Breaden Katrina , Brommeyer Mark
Published: January 2024

Nursing is the largest workforce in health care with nurses increasingly required to work with digital health technologies. However, despite the adoption of nursing informatics in Australia in the mid-1980s, nursing graduates are not being adequately equipped to use these technologies in a way that benefits the profession and improves patient care. Using a scoping review approach, this paper presents an analysis of contemporary published literature and describes the barriers to faculty engagement with digital health technologies in undergraduate nursing education. Thirty five articles were included and identified faculty lack of understanding of nursing informatics and resistance to technologies, limited infrastructure and expenditure, and limited educational resources and best practice recommendations as significant barriers to the integration of nursing informatics into undergraduate nursing curricula. Recommendations for faculty development will be explored.
Demonstrating Digital Health Clinical Competence in Practice: A Method for Developing Entrustable Professional Activities
Authors: Merolli Mark , Gray Kathleen
Published: January 2024

There is broad agreement regarding fundamental digital health competencies that clinical health professionals should possess. However, there is still no clear way to observe and assess their application, during the actual work practices undertaken in clinical settings. The present paper tackles this competency-practice gap. It describes a novel health workforce research process to evolve competency statements into entrustable professional activities (EPAs) for using digital health and informatics in a clinical profession. It illustrates the use of the EPA-DH-CP framework in the context of physiotherapy. This framework offers a systematic approach for any clinical profession to develop EPAs that set out their expectation of how practitioners will demonstrate relevant digital health competencies. It can inform pre-clinical education and continuing professional development in digital health, and implementation of changes in scopes and standards of digital health clinical practice. It can support interprofessional evaluation of digital health EPAs and other EPAs.
YouTube Video Analytics for Patient Education: An Exploratory Clustering of Obstructive Sleep Apnea Videos
Authors: Zhang Ruoyu , Shin Jennifer , Schulz Kristine , Liu Xiao , Susarla Anjana , Padman Rema
Published: January 2024

With the growing popularity of content-sharing platforms, patients are increasingly using the Internet as a critical source of health information. As one of the most popular video-sharing sites, YouTube provides easy access to health information seekers, but it is difficult and time-consuming to identify and retrieve high-quality videos that may serve as engaging patient education materials. This paper reports on an exploratory analysis of 317 YouTube videos on Obstructive Sleep Apnea (OSA) to better understand some key features of the videos and the relationships between them to facilitate subsequent video classification and recommendation. Features intrinsic to a video, such as video duration, and extrinsic, such as the number of views, are analyzed using unsupervised clustering methods and the Sankey diagram to discover the relationship between the clusters and their significance across different clusters, providing promising insights for the assessment of video quality.
Emergency Department Demand and the First Year of the COVID-19 Pandemic
Authors: Boyle Justin , Khanna Sankalp , Lind James
Published: January 2024

We present a retrospective analysis of Emergency Department daily patient flow across 84 hospitals in Queensland, Australia over a four-year period from 2017 – 2020, leading up to and including the start of the COVID-19 pandemic. Daily ED demand significantly increased year-on-year over the study period, though significant increases in 2020 were likely attributed to ED fever screening clinics. Compliance against a four-hour ED Length of Stay target had been slightly decreasing since 2017, and the first year of the pandemic showed significant improvements in target compliance compared to previous years for all patients including the cohort admitted from ED. The length of stay for ED patients was also significantly less in 2020 (mean = 3.1 hours) compared to previous years. As an area of topical interest, a special focus on influenza-like illness presentations to ED helps quantify changes in volume of this cohort. This knowledge assists hospitals in planning and responding to variations in hospital demand.
Evaluation of Smart Pump Interoperability with an Electronic Medical Record System to Improve Infusion Safety
Authors: Chin Karen , Donovan Jarrod , Bingham Gordon , Poole Susan , Tong Erica
Published: January 2024

Medications administered via intravenous (IV) infusions have high potential for patient harm. Evaluation of the rate of variances between the medication order on the Electronic Medical Record (EMR) and IV infusion details in the smart pump was performed pre and post- implementation of smart pump and EMR interoperability. Introduction of smart pumps with EMR interoperability resulted in a statistically significant reduction in frequency of variances.
Discontinuity of Stroke Care in a Black Urban Cohort: Insight from EHR Data
Authors: Faysel Mohammad A. , Kaufman David , Afable Aimee , Jumbo Adiebonye , Levine Steven
Published: January 2024

This poster describes the findings from a study that attempts to characterize an underserved Black stroke cohort who are at risk for discontinuities of care using electronic health record (EHR). Preliminary analysis revealed that 90.1% of the patients were Black. After being diagnosed with a stroke, 57% of the population did not return for follow-up. The objective is to understand the factors contributing to discontinuity of care in Black stroke patients.
Data-Exchange Between Electronic Medical Record and Viral-Load Laboratory Database Towards Improving HIV Care in Ethiopia
Authors: Petros Asaminew , Desalegn Daniel Melese , Dessie Getachew Fikadie , Mumme Bedri Ahmed , Abebe Kalechristos , Haile Dagim Melkie , Bogale Hiwot Berhanu , Mohammed Minen Sead , Tesfaye Yohannes , Tedla Yared , Gutta Gonfa Ayana , Bekedami Desalegn Lulu , Melaku Zenebe , Habte Dereje , Lulseged Sileshi
Published: January 2024

Electronic viral load (VL) Test Ordering and Result Reporting System (ETORRS) was introduced to create data exchange between the existing VL database and the electronic medical record (EMR) system, with the aim of reducing laboratory test results turnaround time (TAT), improving data quality, and supporting timely clinical response for patients with high VL. This use case is an illustrative example of initiating and adopting the principles of health information exchange for a priority health program.
Clinical Implementation of an AI Early Warning System Algorithm: Lessons Learned
Authors: Meehan Anne M. , Core Marcia A. , Ross Jared M. , Rahman Parvez A. , Borah Bijan J. , Caraballo Pedro J.
Published: January 2024

The Deterioration Index (DI) is an automatic early warning system that utilizes a machine learning algorithm integrated into the electronic health record and was implemented to improve risk stratification of inpatients. Our pilot implementation showed superior diagnostic accuracy than standard care. A score >60 had a specificity of 88.5% and a sensitivity of 59.8% (PPV 0.1758, NPP 0.9817). However, acceptance in the clinical workflow was divided; nurses preferred standard care, while providers found it helpful.
Enhancing Clinical Safety and Cancer Patient Experience Through Comprehensive eLearning on Central Venous Access Devices
Authors: McLean Lisa , Curtis Kerrie , Hodges Jacqueline , King Lisa , Wuellner Laura , Zheng Annie , Rushton Shelley , O’Brien Tracey
Published: January 2024

Health practitioners often insert and maintain central venous access devices (CVADs) as part of cancer care. One in four CVADs prematurely fail, which is associated with increased mortality, morbidity and a negative impact on quality of life. To support implementation of updated guidelines, eviQ Education developed a comprehensive, peer-reviewed, evidence-based CVADs eLearning package. An evaluation indicated that the eLearning supported clinicians’ practice and increased knowledge and clinical competency in CVAD insertion and management.
Implementing Tap-to-Witness Technology in the Electronic Medical Record
Authors: Jedwab Rebecca Miriam , Pham Anthony , Gogler Janette , Garduce James-Norbert , Foster Joanne , Brook Rebecca , Dobroff Naomi
Published: January 2024

The implementation of an organisation-wide EMR system in 2019 included single sign-on technology for nurses and midwives. This first-in-Australia study extended the use of this technology to enable nurses and midwives to tap-to-witness for high-risk medications, blood and blood products, and expressed breast milk. A saving of 7 seconds per interaction was observed with nurses and midwives reporting appreciation for ongoing EMR enhancement to reduce EMR-related documentation burden.
Evaluation of a Machine Learning-Based Decision Support Intervention for Inpatient Falls
Authors: Cho Insook , Kim MiSoon , Song Mi Ra
Published: January 2024

Inpatient falls are frequent adverse events, with various injuries occurring in one-third of falls. International practice guidelines recommend multifaceted risk assessment and risk-targeted interventions through multifactorial activities. However, the effectiveness is mixed for such recommendations implemented using traditional approaches. This study proposed a well-designed systemic and clinical decision support approach using machine learning techniques to leverage the implementation of preventive activities of nursing processes leading to outcome changes.
Expanding Technology-Enabled Nurse-Delivered Chronic Disease Care: EXTEND
Authors: Shaw Ryan J. , Crowley Matthew J.
Published: January 2024

Mobile monitoring-enabled technologies could enhance telehealth for chronic illness care. EXTEND is an active comparator randomized trial (N=220) of two 24-month interventions: 1) mobile monitoring as a self-management tool (EXTEND); and 2) a 12-month nurse and pharmacist-delivered telehealth intervention incorporating mobile monitoring, self-management support, and medication management that is followed by a 12-month self-management period (EXTEND Plus). EXTEND Plus is a pragmatic approach to integrating mobile monitoring-enabled telehealth for patients uncontrolled diabetes and hypertension into existing clinical infrastructure.
Web-Based Intervention for Multilingual Family Carers of People with Dementia: Insights from the DrawCare Study
Authors: Ulapane Nalika , Wickramasinghe Nilmini , Dang Thu Ha , Thodis Antonia , Brijnath Bianca
Published: January 2024

Addressing the needs of ethnically diverse multilingual people can be challenging in environments that are non-native to them. The consequences of this issue become more significant in healthcare contexts. Insights from the DrawCare study—an Australian study that explores the effectiveness of a web-based intervention for multilingual family carers of people with dementia—are presented illustrating the enabling role of digital health.
Evaluation of Machine Translation Accuracy Focused on the Adverse Event Terminology for Medical Devices
Authors: Yagahara Ayako , Uesugi Masahito , Yokoi Hideto
Published: January 2024

The purpose of this study was to evaluate the accuracy of deep neural machine translation focused on medical device adverse event terminology. 10 models were obtained, and their English-to-Japanese translation accuracy was evaluated using quantitative and qualitative measures. No significant difference was found in the quantitative index except for a few pairs. In the qualitative evaluation, there was a significant difference and googletrans and GPT-3 were regarded as useful models.
Designing a Digital Health Solution: A Platform for Automated Surveillance of Fungal Infection
Authors: Khanina Anna , Rozova Vlada , Elkins Sri , Verspoor Karin , Thursky Karin
Published: January 2024

Surveillance of invasive fungal infection (IFI) requires laborious review of multiple sources of clinical information, while applying complex criteria to effectively identify relevant infections. These processes can be automated using artificial intelligence (AI) methodologies, including applying natural language processing (NLP) to clinical reports. However, developing a practically useful automated IFI surveillance tool requires consideration of the implementation context. We employed the Design Thinking Framework (DTF) to focus on the needs of end users of the tool to ensure sustained user engagement and enable its prospective validation. DTF allowed iterative generation of ideas and refinement of the final digital health solution. We believe this approach is key to increasing the likelihood that the solution will be implemented in clinical practice.
Annotation of Opioid Use Disorder Entity Modifiers in Clinical Text
Authors: Almudaifer Abdullateef I. , Feldman Sue S. , O’Leary Tobias , Covington Whitney L. , Hairston JaMor , Deitch Zachary , Crisan Estera , Riggs Kevin , Walters Lauren , Osborne John D.
Published: January 2024

Natural Language Processing can be used to identify opioid use disorder in patients from clinical text1. We annotate a corpus of clinical text for mentions of concepts associated with unhealthy use of opiates including concept modifiers such as negation, subject, uncertainty, relation to document time and illicit use.
Structured Data Retrieval and Analysis of HL7 v2 Messages with Elasticsearch
Authors: Cheng Ka Yung , Pazmino Santiago , Qian Hao , Bergh Björn , Schreiweis Björn
Published: January 2024

The era of the electronic health record (EHR) requires lots of semantic interoperability for data sharing and reusability. We select HL7 v2 messages as the most common structured data type in hospital information systems, to investigate the plausibility of using Elasticsearch (ES) as a healthcare search engine and data analytics tool. Due to the facts, Elasticsearch can be integrated as a powerful searchable database for practical healthcare applications, to analyze structured healthcare data from various locations. It allows easy and efficient searching for complex query tasks.
Effect of Push-Pull HEPA Filters on Air Age in a Dental Treatment Room
Authors: Nambu Eriko , Nozaki Kazunori , Kokomoto Kazuma , Hayashi Mikako
Published: January 2024

Air quality was evaluated by visualizing with CFD (Computational Fluid Dynamics) where air tends to stagnate in the dental practice space when natural ventilation and HEPA filters are used together. The results showed that natural ventilation by opening and closing windows and doors alone was not sufficient.
Design and Application of Multi-Center Clinical Research Platform for Phenotyping of Voriconazole Hepatotoxicity
Authors: Zhang Ying , Wang Yuqing , Chi Shengqiang , Ru Hua , Jiang Yifan , Tian Yu , Zhou Tianshu , Li Jingsong
Published: January 2024

We introduce a phenotyping pipeline for voriconazole hepatotoxicity based on a multi-center clinical research platform. Using the platform’s queue construction, feature generation, and feature screening functions, 52 features were obtained for model training. The prediction model of voriconazole hepatotoxicity was obtained by using the model training and evaluation functions of the platform. Important risk factors and protection factors of the model were listed.
Data Derived Disease Diagnostics
Authors: Butler Gráinne , Shanks Josiah , Buttery Jim , Quinlan Cathy
Published: January 2024

Microscopic haematuria is a common incidental finding in childhood which often resolves on its own. However, it can be an early marker of genetic kidney disease. It is best practice to repeat testing to ensure resolution. Using data from the electronic medical record, we set out to find children without follow up, offer testing, and look for genetic kidney disease.
Substance Use Telemedicine Retention and Outcomes
Authors: Maxwell Justine F. , Li Li , Feldman Sue S.
Published: January 2024

Evidence-based SUD treatments are available, yet utilization remains low demonstrating the need to utilize alternative treatment delivery methods. We assess the effectiveness of an SUD treatment program delivered via telemedicine and in-person mode over 12 months. 40% of the intervention group remained engaged and 46% remained opioid free.
Tathwîr Qamûs al-Jayb li Thullâb bi Madrasah al-Ibtidâiyyah
Authors: Triyana Arum Agus , Erlina Erlina , Koderi Koderi , Anis Indah Khoirul , Nurudin M
Published: January 2024

This study aims to produce a product in the form of an Arabic-Indonesian dictionary. The role of the media is very important in learning, namely to increase student motivation, arouse enthusiasm for learning and can liven up an effective learning atmosphere. Then researchers are interested in compiling a pocket dictionary, with the hope that the existence of this Arabic-Indonesian pocket dictionary product allows students to study independently and can help them to more easily understand the material. The formulation of the problem in this study is how is the preparation of the Arabic-Indonesian pocket dictionary as a supporter of the Arabic-Madrasah Ibtidaiyah book. Furthermore, this research is a research and development developed by Reiser and Mollenda in 1990 or better known as the ADDIE model with the stages, namely analysis, design, development, implementation and evaluation. The data collection instrument used was a questionnaire. While the technical analysis of the data used in the form of material expert validation sheets, design expert validation sheets, teacher and student response questionnaires.The results of this study are proven by the value of the validation results by material experts, namely 89% and the validation results by learning design experts, namely 86%. The value of the educators is 86% and the average value of the students' pre-test is 54.16 and the post-test score is 84.16. So it was concluded that the preparation of the Arabic-Indonesian pocket dictionary as a support for the Arabic book of Madrasah Ibtidaiyah was very feasible to use.
Tanfîdz Al-Mubârazah at-Tamtsîliyyah li al-Fariq li Ta’lîm Mâddah an-Nahwiy
Authors: Ramli Lalu Abdul Aziz , Muassomah Muassomah , Anwar M Syahrul , Norazizah Siti , Siregar Amy Fitriani
Published: January 2024

The progress of education in a country is influenced by the quality of teachers and needs to be more creative and innovative in choosing appropriate teaching methods so that the quality of student learning outcomes can be improved. The purpose of this study was to determine the quality of the implementation of student learning outcomes using the cooperative learning team games tournament method and its advantages and disadvantages. This study used a qualitative descriptive analysis method, with data collection techniques using observation and interviews. The results of the analysis carried out by the researcher after the implementation of the team representative duels, there is an ideal increase in the quality of educational outcomes, which proves that this model has a positive effect on student education, and can be observed in terms of participation, interaction, discussion and competition. In general, students like to apply this model because they considered was useful and fun. It was concluded that team games tournament is a model that provides opportunities for students to be more active, answer questions, share opinions and increase student activity in teaching so that the quality of learning outcomes can increase.
Study on chemical constituents of the ethyl acetate extract of Elephantopus mollis
Authors:   University of Science, Vietnam National University Ho Chi Minh City, Vietnam, Nguyen Loc Le , Thi Phuong Thao Dang ,   University of Science, Vietnam National University Ho Chi Minh City, Vietnam, Thi Y Nhi Nguyen ,   University of Science, Vietnam National University Ho Chi Minh City, Vietnam, Le Quan Tran ,   University of Science, Vietnam National University Ho Chi Minh City, Vietnam
Published: January 2024

Elephantopus mollis, family Asteraceae, is a herbaceous perennial plant with the main component of sesquiterpene lactones. In the Elephantopus mollis, there are other chemical components such as triterpenoids, steroids, flavonoids, and simple phenolic compounds. Since this plant was discovered and studied until now, there have been many studies on the biological activity of Elephantopus mollis, in which the cytotoxic activity was noted to be superior to many cell lines such as breast cancer cell lines (MCF-7; T-47D), lung cancer cell lines (A549; NCI-H2), leukemia cell line (HL-60), lymphoma cell line (RAJI)… The cytotoxic studies were performed mainly on petroleum ether, ethyl acetate, methanol, and water extracts. In Vietnam, Elephantopus mollis distributed mainly in the central highlands and are used in the traditional medicine to treat toxic boils, skin diseases, acute lymphadenitis, acute jaundice… In this paper, six compounds were isolated from an ethyl acetate extract of the whole plant, including two known sesquiterpene lactones, molephantinin (1), 2-deethoxy-2β-methoxyphantomolin (2) and four other compounds including a simple phenolic, methyl 4-hydroxybenzoate (3); a triterpene, epifridelanol (4), a phytosterol, stigmasterol (5), and a biflavonoid, lophirone L (6). Among them, compound 6 was found for the first time in Elephantopus mollis species. The chemical structure of the isolated compounds were elucidated by spectroscopic analysis 1H- NMR, 13C-NMR, HSQC, HMBC and HR-ESI-MS, combining with the comparison of their NMR data with those published in the literature.