bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the Safety of a New Clinical Decision Support System for a National Helpline
Authors: Luckraj Nirvana , Strazzari Renee , Coiera Enrico , Magrabi Farah
Published: January 2024

We assessed the safety of a new clinical decision support system (CDSS) for nurses on Australia’s national consumer helpline. Accuracy and safety of triage advice was assessed by testing the CDSS using 78 standardised patient vignettes (48 published and 30 proprietary). Testing was undertaken in two cycles using the CDSS vendor’s online evaluation tool (Cycle 1: 47 vignettes; Cycle 2: 41 vignettes). Safety equivalence was examined by testing the existing CDSS with the 47 vignettes from Cycle 1. The new CDSS triaged 66% of vignettes correctly compared to 57% by the existing CDSS. 15% of vignettes were overtriaged by the new CDSS compared to 28% by the existing CDSS. 19% of vignettes were undertriaged by the new CDSS compared to 15% by the existing CDSS. Overall performance of the new CDSS appears consistent and comparable with current studies. The new CDSS is at least as safe as the old CDSS.
Exploring a Mechanism Toward Automated Feedback for Cancer Patient Self-Reporting
Authors: Phong Kevin , Ding Mengchen , Cho Youmin , Jiang Yun , Gong Yang
Published: January 2024

“Infobuttons” spearheaded electronic health records (EHR) based decision support by offering automated knowledge resources to physicians. However, how such a mechanism could be leveraged to provide optimal resources to patients remains unanswered. Informatics approaches are expected to utilize more relevant information beyond EHR, such as patient-reported outcomes, to support clinical decisions. This pilot study is intended to explore how patient-reported outcomes version of the common terminology criteria for adverse events (PRO-CTCAE) in EHR can be incorporated and how to recommend tailored content to cancer patients via automated feedback.
Preliminary Results of a Grounded Theory Study on Using Mobile Health for Physical Activity
Authors: Wang Rui , Honey Michelle , Day Karen
Published: January 2024

As the first stage of substantive theory building, this study explored the behavioral responses of people with long-term weight concerns using mHealth to increase their physical activity within a New Zealand context. A constructivist grounded theory method was adopted. Twenty-two participants with long-term weight concerns and personal experience using mobile health to increase physical activity participated in in-depth interviews. Four themes and eight categories were conceptualized: motivation, physical activity behavioral responses, mobile health evaluation, and social interaction. The role of mobile health in increasing physical activity and improving overall wellness is broadly acknowledged and facilitates, to some extent, the social interactions among family, friends and the wider community.
Automatic Extraction of Skin and Soft Tissue Infection Status from Clinical Notes
Authors: Rhoads Jamie L.W. , Christensen Lee , Westerdahl Skylar , Stevens Vanessa , Chapman Wendy W. , Conway Mike
Published: January 2024

The reliable identification of skin and soft tissue infections (SSTIs) from electronic health records is important for a number of applications, including quality improvement, clinical guideline construction, and epidemiological analysis. However, in the United States, types of SSTIs (e.g. is the infection purulent or non-purulent?) are not captured reliably in structured clinical data. With this work, we trained and evaluated a rule-based clinical natural language processing system using 6,576 manually annotated clinical notes derived from the United States Veterans Health Administration (VA) with the goal of automatically extracting and classifying SSTI subtypes from clinical notes. The trained system achieved mention- and document-level performance metrics of the range 0.39 to 0.80 for mention level classification and 0.49 to 0.98 for document level classification.
Advanced Care Planning Content Encoding with Natural Language Processing
Authors: Knoll Benjamin C. , Gunderson Melissa , Rajamani Geetanjali , Wick Elizabeth C. , Colley Alexis , Lindemann Elizabeth , Rizvi Rubina , Diethelm Molly , Hultman Gretchen , Pierce Logan , Zhang Rui , Melton Genevieve B.
Published: January 2024

While advanced care planning (ACP) is an essential practice for ensuring patient-centered care, its adoption remains poor and the completeness of its documentation variable. Natural language processing (NLP) approaches hold promise for supporting ACP, including its use for decision support to improve ACP gaps at the point of care. ACP themes were annotated on palliative care notes across four annotators (Fleiss kappa = 0.753) and supervised models trained (Huggingface models bert-base-uncased and Bio_ClinicalBERT) using 5-fold cross validation (F1=0.8, precision=0.75, recall=0.86, any theme). When applied across the full note corpus of 12,711 notes, we observed variability in documentation of ACP information. Our findings demonstrate the promise of NLP approaches for informatics-based approaches for ACP and patient-centered care.
Rule-Based Text Classification of Dental Diagnosis
Authors: Wang Mei , Agrawal Anushka , Rogers Nicole , John Vanchit , Thyvalikakath Thankam
Published: January 2024

Unstructured medical records boast an abundance of information that could greatly facilitate medical decision-making and improve patient care. With the development of Natural Language Processing (NLP) methodology, the free-text medical data starts to attract more and more research attention. Most existing studies try to leverage the power of such unstructured data using Machine Learning algorithms, which would usually require a relatively large training set, and high computational capacity. However, when faced with a smaller-scale project, opting for an alternative approach may be more effective and practical. This project proposes an efficient and light-weight rule-based approach to categorize dental diagnosis data. It not only fills the void of dental records in the medical free-text processing area, but also demonstrates that with expertly designed research structure and proper implementation, simple method could achieve our study goal very competently.
Development of an ASR System for Medical Conversations
Authors: Renato Alejandro , Luna Daniel , Benítez Sonia
Published: January 2024

In this work we document the development of an ASR system for the transcription of conversations between patient and doctor and we will point out the critical aspects of the domain. The system was trained with an acoustic base of spontaneous speech that has a domain language model and a supervised phonetic dictionary. Its performance was compared with two systems: a) NeMo End-to-End Conformers in Spanish and b) Google API ASR (Automatic Speech Recognition) Cloud. The evaluation was carried out on a set of 208 teleconsultations recorded during the year 2020. The WER (Word Error Rate) was evaluated in ASR, and Recall and F1 for recognized medical entities. In conclusion, the developed system performed better, reaching 72.5% accuracy in the domain of teleconsultations and an F1 for entity recognition of 0.80.
Influence of Context in Transformer-Based Medication Relation Extraction
Authors: Modersohn Luise , Hahn Udo
Published: January 2024

The extraction of medication information from unstructured clinical documents has been a major application of clinical NLP in the past decade as evidenced by the conduct of two shared tasks under the I2B2 and N2C2 umbrella. We here propose a new methodological approach which has already shown a tremendous potential for increasing system performance for general NLP tasks, but has so far not been applied to medication extraction from EHR data, namely deep learning based on transformer models. We ran experiments on established clinical data sets for English (exploiting I2B2 and N2C2 corpora) and German (based on the 3000PA corpus, a German reference data set). Our results reveal that transformer models are on a par with current state-of-the-art results for English, but yield new ones for German data. We further address the influence of context on the overall performance of transformer-based medication relation extraction.
COVID-19 Event Extraction from Twitter via Extractive Question Answering with Continuous Prompts
Authors: Jiang Yuhang , Kavuluru Ramakanth
Published: January 2024

As COVID-19 ravages the world, social media analytics could augment traditional surveys in assessing how the pandemic evolves and capturing consumer chatter that could help healthcare agencies in addressing it. This typically involves mining disclosure events that mention testing positive for the disease or discussions surrounding perceptions and beliefs in preventative or treatment options. The 2020 shared task on COVID-19 event extraction (conducted as part of the W-NUT workshop during the EMNLP conference) introduced a new Twitter dataset for benchmarking event extraction from COVID-19 tweets. In this paper, we cast the problem of event extraction as extractive question answering using recent advances in continuous prompting in language models. On the shared task test dataset, our approach leads to over 5% absolute micro-averaged F1-score improvement over prior best results, across all COVID-19 event slots. Our ablation study shows that continuous prompts have a major impact on the eventual performance.
Data Augmentation with Nearest Neighbor Classifier for Few-Shot Named Entity Recognition
Authors: Ge Yao , Al-Garadi Mohammed Ali , Sarker Abeed
Published: January 2024

Few-shot learning (FSL) is a category of machine learning models that are designed with the intent of solving problems that have small amounts of labeled data available for training. FSL research progress in natural language processing (NLP), particularly within the medical domain, has been notably slow, primarily due to greater difficulties posed by domain-specific characteristics and data sparsity problems. We explored the use of novel methods for text representation and encoding combined with distance-based measures for improving FSL entity detection. In this paper, we propose a data augmentation method to incorporate semantic information from medical texts into the learning process and combine it with a nearest-neighbor classification strategy for predicting entities. Experiments performed on five biomedical text datasets demonstrate that our proposed approach often outperforms other approaches.
Graph Representation Learning-Based Fixed-Length Clinical Feature Vector Generation from Heterogeneous Medical Records
Authors: Seki Tomohisa , Kawazoe Yoshimasa , Ohe Kazuhiko
Published: January 2024

Transformation of patient data extracted from a database into fixed-length numerical vectors requires expertise in topical medical knowledge as well as data manipulation—thus, manual feature design is labor-intensive. In this study, we propose a machine learning-based method to for this purpose applicable to electronic medical data recorded during hospitalization, which utilizes unsupervised feature extraction based on graph embedding. Unsupervised learning is performed on a heterogeneous graph using Graph2Vec, and the inclusion of clinically useful data in the obtained embedding representation is evaluated by predicting readmission within 30 days of discharge based on it. The embedded representations are observed to improve predictive performance significantly as the information contained in the graph increases, indicating the suitability of the proposed method for feature design corresponding to clinical information.
Transfer Learning for Mortality Prediction in Non-Small Cell Lung Cancer with Low-Resolution Histopathology Slide Snapshots
Authors: Clark Matthew , Meyer Christopher , Ramos-Cejudo Jaime , Elbers Danne C. , Pierce-Murray Karen , Fricks Rafael , Alterovitz Gil , Rao Luigi , Brophy Mary T. , Do Nhan V. , Grossman Robert L. , Fillmore Nathanael R.
Published: January 2024

High-resolution whole slide image scans of histopathology slides have been widely used in recent years for prediction in cancer. However, in some cases, clinical informatics practitioners may only have access to low-resolution snapshots of histopathology slides, not high-resolution scans. We evaluated strategies for training neural network prognostic models in non-small cell lung cancer (NSCLC) based on low-resolution snapshots, using data from the Veterans Affairs Precision Oncology Data Repository. We compared strategies without transfer learning, with transfer learning from general domain images, and with transfer learning from publicly available high-resolution histopathology scans. We found transfer learning from high-resolution scans achieved significantly better performance than other strategies. Our contribution provides a foundation for future development of prognostic models in NSCLC that incorporate data from low-resolution pathology slide snapshots alongside known clinical predictors.
Say Goodbye to the ‘Paper on Screen’, Rethinking Presentation of and Interaction with Medical Information
Authors: Rausch Dennis , Kwade Zuzanna , Dahlweid Michael , Kozinova Irina , Nathoo Shainal , Yasini Mobin
Published: January 2024

Traditionally, Electronic Medical Records (EMR) have been designed to mimic paper records. Organizing and presenting medical information along the lines that evolved for non-digital records over the decades, reduced change management for medical users, but failed to make use of the potential of organizing digital data. We proposed a method to create clinical dashboards to increase the usability of information in the medical records. Official clinical guidelines were studied by a working group, including dashboard target users. Necessary clinical concepts contained in the medical records were identified according to the clinical context and finally, dedicated technical tools with standard terminologies were used to represent categories of information. We used this method to generate and implement a dashboard for sepsis. The dashboard was found to be appropriate and easy to use by the target users.
Graphical Presentations in Systemic Anticancer Treatment Network Meta-Analyses: A Systematic Review
Authors: Jain Sandeep , Li Xuanyi , Warner Jeremy
Published: January 2024

Network meta-analysis (NMA) draws conclusions about indirect comparisons of randomized clinical trials and is considered high-level evidence. Most NMA publications make use of network plots to portray results. Network plots are complex graphics that can have many visual attributes to portray useful information, such as node size, color, and graph layout. We analyzed the network plots from 162 NMAs of systemic anticancer therapy efficacy using a set of 16 attributes. Our review showed that the current state of network plot data visualizations within the NMA space lacks diversity and does not make use of many of the visual attributes available to convey information. More thoughtful design choices should be placed behind these important visualizations, which can carry clinical significance and help derive treatment plans for patients.
Modelling Planned vs. Actual Start Time to Control the Efficiency of Surgery
Authors: Boyle Justin , Hassanzadeh Hamed , Khanna Sankalp , Biki Barbara , Syed Faraz , Borkwood Ellen , Sweeney Lianne
Published: January 2024

To control the efficiency of surgery, it is ideal to have actual starting times of surgical procedures coincide with their planned start time. This study analysed over 4 years of data from a large metropolitan hospital and identified factors associated with surgery commencing close to the planned starting time via statistical modelling. A web application comprising novel visualisations to complement the statistical analysis was developed to facilitate translational impact by providing theatre administrators and clinical staff with a tool to assist with continuous quality improvement.
Defining the Boundaries of Psychiatric and Medical Knowledge: Applying Cartographic Principles to Self-Organising Maps
Authors: Amos Andrew , Lee Kyungmi , Gupta Tarun Sen , Malau-Aduli Bunmi
Published: January 2024

Biases in selection, training, and continuing professional development of medical specialists arise in part from reliance upon expert judgement for the design, implementation, and management of medical education. Reducing bias in curriculum development has primarily relied upon consensus processes modelled on the Delphi technique. The application of machine learning algorithms to databases indexing peer-reviewed medical literature can extract objective evidence about the novelty, relevance, and relative importance of different areas of medical knowledge. This study reports the construction of a map of medical knowledge based on the entire corpus of the MEDLINE database indexing more than 30 million articles published in medical journals since the 19th century. Techniques used in cartography to maximise the visually intelligible differentiation between regions are applied to knowledge clusters identified by a self-organising map to show the structure of published psychiatric evidence and its relationship to non-psychiatric medical domains.
Visualising Variation in the Real-World Clinical Delivery of Chemotherapy Protocols
Authors: Kennedy Georgina , Stevens Meg , Churches Timothy
Published: January 2024

Typical univariate measures of variation in chemotherapy protocols fail to capture and describe the full multi-dimensional complexity of treatment adjustments in real-world data. In this preliminary work, we propose novel visualisations of observed treatment events, as well as treatment-as-delivered relative to initial prescriptions, as a means of gaining insights into complex patterns of treatment variation in cancer patients. Simple clustering techniques were also used to confirm the utility of these visualisations and our ability to correlate observed variations with historical events.
Assessing Internet Search Models in Predicting Daily New COVID-19 Cases and Deaths in South Korea
Authors: Husnayain Atina , Su Emily Chia-Yu
Published: January 2024

Search data were found to be useful variables for COVID-19 trend prediction. In this study, we aimed to investigate the performance of online search models in state space models (SSMs), linear regression (LR) models, and generalized linear models (GLMs) for South Korean data from January 20, 2020, to July 31, 2021. Principal component analysis (PCA) was run to construct the composite features which were later used in model development. Values of root mean squared error (RMSE), peak day error (PDE), and peak magnitude error (PME) were defined as loss functions. Results showed that integrating search data in the models for short- and long-term prediction resulted in a low level of RMSE values, particularly for SSMs. Findings indicated that type of model used highly impacts the performance of prediction and interpretability of the model. Furthermore, PDE and PME could be beneficial to be included in the evaluation of peaks.
Predicting Urgent Dialysis at Ambulance Transport to the Emergency Department Using Machine Learning Methods
Authors: Majouni Sheida , Tennankore Karthik , Abidi Syed Sibte Raza
Published: January 2024

Hemodialysis patients frequently require ambulance transport to the hospital for dialysis. Some patients require urgent dialysis (UD) within 24 hours of transport to hospital to avoid morbidity and mortality. UD is not available in all hospitals; therefore, predicting patients who need UD prior to hospital transport can help paramedics with destination planning. In this paper, we developed machine learning models for paramedics to predict whether a patient needs UD based on patient characteristics available at the time of ambulance transport. This paper presented a study based on ambulance data collected in Halifax, Canada. Given that relatively few patients need UD, a class imbalance problem is addressed by up-sampling methods and prediction models are developed using multiple machine learning methods. The achieved prediction scores are F1-score=0.76, sensitivity=0.76, and specificity=0.97, confirming that models can predict UD with limited patient characteristics.
Development and Usability Evaluation of an Opioid Management App
Authors: Deshpande Sagar , Gunderson Melissa , Rajamani Geetanjali , Park Suhyun , Diethelm Molly , Marquard Jenna , Melton Genevieve , Rizvi Rubina
Published: January 2024

We describe the development and usability evaluation of a novel patient engagement tool (OPY) in its early stage from perspectives of both experts and end-users. The tool is aimed at engaging patients in positive behaviors surrounding the use, weaning, and disposal of opioid medications in the post-surgical setting. The messaging and design of the application were created through a behavioral economics lens. Expert-based heuristic analysis and user testing were conducted and demonstrated that while patients found the tool to be easy to use and subjectively somewhat useful, additional work to enhance the user interface and features is needed in close partnership with developers and stakeholders.
Machine-Learning Based Risk Assessment for Cancer Therapy-Related Cardiac Adverse Events Among Breast Cancer Patients
Authors: Nguyen Quynh T.N. , Phan Phuc T. , Lin Shwu-Jiuan , Hsu Min-Huei , Li Yu-Chuan (Jack) , Hsu Jason C. , Nguyen Phung-Anh
Published: January 2024

The study aims to develop machine-learning models to predict cardiac adverse events in female breast cancer patients who receive adjuvant therapy. We selected breast cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2004 and December 2020. Patients were monitored at the date of prescribed chemo- and/or -target therapies until cardiac adverse events occurred during a year. Variables were used, including demographics, comorbidities, medications, and lab values. Logistics regression (LR) and artificial neural network (ANN) were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 1321 patients (an equal 15039 visits) were included. The best performance of the artificial neural network (ANN) model was achieved with the AUC, precision, recall, and F1-score of 0.89, 0.14, 0.82, and 0.2, respectively. The most important features were a pre-existing cardiac disease, tumor size, estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), cancer stage, and age at index date. Further research is necessary to determine the feasibility of applying the algorithm in the clinical setting and explore whether this tool could improve care and outcomes.
Ensemble Clustering to Generate Phenotypes of Kidney Transplant Donors and Recipients
Authors: Abidi Syed Sibte Raza , Jalakam Kranthi , Abidi Syed Hani Raza , Tennankore Karthik
Published: January 2024

In this paper we investigate the generation of phenotypes for kidney transplant donors and recipients to assist with decision making around organ allocation. We present an ensemble clustering approach for multi-type data (numerical and categorical) using two different clustering approaches—i.e., model based and vector quantization based clustering. These clustering approaches were applied to a large, US national deceased donor kidney transplant recipient database to characterize members of each cluster (in an unsupervised fashion) and to determine whether the subsequent risk of graft failure differed for each cluster. We generated three distinct clusters of recipients, which were subsequently used to generate phenotypes. Each cluster phenotype had recipients with varying clinical features, and the risk of kidney transplant graft failure and mortality differed across clusters. Importantly, the clustering results by both approaches demonstrated a significant overlap. Utilization of two distinct clustering approaches may be a novel way to validate unsupervised clustering techniques and clustering can be used for organ allocation decision making on the basis of differential outcomes.
Interpreting Laboratory Results with Complementary Health Information: A Human Factors Perspective
Authors: Joseph Amanda L. , Monkman Helen , MacDonald Leah , Lai Claudia
Published: January 2024

The desire to access personal and high-quality health information electronically is increasing, not only in Canada, but globally. With the advent of the COVID - 19 pandemic the desire and demand for telemedicine and timely access to personal health data such as online laboratory (lab) results has increased substantially. This study examines citizens’ perspectives of being provided with high-quality information about a specific lab test (i.e., potassium) in the same display as a trend graph. Therefore, the objective of this study is to test how participants managed this additional information about the context of the test, understood, and applied it. The researchers analyzed the responses of semi-structured interviews with Canadian participants (N=24) using conventional content analysis. This paper examined four themes related to providing complementary information concurrently with lab results in the same display: 1) Benefits of Collocated Information, 2) Information Overload, 3) Misinterpretation, 4) Confusion. This study provided examples of some of the difficulties that the participants faced accessing their lab values online, while navigating and discerning complimentary high-quality health information available in their patient portal.
End Users’ Perceived Engagement with Clinical Dashboards: A Rapid Review
Authors: Gao Grace , Martin Christie L. , Vaughan Camille , Markland Alayne , Kelly Ursula , Pathak Neha , Wallace Joseph , Burningham Zachary
Published: January 2024

Clinical dashboards are an emerging and fast-evolving technology used to support frontline clinicians’ practice. Understanding end users’ perceived engagement with clinical dashboards is essential to co-design, implementation, and adoption. There is a lack of literature exploring the integration of dashboards into clinical workflow. This rapid review explores clinical end users’ perceived engagement with dashboards that support workflow. We conducted a literature search in PubMed and CINAHL. Four articles met our eligibility criteria. Findings reveal variations in taxonomy and measures used to evaluate clinicians’ perceived engagement. There are also a variety of reported barriers and facilitators to adoption. Standardized frameworks and vocabulary are needed to facilitate a common understanding of clinical end users’ perceived engagement with dashboards.
The Impact and Usability of the eRIC System in the ICU – A Qualitative Study
Authors: Li Julie , Hardie Rae-Anne , Dahm Maria R. , Georgiou Andrew
Published: January 2024

The Intensive Care Unit (ICU) is an information-intense environment where more patient data points are recorded than in other wards. The electronic Record for Intensive Care (eRIC) is an ICU information system that integrates patient data every minute from multiple systems. Once implemented across New South Wales (NSW), eRIC will be one of the largest system-wide ICU clinical information systems in the world. This study explored experiences with the use of eRIC by ICU clinicians at an Australian metropolitan teaching hospital. Semi-structured, in-depth interviews relating to physician electronic test management processes were conducted with 11 ICU clinicians and one clinical information system manager was observed in their use of the system. The introduction of eRIC resulted in an additional patient record, which was perceived to hold implications for workflow and patient safety. Study findings are valuable for informing implementation as the rollout of eRIC continues.
Facial Mimicry and Doctor-Patient Satisfaction: The Feasibility of Artificial Empathy in a Clinical Video Data
Authors: Rahmanti Annisa Ristya , Huang Chih-Wei , Muhtar Muhammad Solihuddin , Yang Hsuan-Chia , Li Yu-Chuan (Jack)
Published: January 2024

Good nonverbal communication between doctor and patient is essential for achieving a successful and therapeutic doctor-patient relationship. Increasing evidence has shown that nonverbal communication mimicry, particularly facial mimicry, where one mirrors another’s facial expressions, is linked to empathy and emotion recognition. Empathy is also the key driver of patient satisfaction. This study explores how facial expressions and facial mimicry influence doctor-patient satisfaction during a clinical encounter. We used a facial emotion recognition-based artificial empathy model to analyze 315 recorded clinical video data of doctors and patients in a dermatology outpatient clinic. The results show a significant negative correlation between patients’ emotions of sadness and neutral and doctor satisfaction, but no correlation between the duration of doctors mimicking patient emotions and patient satisfaction. These findings provide valuable insights into the future design of systems that can further enhance clinician awareness to maintain communication skills in the search for better doctor-patient satisfaction.
What Do Health Service Users Think About the Use of Their Data for AI Development?
Authors: Dobson Rosie , Whittaker Robyn
Published: January 2024

AI tools are being introduced within health services around the globe. It is important that tools are developed and validated using the available health information of the population where it is intended to be used. We set out to determine what patients thought about the use of their health information for this purpose. In interviews we found that the patients of a health service in Auckland, Aotearoa New Zealand, are generally comfortable with their health information being used for these purposes but with conditions (around public good, governance, privacy, security, transparency, and restrictions on commercial gain) and with careful consideration of their perspectives. We suggest that health services should take the time to have these conversations with their communities and to provide open and clear communication around these developments in their services.
CBL on FHIR: A FHIR-Based Platform for Health Professional Education
Authors: Braunstein Mark , Barry Ben , Steel Jim , Ukovich Deanne , Grimes John , Conlan David , Jones Sophie , Dobbins Chelsea , Hansen David
Published: January 2024

A FHIR based platform for case-based instruction of health professions students has been developed and field tested. The system provides a non-technical case authoring tool; supports individual and team learning using digital virtual patients; and allows integration of SMART Apps into cases via its simulated EMR. Successful trials at the University of Queensland have led to adoption at the University of Melbourne.
Teaching Module to Train Students in the Participatory Design of Digital Technologies for Disability Assistance
Authors: Ugon Adrien , Isnard Bagnis Corinne , Pelayo Sylvia
Published: January 2024

Teaching the principles of participatory design to students mainly interested in digital skills is important because user-centered approaches have become essential in the field of new technologies when we want to guarantee that a product will meet the needs of its end-users. Working with technologies dedicated to the disability assistance was considered to be the right application domain. In 2021, ESIEE Paris, a school training students to become engineers with digital skills, created and opened a new teaching module to learn how to follow the principles of participatory design to improve the quality of a project. This paper describes the organization and the conclusions of this experience after two editions of the module.
The Saltomachy War – A Metaverse Escape Room on the War Against Salt
Authors: Ahmad Arasyulhaq Bin , Tan Serene , Yap Kevin
Published: January 2024

Excessive salt intake is a risk factor that leads to cardiovascular diseases. Public education on healthy eating habits and maintaining a healthy lifestyle can enable consumers to make better informed choices. This study developed a Metaverse Escape Room to increase public awareness on the “War Against Salt”. Known as “The Saltomachy War” (TSW), 29 participants played the game in teams to solve puzzles across five “worlds” (rooms) to “escape” the game. Median post-game knowledge quiz scores were significantly higher than the pre-game quiz scores (12/15 versus 9/15, p<0.001). All players agreed that the content in TSW was useful in daily life (100%). TSW was easy to navigate (93.1%) and majority were engaged and actively participated in gameplay (96.6%). The storyline was creative and interesting (93.1%). Almost all players liked TSW to be in their curriculum and would recommend TSW to friends and family (96.6% each).
Data-Driven Interventions for an Emergency Preparedness System: A National Experience in Australia
Authors: Andrade Andre Q. , Kerr Mhairi , Roughead Elizabeth E.
Published: January 2024

Natural disasters and health emergencies disproportionally affect vulnerable populations causing disruptions to usual care and increasing chronic disease burden. Data and digital technologies are important tools to identify and mitigate indirect effects of emergencies. In this paper, we describe the methods used in the development of a series of digital emergency preparedness interventions to mitigate the direct and indirect consequences of the COVID-19 pandemic in the veteran community in Australia. The case studies demonstrate the use of data for surveillance, patient phenotyping, data-driven decision support and stakeholder communication in primary care. The intervention successfully increased appropriate healthcare use by vulnerable individuals and could be expanded to other populations.
National Trial Overview: Towards Accessible and Patient-Centered Healthcare
Authors: Weber Ditte , Nøhr Christian
Published: January 2024

This protocol paper explores the initiative to build and implement a National Trial Overview to make clinical trials more accessible to patients and health professionals in Denmark. The paper address how a user-centered evaluation of the platform will be conducted and how the National Trial Overview can contribute to enhancing diversity, equity, and inclusion of patients and health professionals, and make access to clinical trials more patient-centered.
Exploring the Geospatial Relationship Between COVID-19 Positivity and Income in Mixed Urban-Rural Population
Authors: Dahu Butros M. , Khan Solaiman , Sheets Lincoln R. , Scott Grant J.
Published: January 2024

The COVID-19 pandemic has had a deep influence on American life in general and on the American economy in particular. However, the burden of the pandemic has not been distributed equally among members of a population based on their social-determinants-of-health. The purpose of this study was to investigate whether the median income was associated with COVID-19 total number of tests and positivity rate in Boone County, Missouri during the pandemic. We analyzed the geospatial data using three heat maps showing the Census tract-wise COVID-19 positivity rate, Census tract-wise median income, and Census tract-wise total number of COVID-19 tests to highlight our study findings. Our study results support the hypothesis that individuals with lower median income tend to have a lower total number of COVID-19 tests and higher COVID-19 positivity rates in Boone County, Missouri. The Pearson correlation coefficient between the positivity rate and median income is -0.324.
Current Status of SNOMED CT National Extensions and Terminology Managements
Authors: Kim Seeun , Shin Soo-Yong , Hwang Ji Eun , Park Hyeoun-Ae
Published: January 2024

We reviewed and surveyed 15 SNOMEDCT national member countries for SNOMED CT national extensions and terminology managements. We found that national extensions were used for adding new contents, developing reference sets, translating, and mapping with other classification system; and terminology management varies in composition and content due to healthcare environment of each member country, eHealth strategy, and infrastructure of national release centers.
Electronic Health Records Role in Predicting Outcomes to Work-Related Musculoskeletal Disorders: A Study Protocol
Authors: Wassell Melinda , Vitiello Andrew , Pollard Henry
Published: January 2024

Work-related musculoskeletal disorders are increasing in cost and time lost from work. Electronic health records have the potential to provide rich data to help inform and predict outcomes to WMSDs. The objective is to compare an EHR dataset from an occupational health service to comparative data, to help determine if the EHR dataset can be used in future studies to predict outcomes to care.
Visit-to-Visit Blood Pressure Variability in Cardiovascular Disease
Authors: Lukitasari Mifetika , Liaw Siaw-Teng , Jalaludin Bin , Jonnagaddala Jitendra
Published: January 2024

Visit-to-visit blood pressure variability (BPV) is associated with cardiovascular disease (CVD), independently of mean blood pressure (BP). However, in real world clinical practice, this phenomenon is frequently considered as random fluctuation. This review aimed to investigate the differences among studies investigating visit-to-visit BPV and CVD using electronic health record (EHR) and clinical trial data. Our review suggests that BP values in clinical trial data are derived using a stricter protocol compared to EHR data. Furthermore, there was no consensus on metrics used in estimation of BPV.
Implementation of a Multilingual Electronic Medical Questionnaire: For Use Inside or Outside the Hospital
Authors: Konishi Shozo , Manabe Shirou , Shimai Yoshie , Namiuchi Yoshiki , Sugimoto Kento , Wada Shoya , Okada Katsuki , Matsumura Yasushi , Takeda Toshihiro
Published: January 2024

We implemented a multilingual medical questionnaire system, which allows patients to answer questionnaires both in and out of the hospital. The response data are sent to and stored as structured data on the server in hospital information system, and could be converted to Japanese and quoted as part of progress notes in the electronic medical record.
An Informatics Platform for the Management of Data for Australian Dementia Network (ADNeT) Initiative
Authors: Trinder Julie , Bourgeat Pierrick , Xia Ying , Fripp Jurgen , Raniga Parnesh
Published: January 2024

Alzheimer’s disease and other dementias are becoming more prevalent and placing increasing burdens on the community. The ADNeT Screening and Trials initiative aims to improve research outcomes by identifying people with an increased risk of developing these diseases and directing them to suitable clinical trials. To support the initiative, we have developed a modular informatics platform utilizing private cloud deployment to securely manage operational and research data across six clinical sites.
High Accuracy Open-Source Clinical Data De-Identification: The CliniDeID Solution
Authors: Meystre Stéphane , Heider Paul
Published: January 2024

Clinical data de-identification offers patient data privacy protection and eases reuse of clinical data. As an open-source solution to de-identify unstructured clinical text with high accuracy, CliniDeID applies an ensemble method combining deep and shallow machine learning with rule-based algorithms. It reached high recall and precision when recently evaluated with a selection of clinical text corpora.
Accuracy Evaluation of an Estimation System for Dental Treatment Sites by Using Image Recognition
Authors: Nishimoto Shintaro , Oka Shintaro , Nozaki Kazunori
Published: January 2024

In dentistry, misidentification of the treatment site may occur an adverse event with irreversible consequences. Among these, the left-right tooth error is the second most common site misidentification. In this study, we developed a treatment site estimation system using image recognition, and the accuracy rate of the left and right teeth was more than 85%. The results suggest that this system can be used to prevent the misidentification of the left and right teeth.
Feasibility of Embedding a Randomised Clinical Trial (RCT) into an Electronic Medical Record (EMR) for Patients Admitted to an Intensive Care Unit (ICU)
Authors: Panganiban Haustine Patt , Nguyen Chinh Dam , Abdelhamid Yasmin Ali , Ankravs Melissa , Karahalios Emily , Macisaac Christopher , Rechnitzer Tom , Sharrock Lucy , Tran-Duy An , Fazio Timothy , Deane Adam M.
Published: January 2024

To establish the feasibility of embedding an RCT into EMR in the ICU, we evaluated the route of phosphate replacement. The EMR screened 207 patients who met the inclusion criteria from 20 April 2022 to 30 June 2022. 162 patients were randomised and 145 patients allocated to treatment. Our study showed that it was feasible to embed screening, randomisation, and treatment allocation for an RCT within an EMR in the ICU.
Answering Japanese Fill-in-the-Blank Questions in the Radiological Technology Field Using BERT
Authors: Yagahara Ayako , Yokoyama Noriya , Higashi Mao
Published: January 2024

The purpose of this study was to evaluate the accuracy of Japanese fill-in-the-blank questions in the radiological technology field using six BERT models. Our original model, created using 330,000 abstracts related to radiological technology, was as accurate as other models created by Wikipedia when vocabulary size was the same. Further improvements in accuracy can be expected if we can increase the vocabulary size and add Japanese Wikipedia data in training.
Ensemble Learning Method for In-Hospital Cardiac Arrest Prediction
Authors: Koo Ja Hyung , Lee Sun Jung , Kim Yun Kwan , Song Hee Seok
Published: January 2024

Cardiac arrest prediction for multivariate time series data have been developed and obtained high precision performance. However, these algorithms still did not achieved high sensitivity and suffer from a high false-alarm. Therefore, we propose a ensemble approach for prediction satisfying precision-recall result compared than other machine learning methods. As a result, our proposed method obtained an overall area under precision-recall curve of 46.7%. It is possible to more accurately respond rapidly cardiac arrest event.
Exploring the Knowledge Structure and Trends for Severe COVID-19 Risk Factors Using Text Network Analysis
Authors: Kang Min-Ah , Lee Soo-Kyoung
Published: January 2024

This study was aimed to identify knowledge structure and trends in severe COVID-19 risk factor using text network analysis. The 22,628 papers published during from January 2020 to December 2021. We analyzed and visualized using Text Rank analyzer and Gephi software. They were grouped into 5 central themes – biomedical factors, occupational environmental factors, demographic factors, health behavior factors, and complications. The emerging topics were identified to the chronological trends. This study can promote a systematic understanding of severe COVID-19 risk factors.
Use of Electronic Quality Monitoring Tool and Central Dashboard to Improve Clinical and Programmatic Decisions
Authors: Eskinder Biniyam , Habte Dereje , Molla Masresha , Hailemeskel Fasil , Tewldebirhan Esayas , Kebede Tekeste , Assefa Tamrat , Kabtyimer Daniel , Amidino Worknesh , Lulseged Sileshi
Published: January 2024

The HIV service quality improvement tool is deployed in 123 health facilities in Ethiopia. The tool uses a central dashboard for visualization and decision making at the health facility and higher levels of the health systems. The dashboard is developed on excel with analytics about HIV testing, case finding, treatment linkage and quality indicators. The dashboard was developed based on the requirements requested during discussions with HIV clinicians and the program team.
Evaluating Effects of Resting-State Electroencephalography Data Pre-Processing on a Machine Learning Task for Parkinson’s Disease
Authors: Vlieger Robin , Daskalaki Elena , Apthorp Deborah , Lueck Christian J. , Suominen Hanna
Published: January 2024

Resting-state electroencephalography pre-processing methods in machine learning studies into Parkinson’s disease classification vary widely. Here three separate data sets were pre-processed to four different stages to investigate the effects on evaluation metrics, using power features from six regions-of-interest, Random Forest Classifiers for feature selection, and Support Vector Machines for classification. This showed muscle artefact inflated evaluation metrics, and alpha and theta band features produced the best results when fully pre-processing data.
Daily Hospital Emerging Infectious Disease(EID) Workflow & Data Processing Automation
Authors: Goh Bee Keow , Sim Ching Yee , Chuah Mark , Siang Chee Meng , Thomas Terrence
Published: January 2024

Following the onset of the covid pandemic two years ago, the Ministry of Health(MOH)’s required all health care cluster groups to provide daily reporting of the emergency department as well as the inpatient situation in the respective healthcare institutions for oversight of the covid situation. In view of the improvements in the data availability and relief of the tedious manual collation, DAO was entrusted with the task to help enable and setup the standardized report and generation process moving forward
Educational Needs Assessment for Family Caregivers of Dementia Patients
Authors: Im Eunyoung , Kim Hyeoneui
Published: January 2024

People living with dementia are highly dependent on caregivers. We conducted an online survey with regard to caregivers’ educational experiences, needs, and expectations. We found that most of the participants lacked educational experiences and expected updated methods through metaverse in virtual reality. Therefore, future studies should verify the effectiveness of education.
Improving Accessibility of Continuing Professional Development for Oncology Health Professionals Through the EviQ Education App
Authors: McLean Lisa , Zheng Annie , Bullivant Catherine , Wuellner Laura , Rushton Shelley , O’Brien Tracey
Published: January 2024

Oncology clinicians must participate in continuing professional development (CPD) to stay up to date with best practice. The Cancer Institute NSW eviQ Education program produces evidence-based, peer-reviewed eLearning resources for oncology professionals. In response to user feedback, eviQ Education trialled a mobile app, EdApp, to improve accessibility of self-directed CPD materials. Following a pilot, users indicated that the EdApp improved CPD accessibility and user experience. EdApp will continue to be used to support evidence-based practice.
Objective Smartwatch Indices of Affective Switching: A Pilot Study in Premenstrual Dysphoric Disorder
Authors: Prim Julianna , Favorov Oleg , Jeffrey Jolie , Girdler Susan , Schiller Crystal
Published: January 2024

Wearable devices offer a unique opportunity to provide real-time monitoring of affective switching (the mood transition into and out of dysregulated affective state), a critical window to detect and prevent depression and suicide. To model affective switching, we studied premenstrual dysphoric disorder (PMDD): a depressive disorder with a regularly occurring monthly trigger. Results supported feasibility of smartwatch monitoring protocol and preliminary evidence that objective physiological and behavioral metrics were associated with affective state.