bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards Automated Evaluation of Patient Centered Care–Assessing the Potential of Electronic Health Records
Authors: von Gerich Hanna , Lozada-Perezmitre Erika , Pruinelli Lisiane , Peltonen Laura-Maria
Published: January 2024

Providing patient centered care is a crucial element of high quality care. It can be defined as a responsive way of caring for and empowering patients, embodying compassion, empathy, and responsiveness to the patient’s needs. The aim of this study was to assess the potential of using EHRs as information source in the development of tools for assessing PCC. An annotation guide following the Person-centred Practice Framework proposed by McCance and McCormack was developed for the purpose of this study. Twenty patients’ documents were manually annotated, resulting in 539 expressions. All dimensions of the framework were covered in the documents, with 61.3% of expressions describing the activity of engaging authentically with the patient. The results of this study indicate that electronic health records are one potential source of information in automated evaluation of patient centered care, however more information is still needed on how to interpret this information.
A Patient-Centered Approach to Collecting and Displaying Patient Identifiers
Authors: Van Hal Christina , Mills Jennifer L. , Gatmaitan Mary , Gong Yang
Published: January 2024

Patient identifiers such as name, date of birth, or gender are the first line of defense to ensure the accuracy of the health data displayed in health information exchange. Health data display is the impetus for clinical decisions and patient outcomes and directly correlates with promoting interoperability and health information exchange. Therefore, constant monitoring of quality metrics is imperative for clinical leaders to keep a pulse on what is happening within their organizations. However, the electronic health records (EHRs) designer should also take precautions to ensure the visualizations are not misleading, given that EHRs have been shown in some studies to lead to increased patient safety events.
Effectiveness, Costs and Satisfaction of Telemedicine: Review of the Current State
Authors: Bruchanski Lucila , Frid Santiago , Tejerina Luis , Sommer Janine , Nelson Jennifer , Otero Paula , Bagolle Alexandre , Plazzotta Fernando
Published: January 2024

The objective of this study was to summarize the evidence in relation to telemedicine systems as regards their effectiveness, costs and satisfaction in the last decade. A summary of main findings is presented. According to results telemedicine proved to be a feasible and effective tool to provide health care as a replacement or complement to usual care, especially when applied to chronic diseases.
Surveillance for Diabetes in Hospital-Evaluation of Dashboard Effectiveness
Authors: Hng Tien-Ming , Kolambkar Reshma , Luo Ching , Pryce David
Published: January 2024

There is a large burden of diabetes in Western Sydney, and this descriptor is also valid for the inpatient population. Optimizing diabetes care in hospital is important to prevent inpatient morbidity. A surveillance system was developed to address this need and a diabetes dashboard was developed around a data model built around the patient journey that integrated key data feeds from the pathology, medication, and patient administration systems. This facilitated the rapid identification and triaging of individuals with diabetes. We evaluated this dashboard using high level clinical and financial indicators. This implementation resulted in an improved time to patient review, and a reduction in 28-day readmission rates but the inpatient length of hospital stay was unchanged. (116 words)
Factors Affecting Family Caregivers’ Satisfaction During Virtual Care Visits
Authors: Khairat Saif , Srilakshmi Cheeti , Helvey Sara , Mann Jami , Barzin Amir
Published: January 2024

There are 18 million caregivers in the US who are essential in the care process for patients to facilitate scheduling appointments, administer medication, and assist with care coordination. The objective of this study was to examine the facilitators and barriers to a positive virtual care experience for caregivers. examined the satisfaction levels among caregivers using a virtual care service at a Southeastern Medical Center in the U.S. We analyzed 231 virtual care visits completed by caregivers. We found that insurance status and medication prescription played a significant role in caregivers’ ratings of their virtual care experience. Caregivers reported that virtual care can improve their quality of life, while mixed opinions were reported with regard to the use of virtual care, and the quality of care provided.
Real-World Effectiveness of Lung Cancer Screening Using Deep Learning-Based Counterfactual Prediction
Authors: Feng Zheng , Chen Zhaoyi , Guo Yi , Prosperi Mattia , Mehta Hiren , Braithwaite Dejana , Wu Yonghui , Bian Jiang
Published: January 2024

The benefits and harms of lung cancer screening (LCS) for patients in the real-world clinical setting have been argued. Recently, discriminative prediction modeling of lung cancer with stratified risk factors has been developed to investigate the real-world effectiveness of LCS from observational data. However, most of these studies were conducted at the population level that only measured the difference in the average outcome between groups. In this study, we built counterfactual prediction models for lung cancer risk and mortality and examined for individual patients whether LCS as a hypothetical intervention reduces lung cancer risk and subsequent mortality. We investigated traditional and deep learning (DL)-based causal methods that provide individualized treatment effect (ITE) at the patient level and evaluated them with a cohort from the OneFlorida+ Clinical Research Consortium. We further discussed and demonstrated that the ITE estimation model can be used to personalize clinical decision support for a broader population.
Sensing Swelling: Towards Remote Monitoring of Craniectomy Patients
Authors: Bradford DanaKai , von Richter Karl , Thayaparan Ganesha , Gibson Simon , Furtado Aeyohan , Owbridge Mark , D’Urso Paul
Published: January 2024

Decompressive craniectomy (DC) is a surgical procedure where a portion of the skull is removed to relieve potentially fatal brain swelling. As the swelling can take months to subside, the patient is discharged from an acute care facility to recover prior to cranioplasty (reconstruction surgery). Cranioplasty is associated with complications due to infection, seizure, haematoma and death. The interval between these surgeries is potentially a modifiable risk factor to reduce the rate of complication. We aim to allow clinicians to remotely monitor patients to facilitate an optimal pre-operative review. We have developed a platform technology encompassing a ‘smart’ device fitted into a skullcap to measure physiological parameters, such as changes in brain swelling, and a clinician portal that allows remote viewing of the patients’ physiological data. The use of patient generated data during the transition between craniectomy and cranioplasty has the potential to significantly improve neurorehabilitation outcomes for patients.
Patient-Centered Clinical Decision Support–Where Are We and Where to Next?
Authors: Dullabh Prashila , Leaphart Desirae , Dhopeshwarkar Rina , Heaney-Huls Krysta , Desai Priyanka
Published: January 2024

Patient-centered clinical decision support (PC CDS) includes digital health tools that support patients, caregivers, and care teams in healthcare decisions that incorporate patient-centered factors related to four components: knowledge, data, delivery, and use. This paper explores the current state of each factor and how each promotes patient-centeredness in healthcare. We conducted a literature review, reviewing 175 peer-reviewed and grey literature, and eighteen key informant interviews. Findings show a need for more research on how to incorporate patient input into the guideline selection and prioritization for PC CDS, development and implementation of PC CDS tools, technical challenges for capturing patient contributed data, and optimizing PC CDS across various settings to meet patient and caregiver needs. While progress is being made in each of the four components of PC CDS, critical gaps remain.
Informing Personalised Gamification Interventions Through a Novel Gamified Quiz
Authors: Dippenaar Christiaan R. , Redd Christian , Varnfield Marlien
Published: January 2024

Nonadherence to medical interventions and other advice leads to increased care costs and poorer health outcomes across a range of medical fields. An approach to increasing adherence is gamification. To maximize the benefits of gamification, a more structured and informed implementation is required. In this paper, a novel web-based personality determiner has been outlined, the integration of which could provide the personalisation required for an optimized gamification implementation.
Making Digital Health Equitable
Authors: Hammond W. Ed , West Vivian L.
Published: January 2024

Most agree that the current healthcare system is broken. Fortunately, technology is increasing at an exponential rate and provides a solution for the future. Digital Health is an integrator concept that has the potential to take advantage of technological advantages. Digital Health converges health, healthcare, research, and everyday life. It includes technologies, platforms, and systems that engage consumers in all aspects of life. It makes health and healthcare be people-centered and personalized. Digital health requires total interoperability – standards, common data elements, and the integration of data from all sources. It demands data sharing. Digital Health brings together a wide range of stakeholders for similar goals using the same resources. Digital Health uses mobile devices and wearable sensors and uses Artificial Intelligence and Machine Learning to handle the vast amount of data Digital Health engages. Finally, Digital Health has the potential to open the gap between the different social and economic classes that must be addressed.
Feasibility of “Symptom Discovery,” a Longitudinal and Comprehensive Data Collection Tool During COVID-19
Authors: Sarkar Sayantani , Kim Katherine , Liu Xin , Joseph Jill , Natale Joanne
Published: January 2024

There are few patient- or public-facing tools for longitudinal and comprehensive symptom assessment, especially when faced with an uncharacterized condition such as COVID-19 or a chronic condition in which symptoms have not been adequately specified. To address this need, we developed the Symptom Discovery mobile application and tested its feasibility with the US public early in the COVID-19 pandemic. Although there were challenges, results showed feasibility and acceptance.
Patient-Centred, Technology-Based Interventions for High Treatment Burden: An Overview of the State of the Art
Authors: Meiklem Ramsay , Bouamrane Matt-Mouley , Kingsmore David , Stevenson Karen , Dunlop Mark , Thomson Peter
Published: January 2024

This study provides an overview of recent efforts to develop technology-based interventions for patients with high treatment burden. Adoption of a technological intervention for high treatment burden will depend on a range of factors, including: the reliability and accessibility of the technology, patient factors including age, socio-economic factors, technology literacy, overall health status and personal priorities – as well as the perceived benefits provided by the technology intervention, from the perspectives of both patients and clinicians.
Adjustable Cuffless Smartphone Attachment (ACSA+) for Estimation of Blood Pressure Trends: A Pilot Study
Authors: Sholokhova Kseniia , Jian Wen-Shan , Yang Hsuan-Chia , Li Yu-Chuan (Jack)
Published: January 2024

Among the elderly, hypertension remains one of the prevalent health conditions, which requires monitoring and intervention strategies. Nevertheless, regular reporting of blood pressure (BP) from these individuals still poses multiple challenges. However, most people own cell phone and are engaged in phone conversations daily. Here, we propose an adjustable cuffless smartphone attachment (ACSA+) equipped with a PPG sensor for the estimation of BP during phone conversations. ACSA+ can be easily attached to the back of any modern cell phone. ACSA+ will help to continuously collect BP data and store it as a trend line.
Towards Structuring Clinical Texts: Joint Entity and Relation Extraction from Japanese Case Report Corpus
Authors: Shibata Daisaku , Shinohara Emiko , Shimamoto Kiminori , Kawazoe Yoshimasa
Published: January 2024

Important pieces of information related to patient symptoms and diagnosis are often written in free-text form in clinical texts. To utilize these texts, information extraction using natural language processing is required. This study evaluated the performance of named entity recognition (NER) and relation extraction (RE) using machine-learning methods. The Japanese case report corpus was used for this study, which had 113 types of entities and 36 types of relations that were manually annotated. There were 183 cases comprising 2,194 sentences after preprocessing. In addition, a machine learning model based on bidirectional encoder representations from transformers was used. The results revealed that the maximum micro-averaged F1 scores of NER and RE were 0.912 and 0.759, respectively. The results of this study are comparable to those of previous studies. Hence, these results could be of substantial baseline accuracy.
Locating Loneliness Through Social Intelligence Analysis
Authors: Ali Shah Hurmat , Househ Mowafa
Published: January 2024

Loneliness is a global public health issue, but the dynamics of loneliness are not understood. Through a global loneliness map, we plan to understand the dynamics of loneliness better by analyzing social media data on loneliness through social intelligence analysis. In this paper, we present the first proof of concept of the global loneliness map. Data on loneliness using keywords associated with loneliness was collected from the USA and analyzed to find meaningful associations of themes with loneliness. The NLP tool used for sentiment analysis of the tweets is a valence aware dictionary for sentiment reasoning (VADER). The tweets with negative sentiment were further analyzed for psychosocial linguistic features to find meaningful correlation between loneliness and socioeconomic and emotional themes and factors. Loneliness is subjective, hence social intelligence analysis through social media and machine learning tools can help us better understand loneliness.
Final Report on the German Clinical Reference Corpus 3000PA
Authors: Hahn Udo , Modersohn Luise , Faller Jakob , Lohr Christina
Published: January 2024

We here report on one of the outcomes of a large-scale German research program, the Medical Informatics Initiative (MII), aiming at the development of a solid data and software infrastructure for German-language clinical natural language processing. Within this framework, we have developed 3000PA, a national clinical reference corpus composed of patient records from three clinical university sites and annotated with a multitude of semantic annotation layers (including medical named entities, semantic and temporal relations between entities, as well as certainty and negation information related to entities and relations). This non-sharable corpus has been complemented by three sharable ones (JSYNCC, GGPONC, and GRASCCO). Overall, 3000PA, JSYNCC and GRASCCO feature about 2.1 million metadata points.
Using Natural Language Processing to Extract and Classify Symptoms Among Patients with Thyroid Dysfunction
Authors: Hwang Sy , Reddy Sujatha , Wainwright Katherine , Schriver Emily , Cappola Anne , Mowery Danielle
Published: January 2024

In the United States, more than 12% of the population will experience thyroid dysfunction. Patient symptoms often reported with thyroid dysfunction include fatigue and weight change. However, little is understood about the relationship between these symptoms documented in the outpatient setting and ordering patterns for thyroid testing among various patient groups by age and sex. We developed a natural language processing and deep learning pipeline to identify patient-reported outcomes of weight change and fatigue among patients with a thyroid stimulating hormone test. We built upon prior works by comparing 5 open-source, Bidirectional Encoder Representations from Transformers (BERT) to determine which models could accurately identify these symptoms from clinical texts. For both fatigue (f) and weight change (wc), Bio_ClinicalBERT achieved the highest F1-score (f: 0.900; wc: 0.906) compared BERT (f: 0.899; wc: 0.890), DistilBERT (f: 0.852; wc: 0.912), Biomedical RoBERTa (f: 0.864; wc: 0.904), and PubMedBERT (f: 0.882; wc: 0.892).
Relation Detection to Identify Stroke Assertions from Clinical Notes Using Natural Language Processing
Authors: Yang Audrey , Kamien Sam , Davoudi Anahita , Hwang Sy , Gandhi Meet , Urbanowicz Ryan , Mowery Danielle
Published: January 2024

According to the World Stroke Organization, 12.2 million people world-wide will have their first stroke this year almost half of which will die as a result. Natural Language Processing (NLP) may improve stroke phenotyping; however, existing rule-based classifiers are rigid, resulting in inadequate performance. We report findings from a pilot study using NLP to improve relation detection for stroke assertion detection to support research studies and healthcare operations.
Answering List-Type Questions in Health Domain with Pretrained Large Language Model: A Case for COVID-19 Symptoms
Authors: Jiang Keyuan , Mujtaba Mohammed M. , Bernard Gordon R.
Published: January 2024

List-type questions, which can have a varying number of answers, are more common in the health domain where people seek for health-related information from a passage or passages. An example of this type of question answering task is to find COVID-19 symptoms from a Twitter post. However, due to the lack of annotated instances for supervised learning, automatic identification of COVID-19 symptoms from Twitter posts is challenging. We investigated detection of symptom mentions in Twitter posts using GPT-3, a pre-trained large language model, along with few-shot learning. Our results of 5-shot and 10-shot learning on a corpus of 655 annotated tweets demonstrate that few-shot learning with pre-trained large language model is a promising approach to answering list-type questions with a minimal amount of effort of annotation.
TAXN: Translate Align Extract Normalize, a Multilingual Extraction Tool for Clinical Texts
Authors: Neuraz Antoine , Lerner Ivan , Birot Olivier , Arias Camila , Han Larry , Bonzel Clara Lea , Cai Tianxi , Huynh Kim Tam , Coulet Adrien
Published: January 2024

Several studies have shown that about 80% of the medical information in an electronic health record is only available through unstructured data. Resources such as medical terminologies in languages other than English are limited and restrain the NLP tools. We propose here to leverage English based resources in other languages using a combination of translation, word alignment, entity extraction and term normalization (TAXN). We implement this extraction pipeline in an open-source library called “medkit”. We demonstrate the interest of this approach through a specific use-case: enriching a phenotypic dictionary for post-acute sequelae in COVID-19 (PASC). TAXN proved to be efficient to propose new synonyms of UMLS terms using a corpus of 70 articles in French with 356 terms enriched with at least one validated new synonym. This study was based on freely available deep-learning models.
Development of a Natural Language Processing System to Identify Clinical Documentation of Electronic Cigarette Use
Authors: Alba Patrick R. , Gan Qiwei , Hu Mengke , Zhu Shu-Hong , Sherman Scott E. , DuVall Scott L. , Conway Mike
Published: January 2024

Electronic Nicotine Delivery Systems (ENDS) use has increased substantially in the United States since 2010. To date, there is limited evidence regarding the nature and extent of ENDS documentation in the clinical note. In this work we investigate the effectiveness of different approaches to identify a patient’s documented ENDS use. We report on the development and validation of a natural language processing system to identify patients with explicit documentation of ENDS using a large national cohort of patients at the United States Department of Veterans Affairs.
Applying and Improving a Publicly Available Medication NER Pipeline in a Clinical Cancer EMR
Authors: Stevens Meg , Kennedy Georgina , Churches Timothy
Published: January 2024

Clinical NLP can be applied to extract medication information from free-text notes in EMRs, using NER pipelines. Publicly available annotated data for clinical NLP are scarce, and research annotation budgets are often low. Fine-tuning pre-trained pipelines containing a Transformer layer can produce quality results with relatively small training corpora. We examine the transferability of a publicly available, pre-trained NER pipeline with a Transformer layer for medication targets. The pipeline performs poorly when directly validated but achieves an F1-score of 92% for drug names after fine-tuning with 1,565 annotated samples from a clinical cancer EMR – highlighting the benefits of the Transformer architecture in this setting. Performance was largely influenced by inconsistent annotation – reinforcing the need for innovative annotation processes in clinical NLP applications.
Identifying Mentions of Pain in Mental Health Records Text: A Natural Language Processing Approach
Authors: Chaturvedi Jaya , Velupillai Sumithra , Stewart Robert , Roberts Angus
Published: January 2024

Pain is a common reason for accessing healthcare resources and is a growing area of research, especially in its overlap with mental health. Mental health electronic health records are a good data source to study this overlap. However, much information on pain is held in the free text of these records, where mentions of pain present a unique natural language processing problem due to its ambiguous nature. This project uses data from an anonymised mental health electronic health records database. A machine learning based classification algorithm is trained to classify sentences as discussing patient pain or not. This will facilitate the extraction of relevant pain information from large databases. 1,985 documents were manually triple-annotated for creation of gold standard training data, which was used to train four classification algorithms. The best performing model achieved an F1-score of 0.98 (95% CI 0.98-0.99).
Extracting Symptoms of Agitation in Dementia from Free-Text Nursing Notes Using Advanced Natural Language Processing
Authors: Vithanage Dinithi , Zhu Yunshu , Zhang Zhenyu , Deng Chao , Yin Mengyang , Yu Ping
Published: January 2024

Nursing staff record observations about older people under their care in free-text nursing notes. These notes contain older people’s care needs, disease symptoms, frequency of symptom occurrence, nursing actions, etc. Therefore, it is vital to develop a technique to uncover important data from these notes. This study developed and evaluated a deep learning and transfer learning-based named entity recognition (NER) model for extracting symptoms of agitation in dementia from the nursing notes. We employed a Clinical BioBERT model for word embedding. Then we applied bidirectional long-short-term memory (BiLSTM) and conditional random field (CRF) models for NER on nursing notes from Australian residential aged care facilities. The proposed NER model achieves satisfactory performance in extracting symptoms of agitation in dementia with a 75% F1 score and 78% accuracy. We will further develop machine learning models to recommend the optimal nursing actions to manage agitation.
Treatment Prediction in the ICU Using a Partitioned, Sequential, Deep Time Series Analysis
Authors: Shapiro Michael , Shahar Yuval
Published: January 2024

We have developed a time-oriented machine-learning tool to predict the binary decision of administering a medication and the quantitative decision regarding the specific dose. We evaluated our tool on the MIMIC-IV ICU database, for three common medical scenarios. We use an LSTM based neural network, and considerably extend its use by introducing several new concepts. We partition the common 12-hour prediction horizon into three sub-windows. Partitioning models the treatment dynamics better, and allows the use of previous sub-windows’ data as additional training data with improved performance. We also introduce a sequential prediction process, composed of a binary treatment-decision model, followed, when relevant, by a quantitative dose-decision model, with improved accuracy. Finally, we examined two methods for including non-temporal features, such as age, within the temporal network. Our results provide additional treatment-prediction tools, and thus another step towards a reliable and trustworthy decision-support system that reduces the clinicians’ cognitive load.
Physician-Centered EHR Data Utilization: A Pilot Study
Authors: Wu Chengkai , Zhou Tianshu , Tian Yu , Sun Huiyao , Liu Zhong , Li Jingsong
Published: January 2024

The utilization of vast amounts of EHR data is crucial to the studies in medical informatics. Physicians are medical participants who directly record clinical data into EHR with their personal expertise, making their roles essential in follow-up data utilization, which current studies have yet to recognize. This paper proposes a physician-centered perspective for EHR data utilization and emphasizes the feasibility and potentiality of digging into physicians’ latent decision patterns in EHR. To support our proposal, we design a physician-centered CDS approach named PhyC and test it on a real-world EHR dataset. Experiments show that PhyC performs significantly better in the auxiliary diagnosis of multiple diseases than globally learned models. Discussions on experimental results suggest that physician-centered data utilization can help to derive more objective CDS models, while more means for utilization need further exploration.
Continuous Remote Patient Monitoring for Post-Discharge Heart Failure Management: Workflow Modeling Using Discrete Event Simulation
Authors: Padman Rema , Venkatasubramanian Anirudh Vaidhyaa , Chi Wei Ning , Solomonides Anthony , Shah Nirav
Published: January 2024

The Cascade-HF protocol is a Continuous Remote Patient Monitoring (CRPM) study at a major health system in the United States to reduce Heart Failure (HF)-related hospitalizations and readmissions using wearable biosensors to collect physiological data over a 30-day period to determine decompensation risk among HF patients. The alerts produced, coupled with electronic patient-reported outcomes, are utilized daily by the home health team, and escalated to the heart failure team as needed, for proactive actions. Limited research has examined anticipating the implementation and workflow challenges of such complex CRPM studies such as resource planning and staffing decisions that leverage the recorded data to drive clinical preparedness and operational efficiency. This preliminary analysis applies discrete event simulation modeling to the Cascade-HF protocol using pilot data from a soft launch to assess workload of the clinical team, evaluate escalation patterns and provide decision support recommendations to enable scale-up for all post-discharge patients.
Challenges in Interpreting Norwegian Child and Adolescent Mental Health Records
Authors: Koochakpour Kaban , Sofie Solheim Frida , Nytrø Øystein , Clausen Carolyn , Frodl Thomas , Koposov Roman , Leventhal Bennett , Pant Dipendra , Brox Røst Thomas , Stien Line , Sverre Westbye Odd , Skokauskas Norbert
Published: January 2024

The Electronic Health Record system BUPdata served Norwegian Child and Adolescent Mental Health Services (CAMHS) for over 35 years and is still an important source of information for understanding clinical practice. Secondary usage of clinical data enables learning and service quality improvement. We present some insights from explorative data analysis for interpreting the records of patients referred for hyperkinetic disorders. The major challenges were data preparation, pre-analysis, imputation, and validation. We summarize the main characteristics, spot anomalies, and detect errors. The results include observations about the patient referral diversity based on 12 different variables. We modeled the activities in an individual episode of care, described our clinical observations among data, and discussed the challenges of data analysis.
Dementia Prediction in Older Adults Using Sex-Specific Health Trajectory Clustering
Authors: Ibrahim Omar A. , Garg Muskan , Fu Sunyang , Vassilaki Maria , Petersen Ronald C. , St Sauver Jennifer , Sohn Sunghwan
Published: January 2024

With increasing number of people living with dementia, the problem of late diagnosis significantly impacts a person’s quality of life while early signs of dementia may provide useful insights to facilitate better treatment plans. With time, this progressive neurodegenerative syndrome could progress from mild cognitive impairment to dementia. A pattern of health conditions can be characterized in unsupervised manner to help predict this progress. As a significant extension to our previous work with streaming clustering model, we consider additional information for predicting dementia onset. With empirical observations, we discover the importance of examining sex and age to predict dementia onset. To this end, we propose a sex-specific model with age-constraint for predicting dementia onset and validate the effectiveness of our models using data from Mayo Clinic Study of Aging (MCSA). The proposed sex-specific models for older adult populations (>=65 years of age) outperformed the previous models with F-score of 77% and 78% for male-specific and female-specific models, respectively. Our experiments of sex-specific temporal clustering of features in older adults demonstrate the potential of more personalized models for early alerts of dementia.
Elucidating Discrepancy in Explanations of Predictive Models Developed Using EMR
Authors: Brankovic Aida , Huang Wenjie , Cook David , Khanna Sankalp , Bialkowski Konstanty
Published: January 2024

The lack of transparency and explainability hinders the clinical adoption of Machine learning (ML) algorithms. While explainable artificial intelligence (XAI) methods have been proposed, little research has focused on the agreement between these methods and expert clinical knowledge. This study applies current state-of-the-art explainability methods to clinical decision support algorithms developed for Electronic Medical Records (EMR) data to analyse the concordance between these factors and discusses causes for identified discrepancies from a clinical and technical perspective. Important factors for achieving trustworthy XAI solutions for clinical decision support are also discussed.
Implementation of an HIV Case Based Surveillance Using Standards-Based Health Information Exchange in Rwanda
Authors: Oluoch Tom , Byiringiro Baptiste , Tuyishime Elysee , Kitema Frank , Ntwali Loic , Malamba Samuel , Wilmore Sridevi , Remera Eric
Published: January 2024

As Rwanda approaches the UNAIDS Fast Track goals which recommend that 95% of HIV-infected individuals know their status, of whom 95% should receive treatment and 95% of those on treatment achieve viral suppression, the country currently relies on an inefficient paper, and disjointed electronic, systems for case-based surveillance (CBS). Rwanda has established an ecosystem of interoperable systems based on open standards to support HIV CBS. Data were successfully exchanged between an EMR, a client registry, laboratory information system and DHIS-2 Tracker, and subsequently, a complete analytic dataset was ingested into MS-Power Business Intelligence (MS-PowerBI) for analytics and visualization of the CBS data. Existing challenges included inadequate workforce capacity to support mapping of data elements to HL7 FHIR resources. Interoperability optimization to support CBS is work in progress and rigorous evaluations on the effect on health information exchange on monitoring patient outcomes are needed.
A New Statistical Method to Detect Disease Outbreaks from Hospital Emergency Department Data
Authors: Yoon Jin , Boyle Justin
Published: January 2024

Early detection and prediction of disease outbreaks are crucial for public health service delivery, containment response, saving patient lives, and reducing costs. We propose a new data-driven statistical methodology for outbreak detection and prediction based on routinely collected hospital Emergency Department data. The time between consecutive ED presentations matching a diagnosis of interest forms the basis of a novel index measure to signal that an outbreak has occurred. We validate the method using historical presentations of influenza-like illness made to a large sample of public hospital EDs in 2020 and compare outbreaks identified by the method with the start of the first wave of COVID-19. The method shows promise within the field of disease outbreak detection.
A Deep Learning-Based System for the Assessment of Dental Caries Using Colour Dental Photographs
Authors: Mehdizadeh Maryam , Estai Mohamed , Vignarajan Janardhan , Patel Jilen , Granich Joanna , Zaniovich Michael , Kruger Estie , Winters John , Tennant Marc , Saha Sajib
Published: January 2024

D1ental caries remains the most common chronic disease in childhood, affecting almost half of all children globally. Dental care and examination of children living in remote and rural areas is an ongoing challenge that has been compounded by COVID. The development of a validated system with the capacity to screen large numbers of children with some degree of automation has the potential to facilitate remote dental screening at low costs. In this study, we aim to develop and validate a deep learning system for the assessment of dental caries using color dental photos. Three state-of-the-art deep learning networks namely VGG16, ResNet-50 and Inception-v3 were adopted in the context. A total of 1020 child dental photos were used to train and validate the system. We achieved an accuracy of 79% with precision and recall respectively 95% and 75% in classifying ‘caries’ versus ‘sound’ with inception-v3.
CMIR: A Unified Cross-Modality Framework for Preoperative Accurate Prediction of Microvascular Invasion in Hepatocellular Carcinoma
Authors: Liu Jing , Ai Yang , Huang Chao , Wang Fang , Xu Yingying , Kitrungrotsaku Titinunt , Ma Jing , Lin Lanfen , Chen Yen-Wei , Li Jingsong
Published: January 2024

Microvascular invasion of HCC is an important factor affecting postoperative recurrence and prognosis of patients. Preoperative diagnosis of MVI is greatly significant to improve the prognosis of HCC. Currently, the diagnosis of MVI is mainly based on the histopathological examination after surgery, which is difficult to meet the requirement of preoperative diagnosis. Also, the sensitivity, specificity and accuracy of MVI diagnosis based on a single imaging feature are low. In this paper, a robust, high-precision cross-modality unified framework for clinical diagnosis is proposed for the prediction of microvascular invasion of hepatocellular carcinoma. It can effectively extract, fuse and locate multi-phase MR Images and clinical data, enrich the semantic context, and comprehensively improve the prediction indicators in different hospitals. The state-of-the-art performance of the approach was validated on a dataset of HCC patients with confirmed pathological types. Moreover, CMIR provides a possible solution for related multimodality tasks in the medical field.
Readmission Risk Based on Debility and Psychosocial Measures: The Western 9 Algorithm
Authors: Good Norm , Nelson Craig , Plant Jason , Barnes Kirsty , Grant Catherine
Published: January 2024

Many studies have stressed the importance of psychosocial determinants of health such as food insecurity, housing instability, and education level, as important drivers of health outcomes. Western Health developed a set of nine screening questions (the Western 9) based on psychosocial measures to profile patients enrolled in the HealthLinks Chronic Care initiative. An aggregate score was then converted into a Low, Medium and High risk profiles. The aim of the study was to see if the Western 9 questions added additional discriminative power to existing risk of readmission algorithms. Results show that the inclusion of the risk profiles significantly improved model fit and calibration compared to a baseline risk model. Suggestions for further refinement include developing weighted indices for the Western 9 to improve model fit.
Comparing Predictive Performance of Time Invariant and Time Variant Clinical Prediction Models in Cardiac Surgery
Authors: Jenkins David A. , Martin Glen P. , Sperrin Matthew , Brown Benjamin , Kimani Linda , Grant Stuart , Peek Niels
Published: January 2024

Clinical prediction models are increasingly used across healthcare to support clinical decision making. Existing methods and models are time-invariant and thus ignore the changes in populations and healthcare practice that occur over time. We aimed to compare the performance of time-invariant with time-variant models in UK National Adult Cardiac Surgery Audit data from Manchester University NHS Foundation Trust between 2009 and 2019. Data from 2009-2011 were used for initial model fitting, and data from 2012-2019 for validation and updating. We fitted four models to the data: a time-invariant logistic regression model (not updated), a logistic model which was updated every year and validated it in each subsequent year, a logistic regression model where the intercept is a function of calendar time (not updated), and a continually updating Bayesian logistic model which was updated with each new observation and continuously validated. We report predictive performance over the complete validation cohort and for each year in the validation data. Over the complete validation data, the Bayesian model had the best predictive performance.
Using Google Analytics with Health Information-Seeking Model to Evaluate the Design of Health Information Websites
Authors: Pang Patrick Cheong-Iao , Munsie Megan , Chang Shanton
Published: January 2024

Health information websites can be useful for information seekers, and their design is crucial for the success of accessing the needed information. While web analytical tools (e.g. Google Analytics) used by such websites can provide descriptive measures of users, there is a disconnection between this data and the current understanding of health information-seeking behaviour. In this work, we leverage a theoretical model to interpret the Google Analytics data. Drawn on the visualisation of user behaviours based on this model, our research shows that better website design can be informed, and the evaluation of health websites can be performed on the basis of different user profiles.
Rare Diseases in Citizen Science – Preliminary Experiences in Developing a Personal Health App
Authors: Schaaf Jannik , Khouri Andreas , Zerr Thomas , Scheidt Jörg , Neff Michaela , Storf Holger
Published: January 2024

SelEe is a German citizen science project aiming to develop a smartphone app for a patient-managed record. The goal is to study rare diseases with the support of interested citizens and people affected by rare diseases. We established a core research team, including professional researchers (leading the project) and citizens. Citizens have the opportunity to discuss the progress, make suggestions regarding the app’s design and data entry and contribute to the dissemination of the project. To gather feedback and experiences from the core research team, we performed an online questionnaire regarding the topics “influence and communication”, “improvements and learning effect”, and “satisfaction”. Finally, 9 citizens of the core research team participated. The results show that the citizens are very satisfied with the design of the app, their participation opportunities and the communication in the project.
Lessons Learned from an Interprofessional European Summer School in Health Informatics
Authors: Hübner Ursula H. , Vieira-Marques Pedro , Hüsers Jens , Haukkakallio Tiina , Ikonen Johanna , Egbert Nicole , Almeida Joao , Babitsch Birgit , Kinnunen Ulla-Mari , Correia Ricardo , Saranto Kaija
Published: January 2024

The aim of this European interprofessional Health Informatics (HI) Summer School was (i) to make advanced healthcare students familiar with what HI can offer in terms of knowledge development for patient care and (ii) to give them an idea about the underlying technical and legal mechanisms. According to the students’ evaluation, interprofessional education was very well received, problem-based learning focussing on cases was rated positively and the learning goals were met. However, it was criticised that the online material provided was rather detailed and comprehensive and could have been a bit overcharging for beginners. These drawbacks were obviously compensated by the positive experience of working in international and interprofessional groups and a generally welcoming environment.
Simulating Telemedicine, Medication Reconciliation, and Social Determinants: A Novel Instructional Approach to Health Systems Competencies
Authors: Lesselroth Blake , Monkman Helen , Liew Andrew , Palmer Ryan , Crosby Kimberly , Kelly Deirdra , Kollaja Liz , Ijams Shannon , Rodriguez Kristin , Homco Juell , Wen Frances
Published: January 2024

While medication reconciliation is necessary to reduce errors, it is often challenging to gather an accurate history in the clinic. Telemedicine offers a relative advantage over clinic and hospital-based interviews by enabling the clinician to inspect the home environment, review pill bottles, and identify social determinants affecting adherence, such as financial instability. To be effective, however, clinicians must be trained in best-practice interview methods and the proper use of telemedicine. There is very little information in the literature describing the best strategies for teaching students or measuring competencies in telemedicine. Therefore, we created an educational module with a telemedicine simulation and an evaluation rubric. We piloted this module with 48 medical and physician assistant students. Most students could complete a virtual interview and gather a medication history. However, only half identified an over-the-counter medication missing from the list. Most students were either entrustable or approaching entrustment in the six telemedicine competencies measured in this simulation. This simulation is valuable for teaching students about medication reconciliation, using telemedicine to close gaps in access to care, and identifying health-related social needs affecting medication adherence.
The Australian Health Informatics Competency Framework: Conceptual Design, Framework Development, and Certification Delivery
Authors: Brommeyer Mark , Bichel-Findlay Jen , Tarabay Tanija , Schaper Louise , O’Driscoll David , Butler-Henderson Kerryn
Published: January 2024

The Australian Health Informatics Competency Framework (AHICF) guides the healthcare workforce in identifying the required competencies to perform as a health informatician, and more definitively defines the foundational body of knowledge on which the discipline is based. The aim of this paper is to describe the conceptual foundations in developing the AHICF v1.0, detail the methods used to revise and publish AHICF v2.0, and explore the certification and workforce outcomes achieved. This paper contributes to the competency framework and certification discourse, and knowledge of the increasing importance and recognition of health informaticians through certification. Further, implications for workforce training and education, career advancement and recruitment strategies, are also discussed.
User Perceptions and Use of Decision Support Medical Apps Among Medical Students: Cross-Sectional Study
Authors: Aldekhyyel Raniah , Almulhem Jwaher , Binkheder Samar , Almulhem Manahel , Mohamed Eman , Aldekhyyel Shahad , Alqahtani Reem , Rajamani Sripriya
Published: January 2024

We aimed to assess medical students’ use of decision-support medical apps and evaluate their perception of app use. A cross-sectional multi-center observational study was conducted among medical students with and without a medical informatics course as part of their undergraduate medical curriculum. We assessed trust, perceptions, patient impression, reliability, and comfort using an online survey. A total of 439 responses were received. There were significant differences between the two groups when indicating which apps, they trust. Students agreed that using apps enhanced knowledge (91%), saved time (88%), improved patient care (85%), and increased diagnostic accuracy (82%). Students indicated that patients would think that students didn’t know what they were doing (63%) or students were fresh out of training (53%) when using apps in the presence of patients. Incorporating medical app usage as part of learning may increase trust and comfort with using medical apps in medical practice.
Health Informatics Workforce in the Digital Health Ecosystem
Authors: Meehan Rebecca
Published: January 2024

Workforce development needs to align with the healthcare data ecosystem emerging from digital transformation in healthcare. Careers for health informaticists are emerging as translational agents between clinicians and data scientists. Digital tools and mechanisms in healthcare, through electronic health records (EHR), devices, capabilities including artificial intelligence (AI), machine learning (ML), interoperability and health information exchange (HIE) allow clinicians and stakeholders to capture, store, access and use health data and information in ways unseen in years past, creating a new digital health ecosystem. This transformation is evolving both technologies and the strategies to influence health outcomes. Careers in health informatics are now part of this data ecosystem, and it is important to examine the current status and its implications for job seekers and for workforce development.
Developing a Framework for the Design and Deployment of Virtual Reality (VR) in Clinical Education
Authors: Moore Nathan , Ahmadpour Naseem , Davids Jennifer , Poronnik Philip , Brown Martin
Published: January 2024

The emerging cost-effective and powerful standalone VR hardware is an increasingly viable supplement to traditional clinical educational modalities. These traditional approaches are effective but can be limited by the cost of simulation infrastructure, the requirement to attend at fixed times and locations and instructor availability present challenges in meeting the needs of clinicians. One barrier facing educators looking to develop bespoke VR-based solutions is the lack of guidelines around their design, development, deployment, and evaluation. Our team has produced and deployed a number of VR-based educational applications. Through reflecting on findings from surveys, interviews, observation, we summarise a range of insights into the complexity and nuances of the clinical VR design and deployment in a framework that can inform and guide educators, clinicians and developers looking to create their own VR applications for use in healthcare.
Defining the Standard of the Terminology of the Mechanical Ventilation Data in Japan
Authors: Saito Kenichi , Kobayashi Shinji , Tsutsumi Takahiko , Takaki Shunsuke , Hashimoto Satoru
Published: January 2024

To effectively use data from intensive care unit patient information systems at multiple hospitals, it is necessary to standardize the data into a well-ordered form. However, terms often vary between devices. We designed a mechanical ventilation concept model and applied data to that model using existing tools and expert opinions. The JSICM glossary was revised based on this study.