Medical Informatics Research Articles

Perception of Health Professionals on Brain Drain in Nepalese Health Sector

Health workers are professionals who are engaged in the delivery of healthcare services to individuals. They include a wide range of roles such as doctors, nurses, midwives, paramedics, and support staff who provide clinical services to patients. They also encompass community health workers and public health professionals who work on disease prevention, health education, and improving access to healthcare. Health workers are essential for the functioning of health systems and play a critical role in ensuring the health and well-being of the population. Brain drain is increasing in Nepal day by day due to the many reasons, including the desire for better pay, work conditions, quality of life, high opportunities, more working hours, personal growth that make health workers leave. This situation is creating problem for Nepal’s health services. The main purpose of this study is to explore the perceptions of health professionals regarding brain drain in Nepal’s health sector. Through in-depth interviews and focused group discussions with various stakeholders, including doctors, nurses, pharmacist, health informatics, front line manager, the research aims to uncover the underlying factors contributing to emigration of skilled health workers. This study also examines the impact on Nepal’s healthcare system and suggest strategies to mitigate the loss of talent. This research contributes the deeper understanding of brain drain and provides insights for policy development to strengthen Nepal’s health sector.

Biomedical Information Retrieval with Positive-Unlabeled Learning and Knowledge Graphs

The rapid growth of biomedical publications has presented significant challenges in the field of information retrieval. Most existing work focuses on document retrieval given explicit queries. However, in real applications such as curated biomedical database maintenance, explicit queries are missing. In this paper, we propose a two-step model for biomedical information retrieval in the case that only a small set of example documents is available without explicit queries. Initially, we extract keywords from the observed documents using large pre-trained language models and biomedical knowledge graphs. These keywords are then enriched with domain-specific entities. Information retrieval techniques can subsequently use the collected entities to rank the documents. Following this, we introduce an iterative Positive-Unlabeled learning method to classify all unlabeled documents. Experiments conducted on the PubMed dataset demonstrate that the proposed technique outperforms the state-of-the-art positive-unlabeled learning methods. The results underscore the effectiveness of integrating large language models and biomedical knowledge graphs in improving zero-shot information retrieval performance in the biomedical domain.

IEEE Journal of Biomedical and Health Informatics Information for Authors

IEEE Journal of Biomedical and Health Informatics Information for Authors

Disparities in time to diagnosis of Radiographic Axial Spondyloarthritis.

Radiographic axial spondyloarthritis (r-axSpA) has a 7-year average diagnostic delay. While the impact of sex or gender on time to diagnosis has been evaluated, the role of social determinants of health remains understudied. We assessed whether time from initial clinical documentation of r-axSpA symptoms to r-axSpA diagnosis (diagnostic delay) varies based on sex, race, ethnicity, or the presence of social needs. We studied patients with r-axSpA from a tertiary center from 2000 to 2022. The cohort was built with the Observational Health Data Sciences and Informatics (OHDSI) network. For the primary analysis, we assessed the time from back pain/spinal pain to r-axSpA diagnosis and secondarily, the time to r-axSpA from any other r-axSpA -related condition. To estimate differences in diagnostic delay, we employed a parametric survival model, accelerated failure time. We included 404 patients (mean age 49, 37% female), with 25.5% identifying as Black, 31.1% as other or unknown race and 14.1% as Hispanic. Patients with a documented social need had a 21% increase in time from back pain to r-axSpA diagnosis (95% CI 0.93, 1.56). In patients with any r-axSpA related condition, time to diagnosis increased similarly by 21% (95% CI 0.92, 1.57). Considering an average time to diagnosis of 34 months, a social need increased time to diagnosis by 7 months. This study reveals a trend toward diagnostic delay in r-axSpA related to social need, sex, race, and ethnicity. Future studies should focus on referral strategies to enable prompt diagnosis and optimize care.

Dr Susan K. Newbold Receives the 2023 Saba Signature Award at the American Medical Informatics Association

Dr Susan K. Newbold Receives the 2023 Saba Signature Award at the American Medical Informatics Association

Cohort profile: the Nanjing Diabetes Cohort database – a population-based surveillance cohort

PurposeTo study epidemiology, complications, risk factors, clinical course and treatment patterns of diabetes, the Nanjing Diabetes Cohort (NDC) was established using anonymised electronic health records from 650 hospitals and primary...

Guest Editorial: Trustworthy Machine Learning for Health Informatics

Guest Editorial: Trustworthy Machine Learning for Health Informatics

Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review

Background: Dental care has been transformed by neural networks, introducing advanced methods for improving patient outcomes. By leveraging technological innovation, dental informatics aims to enhance treatment and diagnostic processes. Early diagnosis of dental problems is crucial, as it can substantially reduce dental disease incidence by ensuring timely and appropriate treatment. The use of artificial intelligence (AI) within dental informatics is a pivotal tool that has applications across all dental specialties. This systematic literature review aims to comprehensively summarize existing research on AI implementation in dentistry. It explores various techniques used for detecting oral features such as teeth, fillings, caries, prostheses, crowns, implants, and endodontic treatments. AI plays a vital role in the diagnosis of dental diseases by enabling precise and quick identification of issues that may be difficult to detect through traditional methods. Its ability to analyze large volumes of data enhances diagnostic accuracy and efficiency, leading to better patient outcomes. Methods: An extensive search was conducted across a number of databases, including Science Direct, PubMed (MEDLINE), arXiv.org, MDPI, Nature, Web of Science, Google Scholar, Scopus, and Wiley Online Library. Results: The studies included in this review employed a wide range of neural networks, showcasing their versatility in detecting the dental categories mentioned above. Additionally, the use of diverse datasets underscores the adaptability of these AI models to different clinical scenarios. This study highlights the compatibility, robustness, and heterogeneity among the reviewed studies. This indicates that AI technologies can be effectively integrated into current dental practices. The review also discusses potential challenges and future directions for AI in dentistry. It emphasizes the need for further research to optimize these technologies for broader clinical applications. Conclusions: By providing a detailed overview of AI’s role in dentistry, this review aims to inform practitioners and researchers about the current capabilities and future potential of AI-driven dental care, ultimately contributing to improved patient outcomes and more efficient dental practices.

Health and medical informatics research: Identifying international collaboration patterns at the country and institution level

Health and medical informatics research: Identifying international collaboration patterns at the country and institution level

Transforming the Future of Digital Health Education: Redesign of a Graduate Program Using Competency Mapping.

Digital transformation has disrupted many industries but is yet to revolutionize health care. Educational programs must be aligned with the reality that goes beyond developing individuals in their own professions, professionals wishing to make an impact in digital health will need a multidisciplinary understanding of how business models, organizational processes, stakeholder relationships, and workforce dynamics across the health care ecosystem may be disrupted by digital health technology. This paper describes the redesign of an existing postgraduate program, ensuring that core digital health content is relevant, pedagogically sound, and evidence-based, and that the program provides learning and practical application of concepts of the digital transformation of health. Existing subjects were mapped to the American Medical Informatics Association Clinical Informatics Core Competencies, followed by consultation with leadership to further identify gaps or opportunities to revise the course structure. New additions of core and elective subjects were proposed to align with the competencies. Suitable electives were chosen based on stakeholder feedback and a review of subjects in fields relevant to digital transformation of health. The program was revised with a new title, course overview, course intended learning outcomes, reorganizing of core subjects, and approval of new electives, adding to a suite of professional development offerings and forming a structured pathway to further qualification. Programs in digital health must move beyond purely informatics-based competencies toward enabling transformational change. Postgraduate program development in this field is possible within a short time frame with the use of established competency frameworks and expert and student consultation.

Integrated Knowledge Graph and Drug Molecular Graph Fusion via Adversarial Networks for Drug-Drug Interaction Prediction.

The Co-administration of multiple drugs can enhance the efficacy of disease treatment by reducing drug resistance and side effects. However, it also raises the risk of adverse drug interactions, presenting a challenging problem in healthcare. Various approaches have been developed to predict drug-drug interactions (DDIs) by leveraging both knowledge graphs and drug attribute information. While these methods have shown promise, they often fail to effectively capture correlations between biomedical information in the knowledge graph and drug properties. This work introduces a novel end-to-end DDI predictor framework based on generative adversarial networks. This framework utilizes a message-passing neural network to capture molecular structure information while employing the knowledge-aware graph attention network to capture the representation of drugs in the knowledge graph through considering the importance of different multihop neighbor nodes and relationships. The dual generative adversarial networks employ two generators and two discriminators in knowledge graph embedding and molecular topology embedding for adversarial training to capture the interrelations and complementary knowledge between molecular structure information and semantic information from the knowledge graph. comprehensive experiments have demonstrated that the proposed method outperforms state-of-the-art algorithms in binary classification, with improvements of 1.0% in accuracy, 0.45% in area under the receiver operating characteristic curve (AUC), 0.24% in area under the precision-recall curve (AUPR), and 0.98% in F1 score. Furthermore, for multiclass classification tasks, improvements were observed across various evaluation metrics, including 0.9% in accuracy, 0.25% in macro precision, 0.2% in macro recall, and 0.28% in macro F1. Additionally, ablation studies were conducted to showcase the effectiveness and robustness of our method in DDI prediction tasks.

Leveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review.

Social determinants of health (SDOH) are critical drivers of health disparities and patient outcomes. However, accessing and collecting patient-level SDOH data can be operationally challenging in the emergency department (ED) clinical setting, requiring innovative approaches. This scoping review examines the potential of AI and data science for modeling, extraction, and incorporation of SDOH data specifically within EDs, further identifying areas for advancement and investigation. We conducted a standardized search for studies published between 2015 and 2022, across Medline (Ovid), Embase (Ovid), CINAHL, Web of Science, and ERIC databases. We focused on identifying studies using AI or data science related to SDOH within emergency care contexts or conditions. Two specialized reviewers in emergency medicine (EM) and clinical informatics independently assessed each article, resolving discrepancies through iterative reviews and discussion. We then extracted data covering study details, methodologies, patient demographics, care settings, and principal outcomes. Of the 1047 studies screened, 26 met the inclusion criteria. Notably, 9 out of 26 (35%) studies were solely concentrated on ED patients. Conditions studied spanned broad EM complaints and included sepsis, acute myocardial infarction, and asthma. The majority of studies (n=16) explored multiple SDOH domains, with homelessness/housing insecurity and neighborhood/built environment predominating. Machine learning (ML) techniques were used in 23 of 26 studies, with natural language processing (NLP) being the most commonly used approach (n=11). Rule-based NLP (n=5), deep learning (n=2), and pattern matching (n=4) were the most commonly used NLP techniques. NLP models in the reviewed studies displayed significant predictive performance with outcomes, with F1-scores ranging between 0.40 and 0.75 and specificities nearing 95.9%. Although in its infancy, the convergence of AI and data science techniques, especially ML and NLP, with SDOH in EM offers transformative possibilities for better usage and integration of social data into clinical care and research. With a significant focus on the ED and notable NLP model performance, there is an imperative to standardize SDOH data collection, refine algorithms for diverse patient groups, and champion interdisciplinary synergies. These efforts aim to harness SDOH data optimally, enhancing patient care and mitigating health disparities. Our research underscores the vital need for continued investigation in this domain.

A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale

Biomedical network learning offers fresh prospects for expediting drug repositioning. However, traditional network architectures struggle to quantify the relationship between micro-scale drug spatial structures and corresponding macro-scale biomedical networks, limiting their ability to capture key pharmacological properties and complex biomedical information crucial for drug screening and therapeutic discovery. Moreover, challenges such as difficulty in capturing long-range dependencies hinder current network-based approaches. To address these limitations, we introduce the Spatial Hierarchical Network, modeling molecular 3D structures and biological associations into a unified network. We propose an end-to-end framework, SpHN-VDA, integrating spatial hierarchical information through triple attention mechanisms to enhance machine understanding of molecular functionality and improve the accuracy of virus-drug association identification. SpHN-VDA outperforms leading models across three datasets, particularly excelling in out-of-distribution and cold-start scenarios. It also exhibits enhanced robustness against data perturbation, ranging from 20% to 40%. It accurately identifies critical motifs for binding sites, even without protein residue annotations. Leveraging reliability of SpHN-VDA, we have identified 25 potential candidate drugs through gene expression analysis and CMap. Molecular docking experiments with the SARS-CoV-2 spike protein further corroborate the predictions. This research highlights the broad potential of SpHN-VDA to enhance drug repositioning and identify effective treatments for various diseases.

Enhancing real world data interoperability in healthcare: A methodological approach to laboratory unit harmonization

ObjectiveThe primary aim of this study is to address the critical issue of non-standardized units in clinical laboratory data, which poses significant challenges to data interoperability and secondary usage. Despite UCUM (Unified Code for Units of Measure) offering a unique representation for laboratory test units, nearly 60% of laboratory codes in healthcare organizations use non-standard units. We sought to design, implement and test a methodology for the harmonization of units to the UCUM standards across a large research network. MethodsUsing dimensional analysis and a curated equivalence table, the proposed methodology harmonizes disparate units to UCUM standards. The process focused on identifying and converting non-UCUM conforming units, with the goal of enhancing data comparability and interoperability across different systems. ResultsThe methodology successfully achieved over 90% coverage of laboratory data with units in UCUM standards across the TriNetX research network, a significant improvement from baseline measurements. This enhancement in unit standardization directly contributed to increased interoperability of laboratory data, facilitating more reliable and comparable data analysis across various healthcare organizations. ConclusionThe successful harmonization of laboratory data units to UCUM standards represents a significant advancement in the field of biomedical informatics. By demonstrating a practical and effective approach to overcoming the challenges of non-standardized units, our study contributes to the broader efforts to improve data interoperability and usability for secondary purposes such as research and observational studies. Future work will focus on addressing the remaining gaps in unit standardization and exploring the implications of this methodology on clinical outcomes and research capabilities.

Building bridges - Fostering collaborative education in training dental informaticians.

Dental informatics is an emerging discipline. Although the accreditation agency governing dental education programs asserts the importance of informatics as foundational knowledge, no well-defined dental informatics courses currently exist within the standard predoctoral dental curriculum. There is a nationwide lack of dental informatics academic programs. This training gap is due to a lack of qualified dental informaticians to impart knowledge on dental informatics. This paper aims to introduce a novel conceptual framework for an interdisciplinary dental informatics program in preparing students to become dental informaticians. In 2023, we developed a standalone graduate certificate program in dental informatics at Indiana University (IU) School of Dentistry in collaboration with IU Luddy School of Informatics, Computing, and Engineering and IU Fairbanks School of Public Health. Feedback was collected through online surveys to assess course quality from students who took Introduction to Health Information in Dentistry. Feedback was analyzed qualitatively, utilizing a thematic analysis approach. Common responses relevant to dental informatics education were grouped into themes. Five major themes emerged during our analysis of the students' feedback: foundational knowledge and skills; experiential learning: learning by doing; access to resources and working on clinical information systems; health promotion through team-based learning; and retention of knowledge assessment and application. A conceptual framework was formulated through these themes as a guideline for future program improvement. This interdisciplinary educational program framework showed how students and faculty from various disciplines could collaborate, learn from each other, and bring in expertise from different domains. The collaboration happens in clinical, laboratory, and virtual settings to acquire hands on learning through practice and research projects. The developed conceptual framework aligned with the interdisciplinary nature of dental informatics. It can potentially be adopted by other interdisciplinary informatics programs in health and non-health care disciplines.

Improving Biomedical Science Literacy and Patient-Directed Knowledge of Tuberculosis (TB): A Cross-Sectional Infodemiology Study Examining Readability of Patient-Facing TB Information.

Tuberculosis (TB) continues be the leading cause of death globally due to an infectious agent. There is a paucity of data describing the readability of patient-facing TB information for service users. The aim of this study was to calculate the readability of multiple global TB information sources. Information on tuberculosis (n = 150 sources) included nine categories, Patient-facing information: WHO publications (n = 17), International governments (n = 19), Hospitals (n = 10), Non-government organisations (NGOs)/charities (n = 20), Cochrane Plain Language Summaries (n = 20); LabTestsOnlineUK (n = 4) and Scientific-facing information: Clinical trials (n = 20), Cochrane abstracts (n = 20), Scientific abstracts (n = 20). Readability was calculated using Readable software, defined by (i) Flesch Reading Ease (FRE), (ii) Flesch-Kincaid Grade Level (FKGL), (iii) Gunning Fog Index and (iv) SMOG Index and two text metrics [words/sentence, syllables/word]. Mean readability values for TB information for the FRE and FKGL were 35.6 ± 1.6 (standard error of mean (SEM)) (US Target ≥60; UK Target ≥90) and 12.3 ± 0.3 (US Target ≤8; UK Target ≤6), respectively, with mean words per sentence and syllables per word of 17.2 and 1.8, respectively. Cochrane Plain Language Summaries had similar readability scores to their matching scientific abstract (p = 0.15). LabTestsOnlineUK yielded a mean FRE score of 51.5 ± 1.2, a mean FKGL score of 10.2 ± 0.5 and text metric scores of 16.7 ± 2.3 and 1.6, for words per sentence and syllables per word, respectively. In descending order, TB information from international governments, hospitals and LabTestsOnlineUK were the most readable (FRE = 57.9, 54.1 and 51.5, respectively), whereas scientific abstracts and Cochrane abstracts were the most difficult to read (13.0 and 30.2, respectively). Patient-facing TB information analysed had poor readability. Effective communication of biomedical science concepts and information relating to TB is vital for service users to enhance their health literacy of tuberculosis, thereby promoting better clinical outcomes. Biomedical scientists are important custodians of scientific information for their service user populations, including other healthcare professionals within the TB multidisciplinary (MDT) team and patient service users. When preparing TB information, this should be checked and modified in real time employing readability calculators, to align with health readability targets.

A Culturally Sensitive Mobile App (DiaFriend) to Improve Self-Care in Patients With Type 2 Diabetes: Development Study.

Mobile apps designed with cultural sensitivity have demonstrated higher user acceptability and greater effectiveness in enhancing self-care skills. However, a significant gap exists in developing such apps for specific populations, such as Portuguese Americans living in southern Massachusetts, home to the second-largest Portuguese community in the United States. This group possesses unique cultural traditions, particularly in dietary practices, including a tendency toward high carbohydrate intake. Tailoring diabetes self-care apps to address these specific cultural requirements could substantially improve diabetes management within this population. The aim of this app development project was to develop a prototype diabetes management app for Portuguese Americans with type 2 diabetes mellitus using the design thinking methodology, incorporating user-centered design principles and cultural sensitivity. This paper describes the phase-2 results, focusing on app design and development. Phase 2 of this app development project adhered to the design thinking methodology delineated by the Hasso Plattner Institute of Design at Stanford University, focusing on 2 critical steps: ideation and prototyping. This phase started in March 2022 and continued until April 2024. The project was driven by a multidisciplinary team consisting of 2 nurse educators; an app development specialist; and 2 graduate research assistants from the university's Computer and Information Sciences Department, both well-versed in mobile app development. Data collected during phase 1, which will be published separately, informed the app design and development process. The prototype of the DiaFriend app (version 1) was designed and developed. The app comprises five features: (1) blood glucose monitoring, (2) weight tracking, (3) carbohydrate tracking, (4) exercise log, and (5) medication reminder. The carbohydrate tracking feature was explicitly tailored to correspond to Portuguese food culture. This paper presents the front-end interface flowchart, demonstrating how the user navigates through each screen. It also discusses the challenges faced during the backend development, such as data not being able to be stored and retrieved. The DiaFriend app (version 1) distinguishes itself from conventional diabetes self-care apps through its emphasis on cultural sensitivity. The development of this app underscores the importance of cultural considerations in health informatics. It establishes a foundation for future research in developing and evaluating culturally sensitive mobile health apps. The adaptation of such technologies has the potential to enhance self-care practices among Portuguese Americans with type 2 diabetes mellitus, with improved glycated hemoglobin levels as a potential outcome. The last step of the design thinking methodology, testing the app, will be conducted in phase 3 and the results will be published elsewhere.

Medical Team Practices and Interpreter Alterations on Family-Centered Rounds.

Robust evidence demonstrates inequities in communication during family-centered rounds for families who use a language other than English (LOE) for health care. This study aimed to characterize the type of interpreter alterations occurring on family-centered rounds and identify medical team communication practices associated with alterations. In this observational study of interpreter-supported family-centered rounds, we recorded and transcribed family-centered rounds encounters for Spanish-speaking families. We assessed measures of medical team communication behaviors and interpreter alterations (omissions, additions, and substitutions) using previously described instruments. We used a content analysis approach to apply defined codes to each interpreted segment and to characterize the nature of interpreter alterations. We assessed the association between medical team communication behaviors and interpreter alterations using χ2 tests. We recorded, transcribed, and coded 529 interpreted segments of 10 family-centered rounds encounters. At least 1 alteration was present in 72% (n = 382/529) of interpreted segments. Omissions were the most common alteration (n = 242/529, 46%) followed by substitutions (n = 177/529, 34%) and additions (n = 71/529, 13%). Interpretation resulted in a potentially negative alteration in 29% (n = 155/529) and a positive alteration in 9% (n = 45/529) of segments. Greater number of sentences in the segment preceding interpretation was associated with an increase in loss of information (P < .001), loss of social support (P = .003), and loss of partnership (P = .020). To improve communication with families that use an LOE, medical teams must abide by best practices for using an interpreter such as frequent pausing to prevent loss of both biomedical and psychosocial information.

2. Measurement Division, Diagnostic Imaging Division, Medical Informatics Division, Radiation Protection Division

2. Measurement Division, Diagnostic Imaging Division, Medical Informatics Division, Radiation Protection Division

Biomedical relation extraction method based on ensemble learning and attention mechanism

BackgroundRelation extraction (RE) plays a crucial role in biomedical research as it is essential for uncovering complex semantic relationships between entities in textual data. Given the significance of RE in biomedical informatics and the increasing volume of literature, there is an urgent need for advanced computational models capable of accurately and efficiently extracting these relationships on a large scale.ResultsThis paper proposes a novel approach, SARE, combining ensemble learning Stacking and attention mechanisms to enhance the performance of biomedical relation extraction. By leveraging multiple pre-trained models, SARE demonstrates improved adaptability and robustness across diverse domains. The attention mechanisms enable the model to capture and utilize key information in the text more accurately. SARE achieved performance improvements of 4.8, 8.7, and 0.8 percentage points on the PPI, DDI, and ChemProt datasets, respectively, compared to the original BERT variant and the domain-specific PubMedBERT model.ConclusionsSARE offers a promising solution for improving the accuracy and efficiency of relation extraction tasks in biomedical research, facilitating advancements in biomedical informatics. The results suggest that combining ensemble learning with attention mechanisms is effective for extracting complex relationships from biomedical texts. Our code and data are publicly available at: https://github.com/GS233/Biomedical.