Medical Informatics Research Articles

A mobile serious game about diabetes self-management: Design and evaluation

Type 2 Diabetes Mellitus (T2DM) is a chronic condition that requires ongoing self-management and education. In recent years, there has been a growing interest in utilizing mobile serious games as a tool for patient education and engagement. This article presents the development of DiaPo, a mobile serious game designed to improve self-management education for patients with T2DM. DiaPo integrates gamification techniques to increase patient engagement and motivation while providing essential information about disease management. The development of DiaPo followed a structured design process, utilizing the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) educational system. This systematic approach allowed for the integration of best practices in educational game design and diabetes care. The development team consisted of experts in medical informatics, game design, and diabetes care, ensuring a multidisciplinary approach to the game's creation. The game's narrative focuses on a T2DM patient who earns positive points for making healthy lifestyle choices and negative points for poor ones. This gamified approach aims to reinforce positive behaviors and provide immediate feedback on negative ones. Interactive animations confirm or deny options selected by the player, further enhancing the learning experience. DiaPo offers a flexible and adaptable platform suitable for diverse audiences, promoting inclusiveness and accessibility in T2DM education. DiaPo represents a novel approach to self-management education for patients with T2DM, utilizing gamification techniques and a multidisciplinary design process to create an engaging and informative mobile serious game. By promoting inclusiveness and accessibility, DiaPo has the potential to empower patients with T2DM to take an active role in their disease management. As the field of mobile serious games continues to evolve, DiaPo stands as a promising tool for improving T2DM education and patient outcomes.

The dynamics of morbidity of adult population of The Russian Federation by diseases of ear and mastoid in 2018-2022

The purpose of the study is to assess dynamics of level of common and primary morbidity in class of diseases of ear and mastoid in adult population of the Russian Federation in 2018-2022. The analysis of morbidity indicators was carried out on the basis of data from statistical collections of Minzdrav of Russia and the Federal State Budget Institution "The Central Research Institute for Health Organization and Informatics of Minzdrav of Russia". It is established that in the Russian Federation, level of common and primary morbidity in class of diseases of ear and mastoid in adult population in 2018-2022 was decreasing and reached its lowest values in 2022 (2875.3 and 1760.8 cases per 100,000 of population respectively). The similar trend is observed in all Federal Okrugs of the Russian Federation. The increasing of level of common morbidity is established in 18 Subjects of the Russian Federation, with maximal increase in the Ivanovo Oblast (+17.0%). The increase of level of primary morbidity was noted in 21 Subjects of the Russian Federation, with maximal increase in the Penza Oblast (+35.0%). The most of Subjects of the Russian Federation are characterized by decreasing of both level of common morbidity with maximal decreasing the Republic of Adygei (-48.0%) and primary morbidity of adult population in class of diseases of ear and mastoid with maximal decreasing in the Republic of Tuva (-54.0%). The presented trends of indicators of morbidity in class of diseases of ear and mastoid are results of complex and multi-factorial processes. heir identification will permit to develop measures improving otorhinolaryngological care of population.

Clinical and translational mode of single-cell measurements: An artificial intelligent single-cell.

With rapid development and mature of single-cell measurements, single-cell biology and pathology become an emerging discipline to understand the disease. However, it is important to address concerns raised by clinicians as to how to apply single-cell measurements for clinical practice, translate the signals of single-cell systems biology into determination of clinical phenotype, and predict patient response to therapies. The present Perspective proposes a new system coined as the clinical artificial intelligent single-cell (caiSC) with the dynamic generator of clinical single-cell informatics, artificial intelligent analyzers, molecular multimodal reference boxes, clinical inputs and outs, and AI-based computerization. This system provides reliable and rapid information for impacting clinical diagnoses, monitoring, and prediction of the disease at the single-cell level. The caiSC represents an important step and milestone to translate the single-cell measurement into clinical application, assist clinicians' decision-making, and improve the quality of medical services. There is increasing evidence to support the possibility of the caiSC proposal, since the corresponding biotechnologies associated with caiSCs are rapidly developed. Therefore, we call the special attention and efforts from various scientists and clinicians on the caiSCs and believe that the appearance of the caiSCs can shed light on the future of clinical molecular medicine.

Abstract P408: Association of Social Risk Factors with Cardiovascular-Kidney-Metabolic Syndrome at Baseline in Youth with Hypertension Disorders: A SUPERHERO Analysis

Poor cardiovascular-kidney-metabolic (CKM) health is associated with an increased risk for cardiovascular disease events and cardiovascular mortality in adults. There is a disproportionate burden of CKM syndrome among adults with social risk factors, and the presence of social risk factors can contribute to health and healthcare disparities in patients with CKM syndrome. Nothing is known about the association of social risk factors with CKM syndrome in youth with hypertension disorders. We conducted a cross-sectional analysis of baseline data from the Study of the Epidemiology of Pediatric Hypertension (SUPERHERO), a retrospective Registry of electronic health record data obtained using standardized and validated biomedical informatics scripts. Inclusion criteria were youth <19 years old who received care from subspecialists for hypertension disorders from 1/1/2026–12/31/2023 defined by ICD-10 codes. Exclusion criteria were kidney failure on dialysis, kidney transplantation, or pregnancy by ICD-10 codes. The outcome was CKM syndrome defined by ICD-10 codes and included no risk, stage 1 and stage 2 CKM syndrome; for this analysis we excluded those who were unclassified ( n =630, 7%). Exposure was presence of at least one social risk factor defined by ICD-10 codes and included food access, transportation, housing, income, education, employment, family, and crime. We used directed acyclic graph-informed bivariate generalized linear models; no biasing paths were present so no additional covariates were included. Of 8,726 participants, the mean age was 12.8 years ( SD 4.5) and 62% were male. CKM syndrome stage 1 prevalence was 20% and CKM stage 2 was 65%. Seventy-four (0.8%) youth had at least one social risk factor. Presence of social risk factors was not associated with CKM syndrome (OR 0.9, 95% CL 0.6 to 1.5). Among youth referred for hypertension disorders, we found that those with social risk factors were not more likely to have CKM syndrome. Given the low prevalence of social risk factors by ICD-10 codes only in this early SUPERHERO phase, there is a critical need for a transdisciplinary approach to more comprehensively screen for and identify social risk factors. Further investigation into this important topic may inform risk stratification based on presence of social risk factors and targeted interventions to improve CKM health. Future SUPERHERO analysis will investigate the impact of interventions addressing social risk factors on CKM health outcomes.

Barriers encountered with clinical data warehouses: Recommendations from a focus group

Background and ObjectiveThe increasing implementation and use of electronic health records over the last few decades has made a significant volume of clinical data being available. Over the past 20 years, hospitals have also adopted and implemented data warehouse technology to facilitate the reuse of administrative and clinical data for research. However, the implementation of clinical data warehouses encounters a set of barriers: ethical, legislative, technical, human and organizational. This paper proposes an overview of difficulties and barriers encountered during a clinical data warehouse (CDW) development and implementation project. MethodsWe conducted a focus group at the 2023 Medical Informatics Europe Conference and invited professionals involved in the implementation of CDW. These experts described their CDW and the difficulties and barriers they encountered at each phase: (i) launching of the data warehouse project, (ii) implementing the data warehouse and (iii) using a data warehouse in routine operations. They were also asked to propose solutions they were able to implement to address the barriers previously reported. ResultsAfter synthesis and consensus, a total of 26 barriers were identified, 10 pertained to tasks, 5 to tools and technologies, 4 to persons, 4 to organization, and 3 to the external environment. To address these challenges, a set of 15 practical recommendations was offered, covering essential aspects such as governance, stakeholder engagement, interdisciplinary collaboration, and external expertise utilization. ConclusionsThese recommendations serve as a valuable resource for healthcare institutions seeking to establish and optimize CDWs, offering a roadmap for leveraging clinical data for research, quality enhancement, and improved patient care.

Preliminary findings from the Delirium and Population Health Informatics Cohort (DELPHIC) - Qatar Study.

Preliminary findings from the Delirium and Population Health Informatics Cohort (DELPHIC) - Qatar Study.

Technology for a Food-secure Future: A Review of Technology Advances in Sustainable Agriculture

There is no doubt that advanced technologies play a very important and crucial role in driving the agricultural sustainable practices in order to satisfy the increasing demand for food production as well as to minimize environmental impacts. This review article aims to provide comprehensive points of view regarding a current state as well as the future advances in smart farming techniques for developing an agricultural sustainability. The digital sensors are considered as key techniques for agricultural transformation which provide an accurate managing and monitoring the various agricultural activities such as crop production, soil moisture, micro climate data, and others. Valuable detailed data are acquired by these sensors to be delivered to the farmers in order to make suitable decisions, ensuring the sustainable and efficient utilization of the agricultural resources. Moreover, managing irrigation using the advanced technologies is benefited wireless monitoring remotely and controlling systems. These technologies and advanced tools help the farmers to reduce water consumption and increase their benefits as well as promotes sustainable management practices of the water resources. Additionally, the drones such as unmanned aerial vehicles (UAVs) can be attached with several kinds of sensors or cameras for capturing detailed information of crop health, soil status, required fertilizers, irrigation, and pesticides. Besides these benefits of drones, they provide a function of an early detection of agricultural problems which allow the decision makers for taking suitable actions. Furthermore, the biotechnology advances such as CRISPR gene editing, and transgenic animals’ developing are very beneficial for enhancing the crop yield, disease resistance, and nutrients availability, and agricultural sustainability. The precision agriculture (PA) integration with geographic information system (GIS) and remote sensing (RS) provides site-specific management and its decisions in order to optimize the inputs utilization, waste reduce, and sustainable practices promotion. Also, advanced technologies’ application (i.e. artificial intelligence ‘AI’, machine learning ‘ML’, and the Internet of Things ‘IoT’) has been found to be a promising for achieving an agricultural sustainability. Therefore, by using these powerful technologies, the farmers can enhance an agricultural production, decrease an environmental effect and achieve the food security.

Special Issue on Informatics Education: < Integrating Diversity, Equity, Inclusion, and Accessibility into a Data Storytelling Model for Health Informatics Education >.

Health informatics education is pivotal in integrating diversity, equity, inclusion, and accessibility (DEIA) principles into curricula and leveraging data with equity considerations. Integrating clinically driven data with other datasets is crucial to comprehensive understanding of patient care demographics, experiences, and outcomes to create equity-minded data storytelling. Publicly available Healthy People 2030 (HP2030) resources complement academic EHRs, supporting tailored learning activities in informatics education to enhance educational utility through a DEIA lens. This case report describes the expansion of an existing DEI checklist to an updated DEIA checklist for preparing future informaticians to collect and critically evaluate DEIA features using this checklist in creating equity-minded data storytelling. An equity-minded data storytelling model and the HP2030 framework were utilized to develop the DEIA checklist. We employed an informal cognitive walkthrough to expand the DEIA checklist and evaluate the DEIA measures or characteristics within datasets from the HP2030 social determinants of health (SDOH) 5 topics using this checklist. We reviewed 76 available SDOH-related datasets and added 6 measures to "demographics" and 7 to "skills, abilities, & accessibility" of the DEIA checklist. Our evaluation of the DEIA checklist verified HP2030's inclusion of all measures, except "religions/beliefs." All DEIA measures were linked to equity and accessibility, 1 in inclusion, and the inclusion of 3 characteristics comprising the category "language" and 6 characteristics comprising the category "images." Results highlighted the accessibility and comprehensiveness of HP2030 demographic data resources, considering SDOH factors and promoting inclusive data representation to address health disparities. The DEIA checklist provides a structured tool in facilitating unbiased data collection and visualization of SDOH-related data in data storytelling through an equity-informed lens. Integrating an equity-minded data storytelling with frameworks like HP2030 enriches health informatics education, broadens students' understanding of health disparities, and supports evidence-based interventions for improved health outcomes.

Special Issue on Informatics Education: Teaching Data Science through an Interactive, Hands-On Workshop with Clinically-Relevant Case Studies.

In this case report, we describe the development of an innovative workshop to bridge the gap in data science education for practicing clinicians (and particularly nurses). In the workshop, we emphasize the core concepts of machine learning and predictive modeling to increase understanding among clinicians. Addressing the limited exposure of healthcare providers to leverage and critique data science methods, this interactive workshop aims to provide clinicians with foundational knowledge in data science, enabling them to contribute effectively to teams focused on improving care quality. The workshop focuses on meaningful topics for clinicians, such as model performance evaluation and introduces machine learning through hands-on exercises using free, interactive python notebooks. Clinical case studies on sepsis recognition and opioid overdose death provide relatable contexts for applying data science concepts. Positive feedback from over 300 participants across various settings highlights the workshop's effectiveness in making complex topics accessible to clinicians. Our approach prioritizes engaging content delivery and practical application over extensive programming instruction, aligning with adult learning principles. This initiative underscores the importance of equipping clinicians with data science knowledge to navigate today's data-driven healthcare landscape, offering a template for integrating data science education into healthcare informatics programs or continuing professional development.

Automatic extraction of transcriptional regulatory interactions of bacteria from biomedical literature using a BERT-based approach.

Transcriptional regulatory networks (TRNs) give a global view of the regulatory mechanisms of bacteria to respond to environmental signals. These networks are published in biological databases as a valuable resource for experimental and bioinformatics researchers. Despite the efforts to publish TRNs of diverse bacteria, many of them still lack one and many of the existing TRNs are incomplete. In addition, the manual extraction of information from biomedical literature ("literature curation") has been the traditional way to extract these networks, despite this being demanding and time-consuming. Recently, language models based on pretrained transformers have been used to extract relevant knowledge from biomedical literature. Moreover, the benefit of fine-tuning a large pretrained model with new limited data for a specific task ("transfer learning") opens roads to address new problems of biomedical information extraction. Here, to alleviate this lack of knowledge and assist literature curation, we present a new approach based on the Bidirectional Transformer for Language Understanding (BERT) architecture to classify transcriptional regulatory interactions of bacteria as a first step to extract TRNs from literature. The approach achieved a significant performance in a test dataset of sentences of Escherichia coli (F1-Score: 0.8685, Matthew's correlation coefficient: 0.8163). The examination of model predictions revealed that the model learned different ways to express the regulatory interaction. The approach was evaluated to extract a TRN of Salmonella using 264 complete articles. The evaluation showed that the approach was able to accurately extract 82% of the network and that it was able to extract interactions absent in curation data. To the best of our knowledge, the present study is the first effort to obtain a BERT-based approach to extract this specific kind of interaction. This approach is a starting point to address the limitations of reconstructing TRNs of bacteria and diseases of biological interest. Database URL: https://github.com/laigen-unam/BERT-trn-extraction.

Health Promotion and Disease Prevention Through Pharmacy Interventions

The rapidly developing discipline of health informatics offers enormous potential to enhance the prompt administration and high-quality services provided by the healthcare system. One invention that has significantly altered the world is information technology. Health informatics is the application of practical information technology features to healthcare practices with the goal of improving patient outcomes. It significantly facilitates information sharing amongst medical experts. In clinical practice, having reliable, evidence-based data readily available is really beneficial. There are several facets to health informatics. The first is the keeping of medical records. This includes computerised prescribing and electronic management of medical records. It is beneficial to have quick access to all of a patient's medical records. Health experts' time can be greatly saved by this. The availability of a sizable patient population's health data, which may be used for population-based investigations and research, is another benefit. A component of health informatics, clinical decision support systems (also known as CDSS) assist medical professionals, particularly physicians, in making decisions regarding patients throughout their regular practice. They are created as applications or software to be utilised in patient care.

Attracting doctors to rural areas under the state program: performance indices

Introduction.The implementation of the state program “Zemsky Doctor” is one of the effective mechanisms to attract medical personnel to medical institutions located in rural areas. The purpose of the study. To analyze the indices for attracting doctors to medical institutionsss located in rural areas of the Russian Federation during Federal Research Institute for Health Organization and Informatics the period from 2015 to 2022 within the framework of the state program “Zemsky Doctor”. Materials and methods. The study used statistical data on eighty two subjects of the Russian Federation based on operational documented inquiries for the period from 2015 to 2022. Analytical and statistical methods were used in the research process. Descriptive statistics and correlation analysis was conducted using the Statistica package version 13.3. Results. During the study period, more than 33,000 physicians of various specialties were attracted and employed in rural areas. The index of the number of doctors attracted to rural areas under the program from 2015 to 2022 increased by 32.2%, from 0.9 to 1.19 per 10,000 rural population. When analyzing this index by regions of the Russian Federation, significant differences were revealed. Research limitations. The research materials are limited by the results of the analysis of quantitative data obtained as a result of an operational request to the subjects of the Russian Federation and statistical data of the forms of federal statistical observation. Conclusion. The study revealed the regions with the lowest level of the indice of physician supply also to have a low index of the number of doctors recruited under the program. The results of correlation analysis showed that in most of the Russian regions there is a direct dependence of the index of physician supply on the results of the implementation of the program “Zemsky Doctor”. The results of the program implementation have a significant impact on the staffing of medical organizations located in rural areas.

SSGU-CD: A combined semantic and structural information graph U-shaped network for document-level Chemical-Disease interaction extraction

Document-level interaction extraction for Chemical-Disease is aimed at inferring the interaction relations between chemical entities and disease entities across multiple sentences. Compared with sentence-level relation extraction, document-level relation extraction can capture the associations between different entities throughout the entire document, which is found to be more practical for biomedical text information. However, current biomedical extraction methods mainly concentrate on sentence-level relation extraction, making it difficult to access the rich structural information contained in documents in practical application scenarios. We put forward SSGU-CD, a combined Semantic and Structural information Graph U-shaped network for document-level Chemical-Disease interaction extraction. This framework effectively stores document semantic and structure information as graphs and can fuse the original context information of documents. Using the framework, we propose a balanced combination of cross-entropy loss function to facilitate collaborative optimization among models with the aim of enhancing the ability to extract Chemical-Disease interaction relations. We evaluated SSGU-CD on the document-level relation extraction dataset CDR and BioRED, and the results demonstrate that the framework can significantly improve the extraction performance.

Deceptively Simple yet Profoundly Impactful: Text Messaging Interventions to Support Health.

This paper examines the use of text message (SMS) interventions for health-related behavioral support. It first outlines the historical progress in SMS intervention research publications and the variety of funds from US government agencies. A narrative review follows, highlighting the effectiveness of SMS interventions in key health areas, such as physical activity, diet and weight loss, mental health, and substance use, based on published meta-analyses. It then outlines advantages of text messaging compared to other digital modalities, including the real-time capability to collect information and deliver microdoses of intervention support. Crucial design elements are proposed to optimize effectiveness and longitudinal engagement across communication strategies, psychological foundations, and behavior change tactics. We then discuss advanced functionalities, such as the potential for generative artificial intelligence to improve user interaction. Finally, major challenges to implementation are highlighted, including the absence of a dedicated commercial platform, privacy and security concerns with SMS technology, difficulties integrating SMS interventions with medical informatics systems, and concerns about user engagement. Proposed solutions aim to facilitate the broader application and effectiveness of SMS interventions. Our hope is that these insights can assist researchers and practitioners in using SMS interventions to improve health outcomes and reducing disparities.

Assessing the Effect of Electronic Health Record Data Quality on Identifying Patients With Type 2 Diabetes: Cross-Sectional Study.

Increasing and substantial reliance on electronic health records (EHRs) and data types (ie, diagnosis, medication, and laboratory data) demands assessment of their data quality as a fundamental approach, especially since there is a need to identify appropriate denominator populations with chronic conditions, such as type 2 diabetes (T2D), using commonly available computable phenotype definitions (ie, phenotypes). To bridge this gap, our study aims to assess how issues of EHR data quality and variations and robustness (or lack thereof) in phenotypes may have potential impacts in identifying denominator populations. Approximately 208,000 patients with T2D were included in our study, which used retrospective EHR data from the Johns Hopkins Medical Institution (JHMI) during 2017-2019. Our assessment included 4 published phenotypes and 1 definition from a panel of experts at Hopkins. We conducted descriptive analyses of demographics (ie, age, sex, race, and ethnicity), use of health care (inpatient and emergency room visits), and the average Charlson Comorbidity Index score of each phenotype. We then used different methods to induce or simulate data quality issues of completeness, accuracy, and timeliness separately across each phenotype. For induced data incompleteness, our model randomly dropped diagnosis, medication, and laboratory codes independently at increments of 10%; for induced data inaccuracy, our model randomly replaced a diagnosis or medication code with another code of the same data type and induced 2% incremental change from -100% to +10% in laboratory result values; and lastly, for timeliness, data were modeled for induced incremental shift of date records by 30 days to 365 days. Less than a quarter (n=47,326, 23%) of the population overlapped across all phenotypes using EHRs. The population identified by each phenotype varied across all combinations of data types. Induced incompleteness identified fewer patients with each increment; for example, at 100% diagnostic incompleteness, the Chronic Conditions Data Warehouse phenotype identified zero patients, as its phenotypic characteristics included only diagnosis codes. Induced inaccuracy and timeliness similarly demonstrated variations in performance of each phenotype, therefore resulting in fewer patients being identified with each incremental change. We used EHR data with diagnosis, medication, and laboratory data types from a large tertiary hospital system to understand T2D phenotypic differences and performance. We used induced data quality methods to learn how data quality issues may impact identification of the denominator populations upon which clinical (eg, clinical research and trials, population health evaluations) and financial or operational decisions are made. The novel results from our study may inform future approaches to shaping a common T2D computable phenotype definition that can be applied to clinical informatics, managing chronic conditions, and additional industry-wide efforts in health care.

Optimizing biomedical information retrieval with a keyword frequency-driven prompt enhancement strategy

BackgroundMining the vast pool of biomedical literature to extract accurate responses and relevant references is challenging due to the domain's interdisciplinary nature, specialized jargon, and continuous evolution. Early natural language processing (NLP) approaches often led to incorrect answers as they failed to comprehend the nuances of natural language. However, transformer models have significantly advanced the field by enabling the creation of large language models (LLMs), enhancing question-answering (QA) tasks. Despite these advances, current LLM-based solutions for specialized domains like biology and biomedicine still struggle to generate up-to-date responses while avoiding “hallucination” or generating plausible but factually incorrect responses.ResultsOur work focuses on enhancing prompts using a retrieval-augmented architecture to guide LLMs in generating meaningful responses for biomedical QA tasks. We evaluated two approaches: one relying on text embedding and vector similarity in a high-dimensional space, and our proposed method, which uses explicit signals in user queries to extract meaningful contexts. For robust evaluation, we tested these methods on 50 specific and challenging questions from diverse biomedical topics, comparing their performance against a baseline model, BM25. Retrieval performance of our method was significantly better than others, achieving a median Precision@10 of 0.95, which indicates the fraction of the top 10 retrieved chunks that are relevant. We used GPT-4, OpenAI's most advanced LLM to maximize the answer quality and manually accessed LLM-generated responses. Our method achieved a median answer quality score of 2.5, surpassing both the baseline model and the text embedding-based approach. We developed a QA bot, WeiseEule (https://github.com/wasimaftab/WeiseEule-LocalHost), which utilizes these methods for comparative analysis and also offers advanced features for review writing and identifying relevant articles for citation.ConclusionsOur findings highlight the importance of prompt enhancement methods that utilize explicit signals in user queries over traditional text embedding-based approaches to improve LLM-generated responses for specialized queries in specialized domains such as biology and biomedicine. By providing users complete control over the information fed into the LLM, our approach addresses some of the major drawbacks of existing web-based chatbots and LLM-based QA systems, including hallucinations and the generation of irrelevant or outdated responses.

Informatics-driven solutions for optimal care delivery in liver transplantation.

Clinical informatics, which combines health information technology and clinical expertise, aims to improve health care delivery and outcomes. For candidates and recipients of liver transplants, the complexities of their management are vast. Care often involves significant volumes of data from various sources and multiple health care settings and health care systems. Informatics represents an opportunity to support the care of these patients through this process; however, it has been underutilized to date. Preliminary examples of informatics use exist for this patient population, such as clinical decision support to link patients to hepatology providers, utilization of digital health technologies to track responses to medical therapy, and artificial intelligence to characterize organ donor compatibility. Further deployment of these tools can optimize care delivery for patients with cirrhosis and across the transplant cascade. In this review, we explore the utility of various techniques of clinical informatics mainly within the transplant care cascade, barriers to implementation, and highlight opportunities for future investigation and optimized use. Although barriers to widespread adoption remain such as interoperability, data access and availability, and structured implementation strategies, the integration of informatics and clinical decision support/artificial intelligence-based tools into routine care for liver transplantation represents an opportunity for marked enhancement in how we deliver care to these patients.

Impact of psychological correlates, Social isolation and loneliness on the management and self care behaviors of Cardio-vascular Disease after COVID- 19 Pandemic

The present research paper examines the impact of psychological correlates (stress, anxiety and depression), social isolation and loneliness in the management and self care behaviors of cardiovascular disease after Covid 19 pandemics. According to World Heart Federation 2023 report shows that more than half a billion people around the world continue to be affected by cardiovascular diseases, which accounted for 20.5 million deaths in 2021 – close to a third of all deaths globally and an overall increase on the estimated 121 million CVD deaths. Over three quarters of Cardiovascular disease deaths take place in low- and middle-income countries. Out of the 17 million premature deaths (under the age of 70) due to non-communicable diseases in 2019 (World Health Organization), 38% were caused by cardiovascular diseases. Most cardiovascular diseases can be prevented by addressing psychologically, socially, behavioral and environmental risk factors such as anxiety, anger, stress, depression, social isolation, loneliness, tobacco use, unhealthy diet and obesity, physical inactivity, harmful use of alcohol and air pollution. Anxiety is associated with a variety of physical and psychological symptoms that can negatively impact the health outcome. Anxiety can lead to increased blood pressure, heart rate, and cortisol levels, which can contribute to the development and progression of Cardio vascular disease. Psychological interventions can help individuals with Cardio vascular disease better cope with the disease, improve their quality of life, and reduce the risk of adverse health outcomes. It is important to detect cardiovascular disease as early as possible for the management and prevention of it. Research involves 300 participants with cardiovascular disease during 2021 to 2024. The data was collected by the participant of Chhattisgarh and Madhya Pradesh visited to the private clinics and hospital for their routine checkups. Using standardized questionnaire following information was gathered i.e. Demographic, biomedical information, anxiety, depression, stress, as well as feelings of social isolation and loneliness, self health care inventory for cardio vascular disease and their lifestyle related information for preventing from the diseases. The data was analyzed using descriptive statistics, and one way ANOVA. The results revealed a significant relationship with psychological correlates, social isolation and loneliness in the management and care of cardiovascular diseases participants after Covid 19 pandemic. The results of the study are fruitful for Health Care Professionals and Health Care Providers for the management of cardiovascular diseases; also it’s useful for Ministry of Health to make a policy related to the control the death rate which is caused due to cardiovascular diseases. Hence it is important to "Identifying sustainable ways to work with communities to take action to prevent and control modifiable risk factors for heart disease is essential for reducing the global burden of heart disease," said George A. Mensah, MD, FACC, director of the Center for Translation Research and Implementation Science at the NHLBI.

Adoption of the OMOP CDM for Cancer Research using Real-world Data: Current Status and Opportunities.

The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) that is developed and maintained by the Observational Health Data Sciences and Informatics (OHDSI) community supports large scale cancer research by enabling distributed network analysis. As the number of studies using the OMOP CDM for cancer research increases, there is a growing need for an overview of the scope of cancer research that relies on the OMOP CDM ecosystem. In this study, we present a comprehensive review of the adoption of the OMOP CDM for cancer research and offer some insights on opportunities in leveraging the OMOP CDM ecosystem for advancing cancer research. Published literature databases were searched to retrieve OMOP CDM and cancer-related English language articles published between January 2010 and December 2023. A charting form was developed for two main themes, i.e., clinically focused data analysis studies and infrastructure development studies in the cancer domain. In total, 50 unique articles were included, with 30 for the data analysis theme and 23 for the infrastructure theme, with 3 articles belonging to both themes. The topics covered by the existing body of research was depicted. Through depicting the status quo of research efforts to improve or leverage the potential of the OMOP CDM ecosystem for advancing cancer research, we identify challenges and opportunities surrounding data analysis and infrastructure including data quality, advanced analytics methodology adoption, in-depth phenotypic data inclusion through NLP, and multisite evaluation.

Self-Reported Learning Strategies and Preferences in Health Informatics.

Despite the proliferation of educational programmes in Health Informatics (HI) worldwide, there is limited knowledge regarding students' preferences and learning strategies in HI courses. To address this gap, we conducted a study to gather and analyse data from three HI courses. Employing the Motivated Strategies for Learning Questionnaire (MSLQ) and theories of deep and surface learning, we designed a questionnaire to collect data. The analysis of students' responses indicates that machine learning emerges as one of the most interesting topics, while certain topics such as data wrangling of genomics data were more challenging for students. Students expressed a preference for sequential learning. They exhibited multimodal tendencies regarding the type of learning resources, with tendency to prefer learning resources that have more visual contents. In all three courses, learners reported using deep learning strategy rather than surface learning, yet they appear to struggle with employing organisation, elaboration, and peer learning tactics. This study provides valuable insights into HI education, offering recommendations for educators, learners, and researchers to enhance HI education.