Digital Health Education for the Future: The SaNuRN (Santé Numérique Rouen-Nice) Consortium's Journey

SaNuRN is a five-year project by the University of Rouen Normandy (URN) and the Côte d’Azur University (CAU) consortium to optimize digital health education for medical and paramedical students, professionals, and administrators. The project includes a skills framework, training modules, and teaching resources. In 2027, SaNuRN is expected to train a significant portion of the 400,000 health and paramedical professions students at the French national level. Our purpose is to give a synopsis of the SaNuRN initiative, emphasizing its novel educational methods and how they will enhance the delivery of digital health education. Our goals include showcasing SaNuRN as a comprehensive program consisting of a proficiency framework, instructional modules, and educational materials and explaining how SaNuRN is implemented in the participating academic institutions. SaNuRN is a project aimed at educating and training health-related and paramedics students in digital health. The project results from a cooperative effort between URN and CAU, covering four French departments. The project is based on the French National Referential on Digital Health (FNRDH), which defines the skills and competencies to be acquired and validated by every student in the health, paramedical, and social professions curricula. The SaNuRN team is currently adapting the existing URN and CAU syllabi to FNRDH and developing short-duration video capsules of 20 to 30 minutes to teach all the relevant material. The project aims to ensure that the largest student population earns the necessary skills, and it has developed a two-tier system involving facilitators who will enable the efficient expansion of the project’s educational outreach and support the students in learning the needed material efficiently. With a focus on real-world scenarios and innovative teaching activities integrating telemedicine devices and virtual professionals, SaNuRN is committed to enabling continuous learning for healthcare professionals in clinical practice. The SaNuRN team introduced new ways of evaluating healthcare professionals by shifting from a knowledge-based to a competencies-based evaluation, aligning with the Miller teaching pyramid and using the Objective Structured Clinical Examination and Script Concordance Test in digital health education. Drawing on the expertise of URN, CAU, and their public health and digital research laboratories and partners, the SaNuRN project represents a platform for continuous innovation, including telemedicine training and living labs with virtual and interactive professional activities. The SaNuRN project provides a comprehensive, personalized 30-hour training package for health and paramedical students, addressing all 70 FNRDH competencies. The program is enhanced using AI and NLP to create virtual patients and professionals for digital healthcare simulation. SaNuRN teaching materials are open-access. The project collaborates with academic institutions worldwide to develop educational material in digital health in English and multilingual formats. SaNuRN offers a practical and persuasive training approach to meet the current digital health education requirements.

Sharing Digital Health Educational Resources in a One-Stop Shop Portal: Tutorial on the Catalog and Index of Digital Health Teaching Resources (CIDHR) Semantic Search Engine

BACKGROUND

Access to reliable and accurate digital health web-based resources is crucial. However, the lack of dedicated search engines for non-English languages, such as French, is a significant obstacle in this field. Thus, we developed and implemented a multilingual, multiterminology semantic search engine called Catalog and Index of Digital Health Teaching Resources (CIDHR). CIDHR is freely accessible to everyone, with a focus on French-speaking resources. CIDHR has been initiated to provide validated, high-quality content tailored to the specific needs of each user profile, be it students or professionals.

OBJECTIVE

This study's primary aim in developing and implementing the CIDHR is to improve knowledge sharing and spreading in digital health and health informatics and expand the health-related educational community, primarily French speaking but also in other languages. We intend to support the continuous development of initial (ie, bachelor level), advanced (ie, master and doctoral levels), and continuing training (ie, professionals and postgraduate levels) in digital health for health and social work fields. The main objective is to describe the development and implementation of CIDHR. The hypothesis guiding this research is that controlled vocabularies dedicated to medical informatics and digital health, such as the Medical Informatics Multilingual Ontology (MIMO) and the concepts structuring the French National Referential on Digital Health (FNRDH), to index digital health teaching and learning resources, are effectively increasing the availability and accessibility of these resources to medical students and other health care professionals.

METHODS

First, resource identification is processed by medical librarians from websites and scientific sources preselected and validated by domain experts and surveyed every week. Then, based on MIMO and FNRDH, the educational resources are indexed for each related knowledge domain. The same resources are also tagged with relevant academic and professional experience levels. Afterward, the indexed resources are shared with the digital health teaching and learning community. The last step consists of assessing CIDHR by obtaining informal feedback from users.

RESULTS

Resource identification and evaluation processes were executed by a dedicated team of medical librarians, aiming to collect and curate an extensive collection of digital health teaching and learning resources. The resources that successfully passed the evaluation process were promptly included in CIDHR. These resources were diligently indexed (with MIMO and FNRDH) and tagged for the study field and degree level. By October 2023, a total of 371 indexed resources were available on a dedicated portal.

CONCLUSIONS

CIDHR is a multilingual digital health education semantic search engine and platform that aims to increase the accessibility of educational resources to the broader health care-related community. It focuses on making resources "findable," "accessible," "interoperable," and "reusable" by using a one-stop shop portal approach. CIDHR has and will have an essential role in increasing digital health literacy.

International scientific communication on COVID-19 data: management pitfalls understanding

BACKGROUND

During the pandemic, countries utilized various forms of statistical estimations of coronavirus disease-2019 (COVID-19) impact. Differences between databases make direct comparisons and interpretations of data in different countries a challenge. We evaluated country-specific approaches to COVID-19 data and recommended changes that would improve future international collaborations.

METHODS

We compared the COVID-19 reports presented on official UK (National Health System), Israeli (Department of Health), Latvian (Center for Disease Prevention and Control) and USA (Centers for Disease Control and Prevention) health authorities' websites.

RESULTS

Our analysis demonstrated critical differences in the ways COVID-19 statistics were made available to the general and scientific communities. Specifically, the differences in approaches were found in the presentation of the number of infected cases and tests, and percentage of positive cases, the number of severe cases, the number of vaccinated, and the number and percent of deaths.

CONCLUSION

Findability, Accessibility, Interoperability and Reusability principles could guide the development of essential global standards that provide a basis for communication within and outside of the scientific community.

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Metrics reloaded: recommendations for image analysis validation

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.

Alert-Grouping: Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle with Alarm Fatigue in Intensive Care

In Intensive Care Units (ICUs), patients are monitored using various devices that generate alerts when specific metrics, such as heart rate and oxygen saturation, exceed predetermined thresholds. However, these alerts can be inaccurate and lead to alert fatigue, resulting in errors and inaccurate diagnoses. We propose Alert grouping, a "Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle Alarm Fatigue in Intensive Care" model. The alert grouping looks at patients at the individual and cluster levels, and healthcare-related constraints to assist medical and nursing teams in setting personalized alert thresholds of vital parameters. By simulating the function of ICU patient bed devices, we demonstrate that the proposed alert grouping model effectively reduces the number of alarms overall, improving the alert system's validity and reducing alarm fatigue. Implementing this personalized alert model in ICUs boosts medical and nursing teams' confidence in the alert system, leading to better care for ICU patients by significantly reducing alarm fatigue, thereby improving the quality of care for ICU patients.

Leveraging Data and Technology to Enhance Interdisciplinary Collaboration and Health Outcomes

OBJECTIVE

To give an overview of recent research and propose a selection of best papers published in 2022 in Informatics for One Health.

METHODS

An extensive search using PubMed and Web of Science was conducted to identify peer-reviewed articles published between December 2021 and December 2022, in order to find relevant publications in the 'Informatics for One Health' field. The selection process comprised three steps: (i) eight candidate best papers were first selected by the two section editors; (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper; and (iii) the editorial committee of the Yearbook conducted the final best paper selection.

RESULTS

The candidate best papers represent studies that characterized significant challenges facing Informatics for One Health. Other trends of interest related to the deployment of medical artificial intelligence tools and the implementation of the FAIR principles within the One Health broad scenario. In general, papers identified in the search fell into one of the following categories: 1) Health improvement via digital technology; 2) Climate change/Environment/Biodiversity; and 3) Maturity of healthcare services.

CONCLUSION

The topic turns extremely important in the next future for what concerns the need to understand complex interactions in order to safeguard the health of populations and ecosystems.

Digital Emergency Management for a Complex One Health Landscape: the Need for Standardization, Integration, and Interoperability

OBJECTIVE

Planning reliable long-term planning actions to handle disruptive events requires a timely development of technological infrastructures, as well as the set-up of focused strategies for emergency management. The paper aims to highlight the needs for standardization, integration, and interoperability between Accident & Emergency Informatics (A&EI) and One Digital Health (ODH), as fields capable of dealing with peculiar dynamics for a technology-boosted management of emergencies under an overarching One Health panorama.

METHODS

An integrative analysis of the literature was conducted to draw attention to specific foci on the correlation between ODH and A&EI, in particular: (i) the management of disruptive events from private smart spaces to diseases spreading, and (ii) the concepts of (health-related) quality of life and well-being.

RESULTS

A digitally-focused management of emergency events that tackles the inextricable interconnectedness between humans, animals, and surrounding environment, demands standardization, integration, and systems interoperability. A consistent and finalized process of adoption and implementation of methods and tools from the International Standard Accident Number (ISAN), via findability, accessibility, interoperability, and reusability (FAIR) data principles, to Medical Informatics and Digital Health Multilingual Ontology (MIMO) - capable of looking at different approaches to encourage the integration between the ODH framework and the A&EI vision, provides a first answer to these needs.

CONCLUSIONS

ODH and A&EI look at different scales but with similar goals for converging health and environmental-related data management standards to enable multi-sources, interdisciplinary, and real-time data integration and interoperability. This allows holistic digital health both in routine and emergency events.

The Need for Green and Responsible Medical Informatics and Digital Health: Looking Forward with One Digital Health

One Health is an important initiative to view the world in a more integrative sense of our health and environment. Digital Health provides essential support to all of us as healthcare professionals and customers. One Digital Health (ODH) combines both One Health and Digital Health to provide a technologically integrative view. ODH gives an essential place to the environment and ecosystems. Thus, health technologies and digital health must be "green" and eco-friendly as much as possible. We suggest in this position paper examples of developing and implementing ODH-related concepts, systems, and products with a respectful consideration of the environment. For humans and animals, developing cutting-edge technologies to improve wellness and healthcare is critical. Nevertheless, we can learn from One Health that digitalization and so One Digital Health must be built to implement green, eco-friendly, and responsible thinking.

One Digital Health Intervention for Monitoring Human and Animal Welfare in Smart Cities: Viewpoint and Use Case

Smart cities and digital public health are closely related. Managing digital transformation in urbanization and living spaces is challenging. It is critical to prioritize the emotional and physical health and well-being of humans and their animals in the dynamic and ever-changing environment they share. Human-animal bonds are continuous as they live together or share urban spaces and have a mutual impact on each other's health as well as the surrounding environment. In addition, sensors embedded in the Internet of Things are everywhere in smart cities. They monitor events and provide appropriate responses. In this regard, accident and emergency informatics (A&EI) offers tools to identify and manage overtime hazards and disruptive events. Such manifold focuses fit with One Digital Health (ODH), which aims to transform health ecosystems with digital technology by proposing a comprehensive framework to manage data and support health-oriented policies. We showed and discussed how, by developing the concept of ODH intervention, the ODH framework can support the comprehensive monitoring and analysis of daily life events of humans and animals in technologically integrated environments such as smart homes and smart cities. We developed an ODH intervention use case in which A&EI mechanisms run in the background. The ODH framework structures the related data collection and analysis to enhance the understanding of human, animal, and environment interactions and associated outcomes. The use case looks at the daily journey of Tracy, a healthy woman aged 27 years, and her dog Mego. Using medical Internet of Things, their activities are continuously monitored and analyzed to prevent or manage any kind of health-related abnormality. We reported and commented on an ODH intervention as an example of a real-life ODH implementation. We gave the reader examples of a "how-to" analysis of Tracy and Mego's daily life activities as part of a timely implementation of the ODH framework. For each activity, relationships to the ODH dimensions were scored, and relevant technical fields were evaluated in light of the Findable, Accessible, Interoperable, and Reusable principles. This "how-to" can be used as a template for further analyses. An ODH intervention is based on Findable, Accessible, Interoperable, and Reusable data and real-time processing for global health monitoring, emergency management, and research. The data should be collected and analyzed continuously in a spatial-temporal domain to detect changes in behavior, trends, and emergencies. The information periodically gathered should serve human, animal, and environmental health interventions by providing professionals and caregivers with inputs and "how-to's" to improve health, welfare, and risk prevention at the individual and population levels. Thus, ODH complementarily combined with A&EI is meant to enhance policies and systems and modernize emergency management.

Ontologies Applied in Clinical Decision Support System Rules: Systematic Review

BACKGROUND

Clinical decision support systems (CDSSs) are important for the quality and safety of health care delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused.

OBJECTIVE

Ontologies have the potential to promote the reuse of CDSS rules. Therefore, we systematically screened the literature to elaborate on the current status of ontologies applied in CDSS rules, such as rule management, which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate, and maintenance, which updates CDSS rules. Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules.

METHODS

The literature search was focused on the intersection of ontologies; clinical decision support; and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database. Grounded theory and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines were followed. One author initiated the screening and literature review, while 2 authors validated the processes and results independently. The inclusion and exclusion criteria were developed and refined iteratively.

RESULTS

CDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules.

CONCLUSIONS

Ontologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the reusability and interoperability of CDSS rules. This review identified and described the ontologies that, despite their limitations, enable Semantic Web technologies and their applications in CDSS rules.

Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study

BACKGROUND

The availability of electronic medical record (EMR) free-text data for research varies. However, access to short diagnostic text fields is more widely available.

OBJECTIVE

This study assesses agreement between free-text and short diagnostic text data from primary care EMR for identification of posttraumatic stress disorder (PTSD).

METHODS

This retrospective cross-sectional study used EMR data from a pan-Canadian repository representing 1574 primary care providers at 265 clinics using 11 EMR vendors. Medical record review using free text and short diagnostic text fields of the EMR produced reference standards for PTSD. Agreement was assessed with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.

RESULTS

Our reference set contained 327 patients with free text and short diagnostic text. Among these patients, agreement between free text and short diagnostic text had an accuracy of 93.6% (CI 90.4%-96.0%). In a single Canadian province, case definitions 1 and 4 had a sensitivity of 82.6% (CI 74.4%-89.0%) and specificity of 99.5% (CI 97.4%-100%). However, when the reference set was expanded to a pan-Canada reference (n=12,104 patients), case definition 4 had the strongest agreement (sensitivity: 91.1%, CI 90.1%-91.9%; specificity: 99.1%, CI 98.9%-99.3%).

CONCLUSIONS

Inclusion of free-text encounter notes during medical record review did not lead to improved capture of PTSD cases, nor did it lead to significant changes in case definition agreement. Within this pan-Canadian database, jurisdictional differences in diagnostic codes and EMR structure suggested the need to supplement diagnostic codes with natural language processing to capture PTSD. When unavailable, short diagnostic text can supplement free-text data for reference set creation and case validation. Application of the PTSD case definition can inform PTSD prevalence and characteristics.

One Digital Health for more FAIRness

BACKGROUND

 One Digital Health (ODH) aims to propose a framework that merges One Health's and Digital Health's specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or, in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention.

OBJECTIVES

 This paper aims to elicit how the ODH framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup.

METHODS

 An integrative analysis of the literature was conducted to extract instances of the need-or of the eventual already existing deployment-of FAIR principles, for each of the three layers (keys, perspectives and dimensions) of the ODH framework. The scope was to assess the extent of scatteredness in pursuing the many facets of FAIRness, descending from the lack of a unifying and balanced framework.

RESULTS

 A first attempt to interpret the different technological components existing in the different layers of the ODH framework, in the light of the FAIR principles, was conducted. Although the mature and working examples of workflows for data FAIRification processes currently retrievable in the literature provided a robust ground to work on, a nonsuitable capacity to fully assess FAIR aspects for highly interconnected scenarios, which the ODH-based ones are, has emerged. Rooms for improvement are anyway possible to timely deal with all the underlying features of topics like the delivery of health care in a syndemic scenario, the digital transformation of human and animal health data, or the digital nature conservation through digital technology-based intervention.

CONCLUSIONS

 ODH pillars account for the availability (findability, accessibility) of human, animal, and environmental data allowing a unified understanding of complex interactions (interoperability) over time (reusability). A vision of integration between these two worlds, under the vest of ODH Interventions featuring FAIRness characteristics, toward the development of a systemic lookup of health and ecology in a digitalized way, is therefore auspicable.

Early Detection and Control of the Next Epidemic Wave Using Health Communications: Development of an Artificial Intelligence-Based Tool and Its Validation on COVID-19 Data from the US

Social media networks highly influence on a broad range of global social life, especially in the context of a pandemic. We developed a mathematical model with a computational tool, called EMIT (Epidemic and Media Impact Tool), to detect and control pandemic waves, using mainly topics of relevance on social media networks and pandemic spread. Using EMIT, we analyzed health-related communications on social media networks for early prediction, detection, and control of an outbreak. EMIT is an artificial intelligence-based tool supporting health communication and policy makers decisions. Thus, EMIT, based on historical data, social media trends and disease spread, offers an predictive estimation of the influence of public health interventions such as social media-based communication campaigns. We have validated the EMIT mathematical model on real world data combining COVID-19 pandemic data in the US and social media data from Twitter. EMIT demonstrated a high level of performance in predicting the next epidemiological wave (AUC = 0.909, F₁ = 0.899).

Digital Health Multilingual Ontology to Index Teaching Resources

The aim of this paper is to present the use of Medical Informatics Multilingual Ontology (MIMO) to index digital health resources that are (and will be) included in SaNuRN (project to teach digital health). MIMO currently contains 1,379 concepts and is integrated into HeTOP, which is a cross-lingual multiterminogy server. Existing teaching resources have been reindexed with MIMO concepts and integrated into a dedicated website. A total of 345 resources have been indexed with MIMO concepts and are freely available at https://doccismef.chu-rouen.fr/dc/#env=sanurn. The development of a multilingual MIMO for enhancing the quality and the efficiency of international projects is challenging. A specific semantic search engine has been deployed to give access to digital health teaching resources.

Quantitative and qualitative analysis of the quality of life of Type 1 diabetes patients using insulin pumps and of those receiving multiple daily insulin injections

Introduction

Insulin pump therapy represents an alternative to multiple daily injections and can improve glycemic control and quality of life (QoL) in Type 1 diabetes mellitus (T1DM) patients. We aimed to explore the differences and factors related to the T1DM-specific QoL of such patients in Latvia.

Design and methods

A mixed-method cross-sectional study on 87 adult T1DM patients included 20 pump users and 67 users of injections who participated in the quantitative part of the study; 8 pump users and 13 injection users participated in the qualitative part. Patients were invited to participate using a dedicated digital platform. Their QoL and self-management habits were assessed using specially developed questionnaires adapted to Latvian conditions. Multiple logistic regression models were built to investigate the association between social and self-management factors and patients’ QoL. In addition, qualitative analysis of answers was performed.

Results

Insulin pump users were younger, had higher incomes, and reported higher T1DM expenses than users of multiple daily injections. There were no differences in self-management between the groups; Total QoL differed at the 0.1 significance level. In fully adjusted multiple logistic regression models, the most important factor that increased Total QoL was lower T1DM-related expenses (odds ratio, OR 7.02 [95% confidence interval 1.29; 38.0]). Men and those with more years of living with T1DM had better QoL (OR 9.62 [2.20; 42.1] and OR 1.16 [1.05; 1.29], respectively), but the method of administration was not significantly associated with QoL (OR 7.38 [0.87; 62.9]). Qualitative data supported the results of quantitative analysis.

Conclusions

QoL was the main reason to use an insulin pump, while the expense was the main reason to avoid the use of it or to stop using it. Reimbursement policies thus should be considered to enable patients to choose the more convenient method for themselves.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12955-022-02029-2.

Medical Informatics and Digital Health Multilingual Ontology (MIMO): a tool to improve international collaborations

Background

Even if English is the leading language for international communication, it is essential to keep in mind that research runs at the local level by local teams generally communicating in their local/national language, especially in Europe among European projects.

Objective

Therefore, the European Federation for Medical Informatics - Working Group on Health Informatics for Inter-regional Cooperation” has one objective: To develop a multilingual ontology focusing on Health Informatics and Digital Health as a collaboration tool that improves international and, in particular, European collaborations.

Results

We have developed the Medical Informatics and Digital Health Multilingual Ontology (MIMO). Hosted on the Health Terminology/Ontology Portal (HeTOP), MIMO contains around 1,000 concepts, 460 MeSH Descriptors, 220 MeSH Concepts, and more than 300 newly created concepts. MIMO is continuously updated to comprise as recent as possible concepts and their translations in more than 30 languages. Moreover, the MIMO’s development team constantly improves MIMO content and supporting information. Thus, during workshop discussions and one-on-one exchanges, the MIMO team has collected domain experts’ opinions about the community’s interests and suggestions for future enhancements. Moreover, MIMO will be integrated to support the annotation and categorization of research products into the HosmartAI European project involving more than 20 countries around Europe and worldwide.

Conclusion

MIMO is hosted by HeTOP (Health Terminology/Ontology Portal), which integrates 100 terminologies and ontologies in 55 languages. MIMO is freely available online. MIMO is portable to other knowledge platforms as part of MIMO’s main aims to facilitate communication between medical librarians, translators, and researchers as well as to support students’ self-learning.

Intergenerational knowledge management in a cutting-edge Israeli industry: Visions and challenges

Knowledge management is a multifaceted, complex, end-to-end organizational process dealing with collecting and using data, information, and knowledge generated by a group of individuals. The current study examines the changes required in companies’ quality systems to enhance intergenerational learning and knowledge retention. Our primary objective was to understand the factors that influence the development of an organizational culture encouraging innovation, knowledge sharing, organizational learning, openness, and providing opportunities to create up-to-date knowledge. We collected the viewpoints and needs of industry professionals by using interviews and a survey. Then, we analyzed the factors that influence knowledge management quality and transfer between workforce generations. The professionals’ primary goal is to introduce, integrate, and improve knowledge in their organization. Their second goal is to facilitate knowledge sharing and transfer between workforce generations. Improving transgenerational knowledge sharing and reducing the loss of knowledge are challenges for all industries. A cutting-edge industry such as the defense field deals with sensitive data, and knowledge management is a strategic need in a competitive context. Quality management standards propose guidelines for developing and enhancing the overall knowledge-related processes. However, implementing them requires a shift in the corporate culture team. Organizational knowledge resilience must be developed by involving the workforce in implementing knowledge management systems.

Trends in the Prevalence of Chronic Medication Use Within Children in Israel Between 2010 and 2019: Protocol for a Retrospective Cohort Study

Background

Prescription of psychostimulants has significantly increased in most countries worldwide for both preschool and school-aged children. Understanding the trends of chronic medication use among children in different age groups and from different sociodemographic backgrounds is essential. It is essential to distinguish between selected therapy areas to help decision-makers evaluate not only the relevant expected medication costs but also the specific services related to these areas.

Objective

This study will analyze differences in trends regarding medications considered psychobehavioral treatments and medications considered nonpsychobehavioral treatments and will identify risk factors and predictors for chronic medication use among children.

Methods

This is a retrospective study. Data will be extracted from the Clalit Health Services data warehouse. For each year between 2010 and 2019, there are approximately 1,500,000 children aged 0-18 years. All medication classes will be identified using the Anatomical Therapeutic Chemical code. A time-trend analysis will be performed to investigate if there is a significant difference between the trends of children’s psychobehavioral and nonpsychobehavioral medication prescriptions. A logistic regression combined with machine learning models will be developed to identify variables that may increase the risk for specific chronic medication types and identify children likely to get such treatment.

Results

The project was funded in 2019. Data analysis is currently underway, and the results are expected to be submitted for publication in 2022. Understanding trends regarding medications considered psychobehavioral treatments and medications considered nonpsychobehavioral treatments will support the identification of risk factors and predictors for chronic medication use among children.

Conclusions

Analyzing the response of the patient (and their parents or caregivers) population over time will hopefully help improve policies for prescriptions and follow-up of chronic treatments in children.

International Registered Report Identifier (IRRID)

DERR1-10.2196/36756

Accident & Emergency Informatics and One Digital Health

OBJECTIVES

Climate changes are the major challenge in public and individual health, as they modify the ecosystem and yield contagious diseases from animal to human. Furthermore, we notice the rapid development of elderly, changing the population demographic. These critical measures have imposed economical costs, require trained personnel, and reduce the healthcare systems' performances.

METHODS

COVID-19 pandemic showed that digital health paradigms such as m-health, telemedicine, and Internet of medical things (IoMT) should be further developed for such disasters. Quarantine was experienced frequently at different levels, which indicates the urgent need to develop smart medical homes for continuous monitoring of the patients. Human health, environment, and animals are the three interwoven aspects of public health that should be formulated under a conceptual and unified framework. Accident and Emergency Informatics (A&EI) considers the prediction and prevention of an individual's health in the long term and detects instant accidents and emergencies for further processes linking to hospital and rescue services for lowering the impact. One Digital Health (ODH) considers the health of the human, the animal, and the environment as a whole.

RESULTS & CONCLUSION

In this position paper, we discuss the mutual benefits of A&EI and ODH in disaster management. We outline the mission, current status of A&EI in healthcare, and summarize the most important development of A&EI-related scope in the other fields of science. We discuss developing smart environments to monitor environmental and animal aspects. Then we examine the use of the ODH framework for enhancing the A&EI capacities to deal with complex disasters. Moreover, we discuss the further development of the international standard accident number (ISAN) to include and link environmental and animal event related data. Besides, ODH will cope with the A&EI protocols and technical specifications to be part of A&EI in the application layer.

WikiMeSH: Multi Lingual MeSH Translations via Wikipedia

OBJECTIVE

The aim of this paper is to propose an extended translation of the MeSH thesaurus based on Wikipedia pages.

METHODS

A mapping was realized between each MeSH descriptor (preferred terms and synonyms) and corresponding Wikipedia pages.

RESULTS

A tool called "WikiMeSH" has been developed. Among the top 20 languages of this study, seven have currently no MeSH translations: Arabic, Catalan, Farsi (Iran), Mandarin Chinese, Korean, Serbian, and Ukrainian. For these seven languages, WikiMeSH is proposing a translation for 47% for Arabic to 34% for Serbian.

CONCLUSION

WikiMeSH is an interesting tool to translate the MeSH thesaurus and other health terminologies and ontologies based on a mapping to Wikipedia pages.

Social Media and the Internet of Things for Emergency and Disaster Medicine Management

Social Media and the Internet of Things are nowadays full and strong components of day-to-day life worldwide. Both allow communicating with others 24 hours a day, 7 days a week without distance limitations. During the last decade, on-site citizens have shared disaster-related first reports on social media. Official institutions are using the same framework for delivering up-to-date and follow-up directives. Moreover, monitoring health risks, patients, and systems behavior in real-time over the Internet-of-Things allows detecting different levels of anomalies that might lead to critical events that need to be managed as an emergency. Emergency and disaster medicines deal with broad and complex medical, surgical, mental health, epidemiological, managerial, and communicational issues. Social Media platforms and the Internet of Things are technologies that increase cyber-physical interactions between individuals, machines, and their environment. The generated data over time are massive and are supporting the emergency or disaster mitigation process. This chapter deals with, in the first section, the social media platforms, and the Internet of Things. Then, at a second one, the concepts of emergency, disaster medicine and management are discussed. In the following two sections, we discuss applications and usages of social media and IoT technologies for improving the management (preparedness, response, recovery, mitigation) of emergencies and disasters as fundamental keys and pillars for efficiently handling the managerial information flow in emergency and disaster contexts.

Reasons for Utilizing Telemedicine during and after the COVID-19 Pandemic: An Internet-Based International Study

The COVID-19 pandemic challenges healthcare services. Concomitantly, this pandemic had a stimulating effect on technological expansions related to telehealth and telemedicine. We sought to elucidate the principal patients' reasons for using telemedicine during the COVID-19 pandemic and the propensity to use it thereafter. Our primary objective was to identify the reasons of the survey participants' disparate attitudes toward the use of telemedicine. We performed an online, multilingual 30-question survey for 14 days during March-April 2021, focusing on the perception and usage of telemedicine and their intent to use it after the pandemic. We analyzed the data to identify the attributes influencing the intent to use telemedicine and built decision trees to highlight the most important related variables. We examined 473 answers: 272 from Israel, 87 from Uruguay, and 114 worldwide. Most participants were women (64.6%), married (63.8%) with 1-2 children (52.9%), and living in urban areas (84.6%). Only a third of the participants intended to continue using telemedicine after the COVID-19 pandemic. Our main findings are that an expected substitution effect, technical proficiency, reduced queueing times, and peer experience are the four major factors in the overall adoption of telemedicine. Specifically, (1) for most participants, the major factor influencing their telemedicine usage is the implicit expectation that such a visit will be a full substitute for an in-person appointment; (2) another factor affecting telemedicine usage by patients is their overall technical proficiency and comfort level in the use of common web-based tools, such as social media, while seeking relevant medical information; (3) time saving as telemedicine can allow for asynchronous communications, thereby reducing physical travel and queuing times at the clinic; and finally (4) some participants have also indicated that telemedicine seems more attractive to them after watching family and friends (peer experience) use it successfully.

One Digital Health Is FAIR

The One Digital Health framework aims at transforming future health ecosystems and guiding the implementation of a digital technologies-based systemic approach to caring for humans' and animals' health in a managed surrounding environment. To integrate and to use the data generated by the ODH data sources, "FAIRness" stands as a prerequisite for proper data management and stewardship.

Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence-Based Infodemiology Study

Background

Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines.

Objective

Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence–based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications.

Methods

The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy.

Results

We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events.

Conclusions

This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process.

Statin Therapy for Primary Prevention in the Elderly and Its Association with New-Onset Diabetes, Cardiovascular Events, and All-Cause Mortality

PURPOSE

This study assessed associations of the use of statins for primary prevention with cardiovascular outcomes among adults ages ≥70 years.

METHODS

In a retrospective population-based cohort study, new users of statins without cardiovascular disease or diabetes mellitus were stratified by ages ≥70 years and <70 years. Using a time-dependent approach, adherence to statins was evaluated according to the proportion of days covered: <25%, 25%-50%, 50%-75%, and ≥75%. We assessed associations of statin therapy with increased risk of new-onset diabetes mellitus and with decreased risks of major adverse cardiovascular events and all-cause mortality.

RESULTS

Of 42,767 new users of statins, 5970 (14%) were ages ≥70 years. The incident rates of major adverse cardiovascular events, all-cause mortality, and new-onset diabetes mellitus in the highest to lowest proportion of days covered categories were 16.9%, 16.7%, and 9.4% and 6.3%, 1.7%, and 9.4%, respectively. For the older group, the adjusted hazard ratios of major adverse cardiovascular events and mortality were significantly decreased for the highest adherence group (proportion of days covered ≥75%): 0.71 (0.57-0.88) and 0.68 (0.54-0.84), respectively. The respective hazard ratios were less favorable for the younger group: 0.80 (0.68-0.93) and 0.74 (0.58-1.03). The risk of new-onset diabetes mellitus was increased for the younger but not the older group.

CONCLUSIONS

Statin use for primary prevention was associated with cardiovascular benefit in adults ages ≥70 years without a significant risk for the development of diabetes. These data may support the use of statin therapy for primary prevention in the elderly.

Reasons for Taking the COVID-19 Vaccine by US Social Media Users

Political and public health leaders promoting COVID-19 vaccination should identify the most relevant criteria driving the vaccination decision. Social media is increasingly used as a source of vaccination data and as a powerful communication tool to increase vaccination. In December 2020, we performed a cross-sectional social media-based survey addressing personal sentiments toward COVID-19 vaccination in the USA. Our primary research objective is to identify socio-demographic characteristics and the reasons for the 1644 survey participants’ attitudes regarding vaccination. We present clear evidence that, contrary to the prevailing public perceptions, young audiences using social media have mostly a positive attitude towards COVID-19 vaccination (81.5%). These younger individuals want to protect their families and their relatives (96.7%); they see vaccination as an act of civic responsibility (91.9%) and express strong confidence in their healthcare providers (87.7%). Another critical factor is the younger population’s fear of personal COVID-19 infection (88.2%); moreover, the greater the number of children the participants have, the greater is their intent to get the COVID-19 vaccine. These results enable a practical public-messaging pathway to reinforce vaccination campaigns addressing the younger population.

Social Media Engagement and Influenza Vaccination During the COVID-19 Pandemic: Cross-sectional Survey Study

Background

Vaccines are one of the most important achievements of modern medicine. However, their acceptance is only partial, with vaccine hesitancy and refusal representing a major health threat. Influenza vaccines have low compliance since repeated, annual vaccination is required. Influenza vaccines stimulate discussions both in the real world and online. Social media is currently a significant source of health and medical information. Elucidating the association between social media engagement and influenza vaccination is important and may be applicable to other vaccines, including ones against COVID-19.

Objective

The goal of this study is to characterize profiles of social media engagement regarding the influenza vaccine and their association with knowledge and compliance in order to support improvement of future web-associated vaccination campaigns.

Methods

A weblink to an online survey in Hebrew was disseminated over social media and messaging platforms. The survey answers were collected during April 2020. Anonymous and volunteer participants aged 21 years and over answered 30 questions related to sociodemographics; social media usage; influenza- and vaccine-related knowledge and behavior; health-related information searching, its reliability, and its influence; and COVID-19-related information searching. A univariate descriptive data analysis was performed, followed by multivariate analysis via building a decision tree to define the most important attributes associated with vaccination compliance.

Results

A total of 213 subjects responded to the survey, of whom 207 were included in the analysis; the majority of the respondents were female, were aged 21 to 40 years, had 1 to 2 children, lived in central Israel, were secular Israeli natives, had higher education, and had a salary close to the national average. Most respondents (128/207, 61.8%) were not vaccinated against influenza in 2019 and used social media. Participants that used social media were younger, secular, and living in high-density agglomerations and had lower influenza vaccination rates. The perceived influence and reliability of the information on social media about COVID-19 were generally similar to those perceptions about influenza.

Conclusions

Using social media is negatively linked to compliance with seasonal influenza vaccination in this study. A high proportion of noncompliant individuals can lead to increased consumption of health care services and can, therefore, overload these health services. This is particularly crucial with a concomitant outbreak, such as COVID-19. Health care professionals should use improved and targeted health communication campaigns with the aid of experts in social media. Targeted communication, based on sociodemographic factors and personalized social media usage, might increase influenza vaccination rates and compliance with other vaccines as well.

One Digital Health: A Unified Framework for Future Health Ecosystems

One Digital Health is a proposed unified structure. The conceptual framework of the One Digital Health Steering Wheel is built around two keys (ie, One Health and digital health), three perspectives (ie, individual health and well-being, population and society, and ecosystem), and five dimensions (ie, citizens’ engagement, education, environment, human and veterinary health care, and Healthcare Industry 4.0). One Digital Health aims to digitally transform future health ecosystems, by implementing a systemic health and life sciences approach that takes into account broad digital technology perspectives on human health, animal health, and the management of the surrounding environment. This approach allows for the examination of how future generations of health informaticians can address the intrinsic complexity of novel health and care scenarios in digitally transformed health ecosystems. In the emerging hybrid landscape, citizens and their health data have been called to play a central role in the management of individual-level and population-level perspective data. The main challenges of One Digital Health include facilitating and improving interactions between One Health and digital health communities, to allow for efficient interactions and the delivery of near–real-time, data-driven contributions in systems medicine and systems ecology. However, digital health literacy; the capacity to understand and engage in health prevention activities; self-management; and collaboration in the prevention, control, and alleviation of potential problems are necessary in systemic, ecosystem-driven public health and data science research. Therefore, people in a healthy One Digital Health ecosystem must use an active and forceful approach to prevent and manage health crises and disasters, such as the COVID-19 pandemic.

An Early Stage Researcher's Primer on Systems Medicine Terminology

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Communication Behavior Changes Between Patients With Diabetes and Healthcare Providers Over 9 Years: Retrospective Cohort Study

BACKGROUND

Health organizations and patients interact over different communication channels and are harnessing digital communications for this purpose. Assisting health organizations to improve, adapt, and introduce new patient-health care practitioner communication channels (such as patient portals, mobile apps, and text messaging) enhances health care services access.

OBJECTIVE

This retrospective data study aims to assist health care administrators and policy makers to improve and personalize communication between patients and health care professionals by expanding the capabilities of current communication channels and introducing new ones. Our main hypothesis is that patient follow-up and clinical outcomes are influenced by their preferred communication channels with the health care organization.

METHODS

This study analyzes data stored in electronic medical records and logs documenting access to various communication channels between patients and a health organization (Clalit Health Services, Israel). Data were collected between 2008 and 2016 from records of 311,168 patients diagnosed with diabetes, aged 21 years and over, members of Clalit at least since 2007, and still alive in 2016. The analysis consisted of characterizing the use profiles of communication channels over time and used clustering for discretization purposes and patient profile building and then a hierarchical clustering and heatmaps to visualize the different communication profiles.

RESULTS

A total of 13 profiles of patients were identified and characterized. We have shown how the communication channels provided by the health organization influence the communication behavior of patients. We observed how different patients respond differently to technological means of communication and change or don't change their communication patterns with the health care organization based on the communication channels available to them.

CONCLUSIONS

Identifying the channels of communication within the health organization and which are preferred by each patient creates an opportunity to convey messages adapted to the patient in the most appropriate way. The greater the likelihood that the therapeutic message is received by the patient, the greater the patient's response and proactiveness to the treatment will be.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/10734.

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

The EFMI Working Group "Healthcare Informatics for Interregional Cooperation": An Evolving Strategy for Building Cooperation Bridges

The Working Group "Health Informatics for Interregional Cooperation" (WG HIIC) of the European Federation for Medical Informatics (EFMI) is dedicated to develop, to implement and to disseminate a strategy for promoting exchange of information, knowledge and experiences all around the world and more particularly, between Health Informatics arena players in the different European continent regions.

A Multi-Lingual Dictionary for Health Informatics as an International Cooperation Pillar

Even if, English is generally used for international communication, it is essential to keep in mind that research is running at the local level by local teams generally communicating in their local/national language. Bearing these in mind, the "European Federation for Medical Informatics Working Group on Health Informatics for Inter-regional Cooperation" has as one of its objectives, to develop a multilingual dictionary focusing on Health Informatics as a collaboration tool allowing improving international and more particularly European cooperation. This dictionary is implemented as a part of HeTOP (Health Terminology/Ontology Portal) which is currently integrating more than 70 terminologies and ontologies in 32 languages. The EFMI Dictionary main aims are helping medical librarians, translators, academic and industrial researchers understanding better one another and supporting students self-learning.

Incident gout and chronic Kidney Disease: healthcare utilization and survival

Background

Uncontrolled gout can cause significant joint and organ damage and has been associated with impairments in quality of life and high economic cost. Gout has also been associated with other comorbid diseases, such as chronic kidney disease. The current study explored if healthcare resource utilization (HRU) and survival differs between patients with incident gout in the presence or absence of chronic kidney disease (CKD).

Methods

Clalit Health Services (CHS) data were used to conduct a retrospective population-based cohort study of incident gout between 1/1/2006–31/12/2009. Incident cases of gout were identified and stratified by CKD status and by age group (< 55 and 55+ years). CKD status was defined as a pre-existing diagnosis of chronic kidney disease, chronic renal failure, kidney transplantation, or dialysis at index date. Demographic and clinical characteristics, as well as healthcare resource use, were reported.

Results

A total of 12,940 incident adult gout patients, with (n = 8286) and without (n = 4654) CKD, were followed for 55,206 person-years. Higher rates of HRU were observed for gout patients with CKD than without. Total annual hospital admissions for patients with gout and CKD were at least 3 times higher for adults < 55 (mean = 0.51 vs 0.13) and approximately 1.5 times higher for adults 55+ (mean = 0.46 vs 0.29) without CKD. Healthcare utilization rates from year 1 to year 5 remained similar for gout patients < 55 years irrespective of CKD status, however varied according to healthcare utilization by CKD status for gout patients 55+ years. The 5-year all-cause mortality was higher among those with CKD compared to those without CKD for both age groups (HR_{< 55 years} = 1.65; 95% CI 1.01–2.71; HR_{55+ years} = 1.50; 95% CI 1.37–1.65).

Conclusions

The current study suggests important differences exist in patient characteristics and outcomes among patients with gout and CKD. Healthcare utilization differed between sub-populations, age and comorbidities, over the study period and the 5-year mortality risk was higher for gout patients with CKD, regardless of age. Future work should explore factors associated with these outcomes and barriers to gout control in order to enhance patient management among this high-risk subgroup.

Population-based cohort of 500 patients with Gaucher disease in Israel

OBJECTIVE

To characterise a population-based cohort of patients with Gaucher disease (GD) in Israel relative to the general population and describe sociodemographic and clinical differences by disease severity (ie, enzyme replacement therapy [ERT] use).

DESIGN

A cross-sectional study was conducted.

SETTING

Data from the Clalit Health Services electronic health record (EHR) database were used.

PARTICIPANTS

The study population included all patients in the Clalit EHR database identified as having GD as of 30 June 2014.

RESULTS

A total of 500 patients with GD were identified and assessed. The majority were ≥18 years of age (90.6%), female (54.0%), Jewish (93.6%) and 34.8% had high socioeconomic status, compared with 19.0% in the general Clalit population. Over half of patients with GD with available data (51.0%) were overweight/obese and 63.5% had a Charlson Comorbidity Index ≥1, compared with 46.6% and 30.4%, respectively, in the general Clalit population. The majority of patients with GD had a history of anaemia (69.6%) or thrombocytopaenia (62.0%), 40.4% had a history of bone events and 22.2% had a history of cancer. Overall, 41.2% had received ERT.

CONCLUSIONS

Establishing a population-based cohort of patients with GD is essential to understanding disease progression and management. In this study, we highlight the need for physicians to monitor patients with GD regardless of their ERT status.

A Social Networks Data Historian Supporting Research in Emergency & Disaster Medicine and Management

The aim of this initial research is to show that data and information collected from Internet Social Networks support the understanding of individual and collective behaviors which can help emergencies and disasters managers to mitigate and to improve preparedness programs for future similar events and to make more suitable decisions.

Patterns of Patients' Interactions With a Health Care Organization and Their Impacts on Health Quality Measurements: Protocol for a Retrospective Cohort Study

Background

Data collected by health care organizations consist of medical information and documentation of interactions with patients through different communication channels. This enables the health care organization to measure various features of its performance such as activity, efficiency, adherence to a treatment, and different quality indicators. This information can be linked to sociodemographic, clinical, and communication data with the health care providers and administrative teams. Analyzing all these measurements together may provide insights into the different types of patient behaviors or more accurately to the different types of interactions patients have with the health care organizations.

Objective

The primary aim of this study is to characterize usage profiles of the available communication channels with the health care organization. The main objective is to suggest new ways to encourage the usage of the most appropriate communication channel based on the patient’s profile. The first hypothesis is that the patient’s follow-up and clinical outcomes are influenced by the patient’s preferred communication channels with the health care organization. The second hypothesis is that the adoption of newly introduced communication channels between the patient and the health care organization is influenced by the patient’s sociodemographic or clinical profile. The third hypothesis is that the introduction of a new communication channel influences the usage of existing communication channels.

Methods

All relevant data will be extracted from the Clalit Health Services data warehouse, the largest health care management organization in Israel. Data analysis process will use data mining approach as a process of discovering new knowledge and dealing with processing data extracted with statistical methods, machine learning algorithms, and information visualization tools. More specifically, we will mainly use the k-means clustering algorithm for discretization purposes and patients’ profile building, a hierarchical clustering algorithm, and heat maps for generating a visualization of the different communication profiles. In addition, patients’ interviews will be conducted to complement the information drawn from the data analysis phase with the aim of suggesting ways to optimize existing communication flows.

Results

The project was funded in 2016. Data analysis is currently under way and the results are expected to be submitted for publication in 2019. Identification of patient profiles will allow the health care organization to improve its accessibility to patients and their engagement, which in turn will achieve a better treatment adherence, quality of care, and patient experience.

Conclusions

Defining solutions to increase patient accessibility to health care organization by matching the communication channels to the patient’s profile and to change the health care organization’s communication with the patient to a highly proactive one will increase the patient’s engagement according to his or her profile.

International Registered Report Identifier (IRRID)

RR1-10.2196/10734

Risk and Disaster Management: From Planning and Expertise to Smart, Intelligent, and Adaptive Systems

"Disaster" means some surprising and misfortunate event. Its definition is broad and relates to complex environments. Medical Informatics approaches, methodologies and systems are used as a part of Disaster and Emergency Management systems. At the Holon Institute of Technology - HIT, Israel, in 2016 a National R&amp;D Center: AFRAN was established to study the disaster's reduction aspects. The Center's designation is to investigate and produce new approaches, methodologies and to offer recommendations in the fields of disaster mitigation, preparedness, response and recovery and to disseminate disaster's knowledge. Adjoint to the Center a "Smart, Intelligent, and Adaptive Systems" laboratory (SIAS) was established with the goal to study the applications of Information and Communication Technologies (ICT) and Artificial Intelligence (AI) to Risk and Disaster Management (RDM). In this paper, we are redefining the concept of Disaster, pointing-out how ICT, AI, in the Big Data era, are central players in the RDM game. In addition we show the merit of the Center and lab combination to the benefit of the performed research projects.

Healthcare Informatics Project-Based Learning: An Example of a Technology Management Graduation Project Focusing on Veterinary Medicine

Teaching Healthcare Informatics using Project-Based Learning focuses students on active and inquiry-based learning and allows them to gain some knowledge and skills in the field. From the perspective of Technology Management, which is at the cross-road of Sciences, Engineering and Business Administration studies, Healthcare Informatics is an interesting application domain for developing both innovation and management capabilities. However, the specificities of Healthcare Informatics (standards, methodologies, human- or animal-focused information) require an additional involvement from the students to deliver projects that fit real-world needs and constraints. In this paper, we initially define the Technology Management field and describe how it is related to Healthcare Informatics, then we introduce Project-Based Learning and finally we present an example of a graduation project that focuses on Veterinary Medicine.

Identification and Description of Healthcare Customer Communication Patterns Among Individuals with Diabetes in Clalit Health Services: A Retrospective Database Study

HMOs record medical data and their interactions with patients. Using this data we strive to identify sub-populations of healthcare customers based on their communication patterns and characterize these sub-populations by their socio-demographic, medical, treatment effectiveness, and treatment adherence profiles. This work will be used to develop tools and interventions aimed at improving patient care. The process included: (1) Extracting socio-demographic, clinical, laboratory, and communication data of 309,460 patients with diabetes in 2015, aged 32+ years, having 7+ years of the disease treated by Clalit Healthcare Services; (2) Reducing dimensions of continuous variables; (3) Finding the K communication-patterns clusters; (4) Building a hierarchical clustering and its associated heatmap to summarize the discovered clusters; (5) Analyzing the clusters found; (6) Validating results epidemiologically. Such a process supports understanding different communication-channel usage and the implementation of personalized services focusing on patients' needs and preferences.

DisEpi: Compact Visualization as a Tool for Applied Epidemiological Research

Outcomes research and evidence-based medical practice is being positively impacted by proliferation of healthcare databases. Modern epidemiologic studies require complex data comprehension. A new tool, DisEpi, facilitates visual exploration of epidemiological data supporting Public Health Knowledge Discovery. It provides domain-experts a compact visualization of information at the population level. In this study, DisEpi is applied to Attention-Deficit/Hyperactivity Disorder (ADHD) patients within Clalit Health Services, analyzing the socio-demographic and ADHD filled prescription data between 2006 and 2016 of 1,605,800 children aged 6 to 17 years. DisEpi's goals facilitate the identification of (1) Links between attributes and/or events, (2) Changes in these relationships over time, and (3) Clusters of population attributes for similar trends. DisEpi combines hierarchical clustering graphics and a heatmap where color shades reflect disease time-trends. In the ADHD context, DisEpi allowed the domain-expert to visually analyze a snapshot summary of data mining results. Accordingly, the domain-expert was able to efficiently identify that: (1) Relatively younger children and particularly youngest children in class are treated more often, (2) Medication incidence increased between 2006 and 2011 but then stabilized, and (3) Progression rates of medication incidence is different for each of the 3 main discovered clusters (aka: profiles) of treated children. DisEpi delivered results similar to those previously published which used classical statistical approaches. DisEpi requires minimal preparation and fewer iterations, generating results in a user-friendly format for the domain-expert. DisEpi will be wrapped as a package containing the end-to-end discovery process. Optionally, it may provide automated annotation using calendar events (such as policy changes or media interests), which can improve discovery efficiency, interpretation, and policy implementation.

Obesity or smoking: Which factor contributes more to the incidence of myocardial infarction?

OBJECTIVE

Comparing the contributions of smoking and obesity to the risk of myocardial infarction (MI) can help prioritize behavioral modifications. The objective of this study was to determine the relative risk of smoking, obesity and the joint burden on the risk of MI.

METHODS

This is a retrospective cohort study of data accessed from electronic medical records of the largest health care organization in Israel. The study population included all 738,380 members of Clalit Health Services, with at least one smoking status and one BMI assessment recorded in 2009 or 2010, aged 40-74years, who were MI-free before 2009. Obesity was defined as BMI >30kg/m(2). New and primary MI between January 1 and December 31, 2011 were recorded.

RESULTS

Rates of MI were: 0.18% for non-obese never smokers, 0.25% for obese never smokers, 0.40% for non-obese past smokers, 0.50% for obese past smokers, 0.53% for non-obese current smokers and 0.66% for obese current smokers. Among non-obese individuals, past smokers and current smokers had a greater risk of MI than did never smokers, after adjusting for age, gender and socioeconomic position (OR, 1.45; 95% CI, 1.23-1.70 and OR, 2.35; 95% CI, 2.10-2.63, respectively). The burden of obesity increased the risk of MI for never smokers but the burden of obesity did not elevate the risk of MI when combined with current or past smoking groups, after adjusting for comorbidities.

CONCLUSIONS

Past and, more so, current smoking confers greater risk for MI than obesity.

Stimulant use for ADHD and relative age in class among children in Israel

BACKGROUND

Diagnosis of children with attention-deficit/hyperactivity disorder (ADHD) is increasing. The present study sought to identify characteristics and medication treatment patterns of children with ADHD and compare them by relative age in class, sex, ethnicity, family size, sibling order, and other socioeconomic status, as well as find trends in disparity of pharmacotherapy.

METHODS

This study was based on data from 1 013 149 Clalit Health Services members aged 6-17 years during 2006-2011. Centrally acting sympathomimetic drug purchases were compared according to children's estimated relative age in class; youngest third (born August to November), middle third (born April to July), and oldest third (born December to March). Treatment trends were determined and compared according to sociodemographic and family-related factors.

RESULTS

The overall prevalence of stimulant use in the population was 2.6% in 2006 and 4.9% in 2011. The annual incidence of stimulant use increased from 0.75% to 1.36%, rising more sharply among children in the older age groups (≥12) than among younger ones. Moreover, the youngest third of children in class was more likely to use medication than the oldest third (risk ratio (RR) 1.17, confidence interval (CI) 1.12-1.23) or the middle third (RR 1.06, CI 1.01-1.11). Of the different ethnic sectors, incidence of stimulant use was highest among general Jewish (1.8% in 2011) and lowest among Arabs (0.37% in 2011).

CONCLUSIONS

The use of stimulant medication is growing among children in Israel. Although the overall use does not exceed the estimated prevalence of ADHD among children, the appropriateness of prescribing to the Israeli pediatric population, especially to the youngest children in class, may be questionable. Copyright © 2016 John Wiley & Sons, Ltd.

Dynamics of circulating hypoxia-mediated miRNAs and tumor response in patients with high-grade glioma treated with bevacizumab

OBJECTIVE Bevacizumab is an antiangiogenic agent under investigation for use in patients with high-grade glioma. It produces a high rate of radiological response; however, this response should be interpreted with caution because it may reflect normalization of the tumor vasculature and not necessarily a true antitumor effect. The authors previously demonstrated that 4 hypoxia-mediated microRNAs (miRNA)-miR-210, miR-21, miR-10b, and miR-196b-are upregulated in glioma as compared with normal brain tissue. The authors hypothesized that the regulation and expression of these miRNAs would be altered in response to bevacizumab treatment. The object of this study was to perform longitudinal monitoring of circulating miRNA levels in patients undergoing bevacizumab treatment and to correlate it with tumor response. METHODS A total of 120 serum samples from 28 patients with high-grade glioma were prospectively collected prior to bevacizumab (n = 15) or temozolomide (TMZ; n = 13) treatment and then longitudinally during treatment. Quantification of the 4 miRNAs was evaluated by real-time polymerase chain reaction using total RNA extracted from the serum. At each time point, tumor response was assessed by Response Assessment in Neuro-Oncology criteria and by performing MRI using fluid attenuated inversion recovery (FLAIR) and contrast-enhanced images. RESULTS As compared with pretreatment levels, high levels of miR-10b and miR-21 were observed in the majority of patients throughout the bevacizumab treatment period. miR-10b and miR-21 levels correlated negatively and significantly with changes in enhancing tumor diameters (r = -0.648, p < 0.0001) in the bevacizumab group but not in the TMZ group. FLAIR images and the RANO assessment did not correlate with the sum quantification of these miRNAs in either group. CONCLUSIONS Circulating levels of miR-10b and miR-21 probably reflect the antiangiogenic effect of therapy, but their role as biomarkers for tumor response remains uncertain and requires further investigation.