Developing an App for Cardiovascular Prevention and Scientific Data Collection

Background: Mobile apps may encourage a lifestyle that avoids unhealthy behaviors, such as smoking or poor nutrition, which promotes cardiovascular diseases (CVD). Yet, little data is available on the utilization, perception, and long-term effects of such apps to prevent CVD. Objectives: To develop a mobile app concept to reduce the individual CVD risk and collect information addressing research questions on CVD prevention while preserving data privacy and security. Methods: To validate the concept, a prototype will be built, and usability studies will be performed. Results: We expect to determine whether it is possible to reach a broad user base and to collect scientific information while protecting user data sufficiently. Conclusion: To address CVD prevention, we propose a mobile coaching app. We expect high acceptance rates in validation studies.

Enabling FAIR Discovery of Rare Disease Digital Resources

Background: Integration of heterogenous resources is key for Rare Disease research. Within the EJP RD, common Application Programming Interface specifications are proposed for discovery of resources and data records. This is not sufficient for automated processing between RD resources and meeting the FAIR principles. Objective: To design a solution to improve FAIR for machines for the EJP RD API specification. Methods: A FAIR Data Point is used to expose machine-actionable metadata of digital resources and it is configured to store its content to a semantic database to be FAIR at the source. Results: A solution was designed based on grlc server as middleware to implement the EJP RD API specification on top of the FDP. Conclusion: grlc reduces potential API implementation overhead faced by maintainers who use FAIR at the source.

Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events

Background: Patients with major adverse cardiovascular events (MACE) such as myocardial infarction or stroke suffer from frequent hospitalizations and have high mortality rates. By identifying patients at risk at an early stage, MACE can be prevented with the right interventions. Objectives: The aim of this study was to develop machine learning-based models for the 5-year risk prediction of MACE. Methods: The data used for modelling included electronic medical records of more than 128,000 patients including 29,262 patients with MACE. A feature selection based on filter and embedded methods resulted in 826 features for modelling. Different machine learning methods were used for modelling on the training data. Results: A random forest model achieved the best calibration and discriminative performance on a separate test data set with an AUROC of 0.88. Conclusion: The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determine the performance of these models and their clinical benefit in prospective settings.

First Accreditation of a Medical Informatics Program by the European Federation of Medical Informatics: Experiences

Background: European universities offer a variety of programs in Medical Informatics. The Europen Federation of Medical Informatics (EFMI) offers accreditation of these programs. Objectives: To describe the process of EFMI accreditation of a new Austrian master’s program and reflect on accreditation benefits. Methods: Reflection on feedback and experiences at UMIT TIROL Results: Accreditation needs quite some preparation but offers essential opportunities for self-reflection and feedback by international experts. Conclusion: Besides national accreditation, medical informatics programs can benefit from the accreditation through international organizations.

Co-Designing a Primary Care Breathlessness Decision Support System: General Practitioners Requirements Analysis, Workflow Assessment and Prototype Development

Clinical decision support systems (CDSS) have been shown in a variety of diseases to lead to improvements in care. The aim of this study is to design a CDSS to assist GPs to assess and manage breathlessness, a highly prevalent symptom in practice. A focus group is conducted to explore the needs of general practitioners (GPs), assess current workflow to identify points for intervention and develop early prototypes for testing. Five GPs took part in the focus group elucidating 248 relevant data points which were then qualitatively analyzed using the Technology Acceptance Model as the theoretical framework. In general, there was a positive attitude towards the use of CDSS for breathlessness with various proposed features from the participants. Twelve high level workflow steps were identified with 5 as key points for intervention. Several proposed features such as reporting likelihood of causes of breathlessness in a patient, link with evidence-based recommendations, integration with clinical notes and patient education materials were translated into a prototype. Mixed-method studies are planned to assess its usability to inform subsequent iterations of the CDSS development.

Pathway-Supporting Health Information Systems: A Review

Care pathways and supporting health information systems (HIS) have been permeate the discipline of Health Information Systems Research (HISR) over years. Traditional objectives of workflow assistance are increasingly extended by interdisciplinary goals from technology, medicine, management and public health research. A systematic literature review is dedicated to this integrating character. It examines the interdisciplinary mesh of objectives associated with care pathways and pathway-supporting HIS in the HISR literature. From 47 identified articles, 6 thematic themes were derived. Their consolidation supports in particular design and development processes as it describes the solution space of future pathway-supporting HIS addressing requirements stated by multiple stakeholders.

Health Pandemic and Social Media: A Content Analysis of COVID-Related Posts on a Telegram Channel With More Than One Million Subscribers

Background: Mobile-based social media play an important role in the dissemination of information during public health emergencies. Objectives: This study aimed to analyze the contents and trends of public messages posted on Telegram during Coronavirus Disease 2019 (COVID-19) pandemic. Methods: A content analysis of the 1781 messages, posted in a public Telegram channel with more than one million subscribers performed over 9-weeks. The messages were categorized into seven categories. Results: In total, 39% (n=703) of all messages were related to COVID-19. With the official confirmation of the case of COVID-19 in Iran, the number of COVID-related massages started to rise. Overall, the most frequent messages were of joke and humor (n=292, 41.5%), followed by educational messages (n=140, 19.9%). Conclusion: Our study showed that the most popular messages during first weeks of COVID pandemic were satirical, indicating that people may not had taken the risks of this pandemic seriously. It is crucial for health organizations to develop strategies for dissemination of reliable health information through social media.

A Mechanism for Verifying the Integrity and Immutability of Tuberculosis Data Using IOTA Distributed Ledger Technology

Background: Intensified research and innovation and rapid uptake of new tools, interventions, and strategies are crucial to fight Tuberculosis, the world’s deadliest infectious disease. The sharing of health data remains a significant challenge. Data consumers must be able to verify the consistency and integrity of data. Solutions based on distributed ledger technologies may be adequate, where each member in a network holds a unique credential and stores an identical copy of the ledger and contributes to the collective process of validating and certifying digital transactions. Objectives: This work proposes a mechanism and presents a use case in Digital Health to allow the verification of integrity and immutability of TB electronic health records. Methods: IOTA was selected as a supporting tool due to its data immutability, traceability and tamper-proof characteristics. Results: A mechanism to verify the integrity of data through hash functions and the IOTA network is proposed. Then, a set of TB related information systems was integrated with the network. Conclusion: IOTA technology offers performance and flexibility to enable a reliable environment for electronic health records.

Introducing a Navigation Algorithm for Reducing the Spread of Diseases in Public Transport Networks

Reducing passenger flow through highly frequented bottlenecks in public transportation networks is a well-known urban planning problem. This issue has become even more relevant since the outbreak of the SARS-CoV-2 pandemic and the necessity for minimum distances between passengers. We propose an approach that allows to dynamically navigate passengers around dangerously crowded stations to better distribute the passenger load across an entire urban public transport network. This is achieved through the introduction of new constraints into routing requests, that enable the avoidance of specific nodes in a network. These requests consider walks, bikes, metros, subways, trams and buses as possible modes of transportation. An implementation of the approach is provided in cooperation with the Munich Travel Corporation (MVG) for the city of Munich, to simulate the effects on a real city’s urban traffic flow. Among other factors, the impact on the travel time was simulated given that the two major exchange points in the network were to be avoided. With an increase from 26.5 to 26.8 minutes on the average travel time, the simulation suggests that the time penalty might be worth the safety benefits.

Topic Discovery on Farsi, English, French, and Arabic Tweets Related to COVID-19 Using Text Mining Techniques

Background: Social networks are a good source for monitoring public health during the outbreak of COVID-19, these networks play an important role in identifying useful information. Objectives: This study aims to draw a comparison of the public’s reaction in Twitter among the countries of West Asia (a.k.a Middle East) and North Africa in order to make an understanding of their response regarding the same global threat. Methods: 766,630 tweets in four languages (Arabic, English French, and Farsi) tweeted in March 2020, were investigated. Results: The results indicate that the only common theme among all languages is “government responsibilities (political)” which indicates the importance of this subject for all nations. Conclusion: Although nations react similarly in some aspects, they respond differently in others and therefore, policy localization is a vital step in confronting problems such as COVID-19 pandemic.