Ebook: The Importance of Health Informatics in Public Health during a Pandemic

This volume contains the accepted papers of the ICIMTH (International Conference on Informatics, Management, and Technology in Healthcare). The Scientific Programme Committee presents to the academic and professional community of Biomedical and Health Informatics the scientific outcomes of the ICIMTH 2020 Conference, which was held virtually from 3 to 5 July 2020 in Athens, Greece.

The ICIMTH 2020 Conference is the 18th Annual Conference in this series of scientific events, gathering scientists working in the field of Biomedical and Health Informatics from all continents.

This year the Conference was held as a Virtual Conference, by means of teleconferencing interactive platforms and equipment, due to the COVID-19 pandemic and the consequent result of lockdown of most of the countries in the world.

The call for papers started early, from December 2019, when no sign of the new virus infection was evident. During the first appearance of the virus in China in mid-January, until mid-March when it first appeared in Italy, most papers submitted were on the usual topics as announced on the Call for Papers. Papers collected after mid-March until early May were mostly focused on the first results of the pandemic analysis with respect to informatics at different countries and with different perspectives of the spread of the virus and the influence on Public Health across the world. So, we are proud in these proceedings to have included papers on the topic of the COVID-19 pandemic in relation to Informatics reporting from different hospitals and institutions of the world from South Korea, to Europe, and the USA.

We are examining the field of Biomedical and Health Informatics in a very broad framework, presenting the research and application outcomes of Informatics from cell to populations, including a number of technologies such as Imaging, Sensors, and Biomedical Equipment and Management and Organisational aspects, including legal and social issues and setting research priorities in Health Informatics. Essentially, Data, Informatics, and Technology inspire health professionals and informaticians to improve healthcare for the benefit of patients.

It should be noted that the Proceedings are published as Open Access with e-access for ease of use and browsing without losing any of the advantages of indexing and citation in the biggest Scientific Literature Databases, such as Medline and Scopus, that the series of Studies in Health Technology and Informatics (SHTI) of IOS Press provides.

At the end of the deadline we had 160 submissions, from which after reviewing we have accepted 110 as papers to be included in the volume proceedings.

The Editors would like to thank the Members of the Scientific Programme Committee, the Organising Committee, and all Reviewers, who have performed a very professional, thorough and objective refereeing of the scientific work in order to achieve a high-quality publishing achievement for a successful scientific event.

Athens, 30.05.2020

The Editors,

John Mantas, Arie Hasman, Mowafa S. Househ, Parisis Gallos and Emmanouil Zoulias

The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent clusters in COVID-19 patients. Over 6,000 adult patients tested positive for the SARS-CoV-2 infection at the Mount Sinai Health System in New York, USA met the inclusion criteria for analysis. Patients’ diagnoses were mapped onto chronicity and one of the 18 body systems, and the optimal number of clusters was determined using K-means algorithm and the elbow method. 4 clusters were identified; the most frequently associated comorbidities involved infectious, respiratory, cardiovascular, endocrine, and genitourinary disorders, as well as socioeconomic factors that influence health status and contact with health services. These results offer a strong direction for future research and more granular analysis.

We applied social network analysis (SNA) to Tweets mentioning cannabis or opioid-related terms to publicly available COVID-19 related Tweets collected from Jan 21^st to May 3^rd, 2020 (n= 2,558,474 Tweets). We randomly extracted 16,154 Tweets mentioning cannabis and 4,670 Tweets mentioning opioids from the COVID-19 Tweet corpora for our analysis. The cannabis related Tweets created by 6,144 users were disseminated to 280,042,783 users and retweeted 11 times the number of original messages while opioid-related Tweets created by 3,412 users were disseminated to smaller number of users. The opioids Twitter network showed more cohesive online group activities and a cleaner online environment with less disinformation. The cannabis Twitter network showed a less desirable online environment with more disinformation (false information to mislead the public) and stakeholders lacking strong science knowledge. Application of SNA to Tweets provides insights for future online-based drug abuse research during the outbreak.

The use of e-health services has for many years gradually increased in Norway as in most European countries. Searching for information about health and illness has previously by far been the most popular service. In this study, we review the literature with the aim of examining any changes in e-health use during the Covid-19 pandemic. We find that there has been a marked change in Norway, with an extreme increase in video consultations, especially in primary care and in the mental health field. The government has also released an app for tracking the illness, which so far has been downloaded by approximately 1/4 of the population. These changes are likely to impact the use of e-health also after the pandemic.

Coronavirus disease (COVID-19) constitutes an ongoing global health problem with significant morbidity and mortality. It usually presents characteristic findings on a chest CT scan, which may lead to early detection of the disease. A timely and accurate diagnosis of COVID-19 is the cornerstone for the prompt management of the patients. The aim of the present study was to evaluate the performance of an automated machine learning algorithm in the diagnosis of Covid-19 pneumonia using chest CT scans. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, and positive predictive value. The method’s average precision was 0.932. We suggest that auto-ML platforms help users with limited ML expertise train image recognition models by only uploading the examined dataset and performing some basic settings. Such methods could deliver significant potential benefits for patients in the future by allowing for earlier disease detection and care.

The increased prevalence and frequency of infectious diseases are alarming with respect to the disproportionate fatalities across different regions, socio-economic conditions, and demographic groups. Combining pathological data, socio-environmental data, and extracted knowledge from white papers, we proposed a Globally Localized Epidemic Knowledge base (GLEK) that can be utilized for efficient and optimal epidemic surveillance. GLEK merges social, environmental, pathological, and governmental intervention data to provide efficient advice for epidemic control and intervention. Heuristically utilizing multi-locus data sources, GLEK can identify the best tailored intervention.

During the last months the Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat. Transmission of the infection has rapidly increased in Europe as well as in Greece, living behind a huge number of deaths. During this situation an analysis of the spread of the disease must be undertaken and characteristics of the virus must be recognized. For the analysis of the impact of the disease in the population during this time period, epidemiological indexes have been introduced.

We randomly extracted publicly available Tweets mentioning COVID-19 related terms (n=2,558,474 Tweets) from Tweet corpora collected daily using an API from Jan 21^st to May 3^rd, 2020. We applied a clustering algorithm to publicly available Tweets authored by African Americans (n=1,763) to detect topics and sentiment applying natural language processing (NLP). We visualized fifteen topics (four themes) using network diagrams (Newman modularity 0.74). Compared to the COVID-19 related Tweets authored by others, positive sentiments, cohesively encouraging online discussions (e.g., Black strong 27.1%, growing up Blacks 22.8%, support Black business 17.0%, how to build resilience 7.8%), and COVID-19 prevention behaviors (e.g., masks 4.7%, encouraging social distancing 9.4%) were uniquely observed in African American Twitter communities. Application of topic modeling techniques to streaming social media Twitter provides the foundation for research team insights regarding information and future virtual based intervention and social media based health disparity research for COVID-19.

It is 200 years since the birth of Florence Nightingale. This keynote paper reviews some of her work relating to health statistics and outlines its continuing legacy to nursing informatics around the world and especially in poorer countries, like South Africa, in the 21st century.

The aim of our study is to propose a remote patient monitoring solution through a smart phone application (Smart Patient) collecting health data to support diagnosis, monitoring and predicting poor outcome in asymptomatic/mild cases of COVID-19, including signs and symptoms, risk factors, comorbidities, medications and vital signs such as body temperature, respiratory rate, heart rate and oxygen saturation. By continuous daily recording of suspected cases and patients, family doctors in the community will be able to follow up cases and intervene promptly when deterioration in vital signs and symptoms takes place referring the patient to the hospital.

Background:

Increasing numbers of intelligent healthcare applications are developed by analysing big data, on which they are trained. It is necessary to assure that such applications will be safe for patients; this entails validation against datasets. But datasets cannot be shared easily, due to privacy, and consent issues, resulting in delaying innovation. Realistic Synthetic Datasets (RSDs), equivalent to the real datasets, are seen as a solution to this.

Objective:

To develop the outline for safety justification of an application, validated with an RSD, and identify the safety evidence the RSD developers will need to generate.

Method:

Assurance case argument development approaches were used, including high level data related risk identification.

Result:

An outline of the justification of such applications, focusing on the contribution of the RSD.

Conclusions:

Use of RSD will require specific arguments and evidence, which will affect the adopted methods. Mutually supporting arguments can result in a compelling justification.

E-learning enables students to participate in online courses across universities. As a part of the HiGHmed joint teaching and training program, we developed an e-learning module entitled Health Enabling Technologies and Data based on the Gilly Salmon 5 stage model didactic concept. This course was implemented at a German Technical University in the winter semester 2019/20 and evaluated by the students after completion. Student evaluation indicates good teaching presence but improvable social presence. From the perspective of the lecturers, we have learned that interactivity should be enhanced to improve the students’ engagement, and incentives shall be established to foster students’ active participation. Therefore, we will revise the course for the next term by (i) web conferences, (ii) assessment of interactivity, and (iii) clear “take-home” messages.

One of the major regulatory factors for health informatics is data privacy protection. In the European Union, a shared set of laws has been implemented – the General Data Protection Regulation. While this set of rules aims at harmonizing the European data privacy protection standards, it fails in properly detailing the handling of anonymized data. This is a problem, as, for example many current research initiatives aim at reusing patient data collected within primary care, but lack a patient consent, hence, might rely on anonymized data as being the only alternative. Within this work, we detail different aspects why the concept of anonymity is wrongly handled within the GDPR and give suggestions how the laws could be adapted.

IT providers offering services based on genetic data face serious challenges in managing health data in compliance with the General Data Protection Regulation (GDPR). Based on a literature research and our experiences, an overview of GDPR compliant processing of sensitive data is given. The GDPR requirements for processing sensitive data were specified for a use case concerning a service provider of a pharmacogenomic decision support system. Start-ups who want to enter into the health market also have to comply with the Medical Device Regulation (MDR). The associated efforts for legal compliance constitute an impediment for many start-ups. We created a comprehensive overview, which aligned the requirements of the GDPR with the life-cycle of a medical device. This overview shall help start-ups to grasp and overcome the regulatory hurdles faster.

Studies in the last decade have focused on identifying patients at risk of readmission using predictive models, in an objective to decrease costs to the healthcare system. However, real-time models specifically identifying readmissions related to hospital adverse-events are still to be elaborated. A supervised learning approach was adopted using different machine learning algorithms based on features available directly from the hospital information system and on a validated dataset elaborated by a multidisciplinary expert consensus panel. Accuracy results upon testing were in line with comparable studies, and variable across algorithms, with the highest prediction given by Artificial Neuron Networks. Features importances relative to the prediction were identified, in order to provide better representation and interpretation of results. Such a model can pave the way to predictive models for readmissions related to patient harm, the establishment of a learning platform for clinical quality measurement and improvement, and in some cases for an improved clinical management of readmitted patients.

The automated detection of adverse events in medical records might be a cost-effective solution for patient safety management or pharmacovigilance. Our group proposed an information extraction algorithm (IEA) for detecting adverse events in neurosurgery using documents written in a natural rich-in-morphology language. In this paper, we challenge to optimize and evaluate its performance for the detection of any extremity muscle weakness in clinical texts. Our algorithm shows the accuracy of 0.96 and ROC AUC = 0.96 and might be easily implemented in other medical domains.

This cross-sectional research aimed to explore the associated factors with nursing practice for patient safety of nurses in Kalasin Hospital, Kalasin Province, Thailand. The 245 samples were randomized which using created questionnaires which was applied to collect their opinion. The results showed that level of nursing practice for patient safety was moderate level (average = 3.55, S.D. = 0.89) in addition to their experience in patient care, knowledge patient safety and safety culture skills was a significant relationship to practice of nursing professional skills with statistical significance (r = 0.625, p-value = 0.001). It was revealed that supporting system and resources sufficient for the top administrative level in hospital were significantly related to nursing practice and patient safety. Policy direction of the hospitals is becoming aware of the importance of transforming organizational culture in order to improve patient safety and nursing practice toward to safety culture as quality awareness in the organization.

Adverse events (AEs) in healthcare are commonly reported internationally. However, the structure of event reporting varies based on the healthcare service system, legislation, and the safety culture among service providers. Based on several studies, medication management-related errors have a long history of being reported. However, there is evidence that information management-related errors increase workload, costs, and patient suffering. This study focuses on AEs reported in the categories of medication and information management in a national reporting system. Our aim was to determine whether patients were informed about these errors and whether there were any relationships between the severity of these errors and the disclosure to patients. Based on the results, almost all errors in both categories without any harm to patients were disclosed to patients. Patients were not informed about 40% of information management-related AEs that caused severe harm.

This mixed-method research aimed to evaluate in need assessments and develop an intervention model for quality improvement information system-QIIS for surveillance and monitoring Patient safety and Personals safety in Kalasin Hospital, Thailand. The process was divided into 3 phases, 1) Studies on needs assessments of patients and hospital staff-2P, 2) Develop the program and assessment 3) Program implementation and evaluation. The 245 participants were participated during October 2019 – Februarys 2020. A questionnaire and semi-structure interview by content analysis were applied to investigate on needs assessments and intervention evaluation were using checklist for observation. The results revealed that information alert from supportive technology especially in real-time were sophisticated improving and significantly increasing with the positively and satisfaction levels of the staff increasingly particularly in pre and post observation (p-value <0.05). The key success of QIIS was comprised as suddenly response and information alert system, for this reason their staff could be immediately responsive to problems as much as they affords.

Rapid access to patient overall health status is essential for a physician during a medical consultation. The use of a HIS for the management of neonatal screening and follow-up of sickle cell disease patients at CERPAD in the Saint-Louis region of Senegal leads the patient electronic records growing in volume and complexity. To facilitate access to relevant information and shortens the time required to analyze and understand these clinical data, an original solution is to set up a data visualization system. In this article, we propose the integration of two iconic visualization tools into the SIMENS-CERPAD module designed for sickle cell screening and healthcare. The two tools use the VCM iconic language and consist of a simplified anatomical schema showing the current health status of the patient and a timeline to visualize its temporal evolution.

Multi-drug-resistant (MDR) infections and their devastating consequences constitute a global problem and a constant threat to public health with immense costs for their treatment. Early identification of the pathogen and its antibiotic resistance profile is crucial for a favorable outcome. Given the fact that more than 24 hours are usually required to perform common antibiotic resistance tests after the sample collection, the implementation of machine learning methods could be of significant help in selecting empirical antibiotic treatment based only on the sample type, Gram stain, and patient’s basic characteristics. In this paper, five machine learning (ML) algorithms have been tested to determine antibiotic susceptibility predictions using simple demographic data of the patients, as well as culture results and antibiotic susceptibility tests. Implementing ML algorithms to antimicrobial susceptibility data may offer insightful antibiotic susceptibility predictions to assist clinicians in decision-making regarding empirical treatment.

Registries usually operate an IT-infrastructure supporting at least data management as one of the business processes. Several activities in Germany between 2007 and 2018 surveyed the market of respective software products. Combining a survey with representatives of software products with a workshop protocol of software demonstrations, a detailed insight into the market of IT-components arose. A comparison between 2015 and 2018 revealed little progress. The focus is still electronic data capture functionality. With the presented activities, rich material is available to assist registry developers in the planning of their IT-infrastructure and the selection of software products.

Medical education can take advantage of big data to enhance the learning experience of students. This paper describes the development of TrainCoMorb, an online, data-driven application for medical students who can practice recognizing comorbidities and their attributable risk for negative outcomes. Trainees access TrainCoMorb to create scenarios of comorbidities, step-by-step, and see snapshots of the risk for inpatient death, hospital septicemia and the projected length of stay. The study utilized an enormous claims dataset (N=11m.). A dynamic Bayesian algorithm was developed, which calculates and updates conditional probabilities for the outcomes under study in each phase of an ongoing scenario. The trainee initiates a scenario by selecting demographics and a principal diagnosis, then adds chronic and hospital-acquired conditions to see a summary of the attributable risk in each phase. TrainCoMorb is anticipated to assist medical students gain a better understanding of comorbidities and their impact on clinical outcomes.

Social networks are new technologies that facilitate the sharing and exchange of information and knowledge as well as the communication between students and professors. The purpose of this study was to investigate views of medical and paramedical sciences students about benefits and barriers of virtual social networks for learning purposes. A cross-sectional study was carried out in 2017 and the research tool was a researcher-made questionnaire based on a literature review. The majority of students (medical= 93% and paramedical sciences= 84.7%) tended to use social networks for learning purposes. Also, the results showed that “sending and receiving educational videos” and “sending and receiving educational texts, posts and contents” was the highest priority for medical and paramedical sciences students for using social networks. Overall, the results of our study showed that the social networks can play an effective role in educating medical students and improve students’ motivation for learning. However, the use of these technologies also brings problems and challenges.

Drug synergy must be taken into account when prescribing several drugs for treating the same disorder. Synergies are sometimes known from clinical trials, but they are not systematically studied. In this paper, we present a visual approach for identifying and explaining the potential synergies between the 2–15 drugs available for a given disorder. It is based on the chaining of two bioinformatics databases, Drug Bank and Signor, and relies on set visualization with rainbow boxes. We apply this approach to antihypertensive drugs, and show that some European recommendations can be visually deduced and explained.