Ebook: Advances in Informatics, Management and Technology in Healthcare

This volume contains the accepted papers of the ICIMTH (International Conference on Informatics, Management, and Technology in Healthcare) for the year 2022, held in Athens, Greece, from 1–3 July 2022. The Scientific Programme Committee hereby presents the scientific outcomes of the ICIMTH 2022 Conference to the academic and professional Biomedical and Health Informatics community.

The ICIMTH 2022 Conference was the 20th Annual Conference in this series of scientific events, which attracts scientists working in the field of Biomedical and Health Informatics from all continents. This year, with the improvement in the pandemic situation as regards COVID-19 and the lifting of restrictions, the Conference was held as a live event. Virtual sessions by means of teleconferencing were, however, allowed for those who were unable to travel due to local restrictions in certain countries.

Data science, informatics, and technology inspire health professionals and informaticians to improve healthcare for the benefit of all patients, so the field of Biomedical and Health Informatics was examined from a very broad perspective at the Conference. Participants presented research and application outcomes of informatics from cell to populations, including several technologies such as imaging, sensors, biomedical equipment, and management and organisational aspects, including legal and social issues. As expected, a significant number of papers were still related to the COVID-19 pandemic. By the deadline, more than 230 submissions had been received from which, after review, 130 were accepted as full papers and 19 as short communication and poster papers.

The organisers would like to thank Dr. Spyros Zogas and Prof. John Mantas for the design of the book covers for the entire conference series. This year the cover commemorates the significance and contribution to medicine, nursing and healthcare of Asclepios, who introduced health institutions as a means of healthcare delivery in early antiquity; Hippocrates, the father of Western medicine; Galen, the most distinguished medical researcher of antiquity; Florence Nightingale, the founder of modern nursing and health statistics; and George Papanikolaou, the pioneer physician, founder of cytopathology, and inventor of the Pap-test.

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

Please note that these Proceedings are also published as an Open Access eBook, which allows e-access for ease of use and browsing without the loss of any of the advantages of indexing and citations, in the major scientific literature databases such as PubMed/Medline and Scopus which the series Studies in Health Technology and Informatics (HTI) of IOS Press provides.

Athens, 20.05.2022

The Editors,

John Mantas, Parisis Gallos, Emmanouil Zoulias, Arie Hasman, Mowafa S. Househ, Marianna Diomidous, Joseph Liaskos, and Martha Charalampidou

It is typical for many digital health research projects to develop IT architectures that will implement integrated care services that may also deliver interventions. As part of compliance with the requirements of the regulation, the components that are considered as a medical device will need to be classified to a medical device category. This is often seen as task that may increase the business risk and a major barrier of the project, particularly during the earlier stages when not all information is available. The paper offers a method assisting with classification of such architectures in the context of the Medical Devices Rregulation, offering a structured way to identifying how the initial deliverables of a project can be used to provide assurance to the justification of the classification.

The early warning system alarms the rapid response team (RRT) for clinical deterioration monitoring and prediction. Available systems do not perform well to decrease the number of ICU transfers or death. This study aimed to address the requirement of an intelligent warning system for timely and accurate RRT activation. Methodology: A literature review was conducted in scientific databases to extract data. Then, a questionnaire was developed for experts’ views collection (N=12). The collected data were analyzed using the Content Validity Ratio (CVR). According to the Lawshe table for the corresponding number of experts, the cut-off=0.56 for items to be accepted/rejected was considered. A schematic structure was suggested. Findings: The analysis of the extracted papers (N=24) and qualitative analysis addressed 44 requirements in the frame of five involved sub-systems, including a patient monitoring system, electronic health record, clinical decision support system, remote monitoring patient, and dashboard &registries. They were confirmed by meeting the least cut-off value (CVR= 0.86). Conclusion: An integrated approach and technologies of IoT, deep and machine learning techniques, big data, advanced databases, and standards to create an intelligent EWS are required.

The main goals of the Swedish eHealth strategy are to enable citizens to achieve good and equal health and welfare, and to support self-determination and increased participation in society. We analyzed the relationship between these goals and the use of eHealth services offered for citizens prior to and during the COVID-19 pandemic. Data was collected through a national citizen survey issued in 2019 and 2021 to a sample size of 15.000 representative individuals each. Results showed that the use of eHealth services was highest in the 30–49 years age group and among respondents with high education. There were no major differences between respondents with high, medium, or low income, and neither between respondents with different degrees of self-perceived health, nor between native Swedish and non-Swedish respondents. Changes in use of different eHealth services over time were most probably related to the pandemic and are not significant. All age groups showed a similar relative increase regarding their use of eHealth services, except when searching the Internet for diagnosis and treatment where persons above 75 years of age had the largest increase. Most significant were the increase in online visits and the decrease in maintaining health, training, or food diaries. Strategic goals related to equity seem to be partly met as eHealth services are used to the same degree by different socio-economic groups. However, the older population uses eHealth services less than other age groups and a deeper understanding of the relationship between specific services and their impact on strategic goals is needed.

Introduction:

OpenWHO provides open-access, online, free and real-time learning responses to health emergencies, which includes capacitating healthcare providers, first liners, medical students and even the general public. During the pandemic and to date, an additional 40 courses for COVID-19 response have led to a massive increase in the number of learners and a change in user’s trends. This paper presents initial findings on enrollment trends, use and completion rates of health emergency courses offered on OpenWHO.

Methods:

The enrolment data statistics were drawn from OpenWHO’s built-in reporting system, which tracks learners’ enrolments, completion rates, demographics and other key course-related data, This information was collected from the beginning of the OpenWHO launch in 2017 up until October 2021.

Results:

Average course completion rate on OpenWHO including all courses and languages was equal to 45.9%. Nearly half (46.4%) of all OpenWHO learners have enrolled in at least 2 courses and 71 000 superusers have completed at least 10 courses on the platform.

Conclusion:

WHO’s learning platform during the pandemic registered record high completion rates and repeat learners enrollment. This highlights the massive impact of the OpenWHO online learning platform for health emergencies and the tangible knowledge transfer and access to health literacy.

Over the past decade, Artificial Intelligence (AI) technologies have quickly become implemented in protecting data, including detecting fraud in healthcare organizations. This scoping review aims to explore AI solutions utilized in fraud detection occurring in treatment settings. To find relevant literature, PubMed and Google Scholar were searched. Out of 183 retrieved studies, 31 met all inclusion criteria. This review found that AI has been used to detect different types of fraud such as identify theft and kickbacks in healthcare. Additionally, this review discusses how AI techniques used in network mapping fraud can detect and visualize the hacker’s network. A proper system must be implemented in healthcare settings for successful fraud detection, which may overall improve the healthcare system.

Despite the prevailing perception that powerful software and hardware are adequate solutions to minimize information systems’ security breaches, privacy remains at stake. Human factors play an important role in maintaining information security as it is evident that non secure practices applied by employees may increase the vulnerability of the systems and lead to privacy issues. Non secure practices found in literature are related to the use of Internet, the use of emails, password management, information handling and incidence reporting. For this purpose a survey was designed to be conducted in seven hospitals in Greece in order to record non secure practices applied by nursing staff and identify correlation factors. This paper presents the reliability test of the HAIS-Q tool which was applied in order to conduct the survey entitled: Examining the Nurses’ non secure IT practices in Greek Hospitals as well as the preliminary results of the study.

Covid-19 is one of the most significant infectious diseases that have faced humanity in the past century from clinical, economic, and social perspectives. Although the role of infectious diseases in human history has been vicious and is well known to humanity, Covid-19 is a special case since it is the first worldwide outbreak in the era of advanced computing and telecommunications. For this reason, it was only logical to see Artificial Intelligence (AI) and Machine Learning (ML) on the top of the list of controls to compact the spread of Covid-19. This paper goes through the applications of AI and ML that were reported in some of the major literature indexes and can be related to the main issues that face healthcare providers during the Covid-19 pandemic. This paper also discusses the applicability of these applications to healthcare organizations and points out the main prerequisites before they can be adopted.

There exist numerous low-risk class I SaMDs with CE marking under European Medical Device Directives (MDD). However, if the manufacturers will make any significant change to these class I SaMDs, the manufacturers shall comply with Medical Device Regulation (MDR) 2017/745 classifications. Class I SaMDs are self-declared without the need for notified body involvement. It is unclear how these devices are monitored if they will undergo any significant changes. Significant change may shift existing low-risk class I SaMDs to higher risk classification. In another hand, it is not clear if all class I SaMDs that are certified under MDD are registered with relevant EU Competent Authorities. Class I SaMDs may have an impact on public health if they are not known and monitored by European competent authorities.

Medical Subject Headings (MeSH) is one of the most important vocabularies for information retrieval in medical research. It enables fast and reliable retrieval of research on PubMed/MEDLINE, the world’s largest body of medical literature. The original English version of the thesaurus can be accessed via a MeSH Browser developed by the NLM. Recently, a multilingual MeSH Browser was proposed to enable usage across languages. To improve upon the original system, a new user interface (UI) was developed using contemporary web design frameworks in combination with principles from cognitive science. It aims to simplify access for medical professionals and increase overall usability. Evaluating such design improvements continually is necessary to quantify the possible positive impact for online systems in medical research. This study therefore directly compares the resulting system to the NLM Browser, using an established online questionnaire. Results show significant improvements in content and navigation as well as overall user satisfaction, while offering feedback for future improvements. This underlines the benefits of employing contemporary web design in terms of usability and user satisfaction.

We developed and implemented a smartphone-based mobile application that uses speech recognition for the point-of-care ordering of radiological examinations. 21 out of 30 physicians completed a usability questionnaire including the Short version of the User Experience Questionnaire (UEQ-S) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The mobile application showed high user acceptance and superior user experience when compared to the conventional workflow. Due to the high usability of our mHealth solution, it might help to facilitate the physician’s daily work.

The use of eHealth components in healthcare is often viewed in the context of disruptive change and ethical pitfalls. We focus on interpersonal relationships between physicians, patients, and care providers and show that positive changes (also) occur within this context.

Medical reports are key elements to guarantee the quality, and continuity of care but their quality remains an issue. Standardization and structuration of reports can increase their quality, but are usually based on expert opinions. Here, we hypothesize that a structured model of medical reports could be learnt using machine learning on retrospective medical reports extracted from clinical data warehouses (CDW). To investigate our hypothesis, we extracted breast cancer operative reports from our CDW. Each document was preprocessed and split into sentences. Clustering was performed using TFIDF, Paraphrase or Universal Sentence Encoder along with K-Means, DBSCAN, or Hierarchical clustering. The best couple was TFIDF/K-Means, providing a sentence coverage of 89 % on our dataset; and allowing to identify 7 main categories of items to include in breast cancer operative reports. These results are encouraging for a document preset creation task and should then be validated and implemented in real life.

Wearable sensors and mHealth apps collect fitness and health data outside of clinical settings. These data are essential for precision medicine. This paper addresses and analyzes the available tools for extracting health and fitness data from wearables and mHealth apps. We focus on the most common tools used for research, namely, the Open mHealth-Shimmer application and Fitrockr research platform.

This is a study protocol to evaluate the Impact of digital games on learning medical terminology of paramedical students. An unblinded randomized controlled trial (RCT) 2-arm, with 1:1 allocation ratio was randomized by 60 students at the faculty of paramedical science at Mashhad University of Medical Science(MUMS), Iran, who had their medical terminology course at the time of this study, would enter the study. To evaluate the game, Participants in both groups attended typical teaching in traditional instructional activities for two months; however, the intervention group played the smartphone-based digital game during the course. The knowledge level of students in the control and intervention group were measured before and immediately after the intervention using the pre-designed questionnaire. This study was approved by the ethical committee of MUMS (approval number IR.MUMS.REC.1400.336).

The ERN-LUNG Population Registry is a new European-wide collection of patients with rare lung diseases, allowing patients to register online in the registry. Medical experts can recruit patients in the registry for disease-specific registries and care options. The Population Registry was implemented on the basis of the open source software OSSE and extended by functions for the self-registration of patients. Patients were invited through patient organizations between May and November 2022. 115 patients registered online in the registry, whereas 60 of them provided full data in the registry form. After first months of usage, further dissemination of the registry is necessary to reach more patients, e.g. by recruiting them via medical centres directly. Improvements of the registry should be conducted to achieve a higher number of fully completed forms.

There is a global emergency in relation to mental health (MH) and healthcare. In the UK each year, 1 in 4 people will experience MH problems. Healthcare services are increasingly oversubscribed, and COVID-19 has deepened the healthcare gap. We investigated the effect of COVID-19 on waiting times for MH services in Scotland. We used national registers of MH services provided by Public Health Scotland. The results show that waiting times for adults and children increased drastically during the pandemic. This was seen nationally and across most of the administrative regions of Scotland. We find, however, that child and adolescent services were comparatively less impacted by the pandemic than adult services. This is potentially due to prioritisation of paediatric patients, or due to an increasing demand on adult services triggered by the pandemic itself.

EHRs provide several benefits with the potential of improving healthcare quality. There is also a growing interest in using EHR for research purposes to improve clinical care and administrative processes. This paper examines the potential role of EHRs in supporting clinical research in primary care. Healthcare professionals (1710) working in primary care centers in Riyadh city, Saudi Arabia, were surveyed. The response rate was 65.9%. The majority of the respondents (76.0%) perceived EHR to provide quick and reliable access to scientific research. To improve EHR utilization in health research, the challenges related to its use should be addressed.

The implementation of a participatory technology development process has been realized within the development of an anti-decubitus system. Nurses as the user group are involved in that process. Beside results, the methodical background and the specific conditions for the realization are presented and discussed.

Registries in health research are complex systems requiring a diverse infrastructure with information and communications technology tools (ICT tools) for manifold tasks. Those tools should support not only data management but also several core and accompanying processes. Recent trends in registry research also need to be taken into account. Thirty-five vendors, suppliers, and experts were included in a survey on ICT tools for registries and cohorts. Information from 28 tools was available for a preliminary analysis. In comparison to 2015 and 2018, coverage of core processes such as registry development or data analysis and utilization increased from below 40% to 39% and higher. Recording patient-reported information and linkage to other data collections was well covered. However, near-patient trends were less supported. The market offers a rich selection of commercial and non-commercial ICT tools for registry research. Due to the manifold offers available from the market, in-house developed software should be an absolute exception.

Log data, captured during use of mobile health (mHealth) applications by health providers, can play an important role in informing nature of user engagement with the application. The log data can also be employed in understanding health provider work patterns and performance. However, given that these logs are raw data, they require robust cleaning and curation if accurate conclusions are to be derived from analyzing them. This paper describes a systematic data cleaning process for mHealth-derived logs based on Broeck’s framework, which involves iterative screening, diagnosis, and treatment of the log data. For this study, log data from the demonstrative mUzima mHealth application are used. The employed data cleaning process uncovered data inconsistencies, duplicate logs, missing data within logs that required imputation, among other issues. After the data cleaning process, only 39,229 log records out of the initial 91,432 usage logs (42.9%) could be included in the final dataset suitable for analyses of health provider work patterns. This work highlights the significance of having a systematic data cleaning approach for log data to derive useful information on health provider work patterns and performance.

Inflammatory bowel disease (IBD) is a chronic relapsing and remitting illness. The presentation, diagnosis and management IBD are complex, and involve multi-disciplinary care with complex information requirements. The lack of an accurate and comprehensive patient record is often a stumbling block for optimal patient care. Blockchain technology therefore appears to be the perfect solution to improve IBD patient care. Blockchain technology can provide comprehensive and secure data transmission. Many current projects using blockchain for IBD care focus on information delivery. Recently, clinical research has shown that patients have different perceptions of what constitutes high-quality care, compared to healthcare professionals. Patient-centred care in IBD has increasingly taken central stage. Concurrently, blockchain in healthcare has shifted focus to argue for allowing the patient to be in the driver’s seat for information access, facilitated by blockchain-enabled patient-driven interoperability and patient-driven care. This paper dissects the risks and benefits of these two approaches in using blockchain in IBD patient care. This paper then explores the socio-technical and clinical considerations in using blockchain in IBD patient care. Finally, this paper presents four key principles in using blockchain to improve IBD paper care, using collaborative participatory design involving patients, healthcare professionals, and health systems.

This paper aims to explore physicians’ adoption of Machine learning models in the healthcare process and barriers that may hinder it. A review of the literature about ML in healthcare included current and potentially beneficial clinical applications and clinicians’ adoption and trust towards such applications. While some physicians are looking forward to using ML to improve their outcomes and reduce their load, we uncovered fear of unwanted outcomes and concerns about privacy of data, legal liability, and patient dissatisfaction.

Radiology reports often contain follow-up imaging recommendations, but failure to comply with them in a timely manner can lead to delayed treatment, poor patient outcomes, complications, and legal liability. Using a dataset containing 2,972,164 exams for over 7 years, in this study we explored the association between recommendation specificity on follow-up rates. Our results suggest that explicitly mentioning the follow-up interval as part of a follow-up imaging recommendation has a significant impact on adherence making these recommendations 3 times more likely (95% CI: 2.95 – 3.05) to be followed-up, while explicit mentioning of the follow-up modality did not have a significant impact. Our findings can be incorporated into routine dictation macros so that the follow-up duration is explicitly mentioned whenever clinically applicable, and/or used as the basis for a quality improvement project focussed on improving adherence to follow-up imaging recommendations.

For cardiological datasets acquired via different methodologies, ECG signals that are recorded in parallel allow for relatively accurate matching. Some research issues, e.g., the identification of timings of the cardiac cycle in seismocardiography, require higher temporal resolutions. Therefore, we introduce a method derived from a feasibility study to determine deviations and factors influencing the merging of signals simultaneously recorded with different modalities.