Special Issue "Advancements in Quality of Patient Care and Health Services Through Health Informatics Innovations"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Care Sciences & Services".

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Dr. Samina Abidi
E-Mail Website
Guest Editor
Dalhousie University, Halifax, Canada
Interests: healthcare knowledge modeling and computerization; digital chronic disease management; m-health
Dr. William Van Woensel
E-Mail Website
Guest Editor
Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
Interests: knowledge representation and reasoning; machine learning; activity recognition; m-health

Special Issue Information

Dear Colleagues,

The latest innovations in health informatics hold great promise of efficiency, cost reduction, and improved health care for both communicable (CDs) and non-communicable diseases (NCDs). Common NCDs such as diabetes, heart disease, cancer, COPD, arthritis, and neuropsychiatric disorders are the leading cause of disease burden and mortality, and their management has been prioritized globally. Prevention and management of NCDs require joint efforts from health care providers and patients. Health Informatics technologies can be leveraged to provide efficient platforms to deliver multidisciplinary patient management programs that are able to promote shared decision making, patient education, and behaviour modification.

Management of communicable diseases (CDs) includes disease surveillance, prevention, and preparedness and requires information from various sources that must be timely, accurate, and visualized to draw actionable insights. The recent COVID-19 pandemic has shown the necessity of healthcare institutions to rapidly adapt to the changing public health landscape. Central to the pandemic response are the emerging research-based health informatics initiatives designed to draw on information and communication technologies to improve the safety, quality, and efficiency of healthcare.

The aim of this Special Issue is to present the latest innovations in health informatics methods and technologies to prevent, track, and manage chronic and communicable diseases. We welcome original contributions in the form of theoretical, engineering-related, and empirical research, review articles, policy articles, and case reports. 

The topics include but are not limited to:

  • Clinical information systems and infrastructure for inter-institution data sharing;
  • Health data visualizations and knowledge translation;
  • Image-based diagnosis, severity assessment, and management using automated image analysis and artificial intelligence (AI) methods and tools;
  • Application of machine learning tools to in-practice data for diagnosis and management, in particular drug repurposing and pharmacovigilance;
  • Knowledge-driven methods toward clinical decision support;
  • Utility of telemedicine and virtual visits to manage illness;
  • Design and implementation of patient registries and databases;
  • Digital mental health services (Mobile or Web-based);
  • At home monitoring and symptom screening tools to aid individuals manage their condition
  • Theory-based mobile apps, portals, and web sites to provide tailored health education to individuals and help them to achieve behaviour modification;
  • Impact of social networks on the COVID-19 pandemic, especially misinformation and views around masks, social distancing, vaccination, and management;
  • Evidence synthesis related to health informatics technologies.

Dr. Samina Abidi
Dr. William Van Woensel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Clinical information system
  • Artificial intelligence
  • Telemedicine
  • Image analysis
  • Mobile apps
  • Patient education
  • Social networks
  • Digital mental health
  • Data visualization

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Article
A Health eLearning Ontology and Procedural Reasoning Approach for Developing Personalized Courses to Teach Patients about Their Medical Condition and Treatment
Int. J. Environ. Res. Public Health 2021, 18(14), 7355; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18147355 - 09 Jul 2021
Viewed by 414
Abstract
We propose a methodological framework to support the development of personalized courses that improve patients’ understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create [...] Read more.
We propose a methodological framework to support the development of personalized courses that improve patients’ understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create a course. We combine the ontology with a procedural reasoning approach and precompiled plans to operationalize a design across disease conditions. The resulting courses generated by the framework are personalized across four patient axes—condition and treatment, comprehension level, learning style based on the VARK (Visual, Aural, Read/write, Kinesthetic) presentation model, and the level of understanding of specific course content according to Bloom’s taxonomy. Customizing educational materials along these learning axes stimulates and sustains patients’ attention when learning about their conditions or treatment options. Our proposed framework creates a personalized course that prepares patients for their meetings with specialists and educates them about their prescribed treatment. We posit that the improvement in patients’ understanding of prescribed care will result in better outcomes and we validate that the constructs of our framework are appropriate for representing content and deriving personalized courses for two use cases: anticoagulation treatment of an atrial fibrillation patient and lower back pain management to treat a lumbar degenerative disc condition. We conduct a mostly qualitative study supported by a quantitative questionnaire to investigate the acceptability of the framework among the target patient population and medical practitioners. Full article
Show Figures

Figure 1

Article
Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data
Int. J. Environ. Res. Public Health 2021, 18(11), 5993; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18115993 - 03 Jun 2021
Viewed by 655
Abstract
Background: COVID-19 preventive measures have been an obstacle to millions of people around the world, influencing not only their normal day-to-day activities but also affecting their mental health. Social distancing is one such preventive measure. People express their opinions freely through social media [...] Read more.
Background: COVID-19 preventive measures have been an obstacle to millions of people around the world, influencing not only their normal day-to-day activities but also affecting their mental health. Social distancing is one such preventive measure. People express their opinions freely through social media platforms like Twitter, which can be shared among other users. The articulated texts from Twitter can be analyzed to find the sentiments of the public concerning social distancing. Objective: To understand and analyze public sentiments towards social distancing as articulated in Twitter textual data. Methods: Twitter data specific to Canada and texts comprising social distancing keywords were extrapolated, followed by utilizing the SentiStrength tool to extricate sentiment polarity of tweet texts. Thereafter, the support vector machine (SVM) algorithm was employed for sentiment classification. Evaluation of performance was measured with a confusion matrix, precision, recall, and F1 measure. Results: This study resulted in the extraction of a total of 629 tweet texts, of which, 40% of tweets exhibited neutral sentiments, followed by 35% of tweets showed negative sentiments and only 25% of tweets expressed positive sentiments towards social distancing. The SVM algorithm was applied by dissecting the dataset into 80% training and 20% testing data. Performance evaluation resulted in an accuracy of 71%. Upon using tweet texts with only positive and negative sentiment polarity, the accuracy increased to 81%. It was observed that reducing test data by 10% increased the accuracy to 87%. Conclusion: Results showed that an increase in training data increased the performance of the algorithm. Full article
Show Figures

Figure 1

Article
The Perception of Patient Safety Strategies by Primary Health Professionals
Int. J. Environ. Res. Public Health 2021, 18(3), 1063; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18031063 - 25 Jan 2021
Viewed by 854
Abstract
Almost all European citizens rank patient safety as very or fairly important in their country. However, few patient safety initiatives have been undertaken or implemented in Poland. The aim was to identify patient safety strategies perceived as important in Poland and compare them [...] Read more.
Almost all European citizens rank patient safety as very or fairly important in their country. However, few patient safety initiatives have been undertaken or implemented in Poland. The aim was to identify patient safety strategies perceived as important in Poland and compare them with those identified in an earlier Dutch study. A web-based survey was conducted among primary healthcare providers in Poland. The findings were compared with those obtained from eight other countries. The strategies regarded as most important in Poland included the use of integrated medical records for communication with specialists and others, patient-held medical records, acceptable workload in general practice, and availability of information technology. However, despite being seen as important, these strategies have not been widely implemented in Poland. This is the first study to identify strategies considered by primary care physicians in Poland to be important for improving patient safety. These strategies differed significantly from those indicated in other countries. Full article

Review

Jump to: Research

Review
Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review
Int. J. Environ. Res. Public Health 2021, 18(9), 4499; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18094499 - 23 Apr 2021
Viewed by 865
Abstract
Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic [...] Read more.
Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic. Full article
Show Figures

Figure 1

Back to TopTop