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Healthcare
Special Issues
Emerging Technologies in Health Informatics and Management
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Emerging Technologies in Health Informatics and Management

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".

Deadline for manuscript submissions: closed (22 October 2021) | Viewed by 21823

Special Issue Editors

Dr. Mahmoud Elkhodr

E-Mail Website
Guest Editor
School of Engineering and Technology, Central Queensland University, Sydney, NSW 2000, Australia
Interests: information systems; artificial intelligence and image processing; computer software; communications technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Omar Darwish

E-Mail Website
Guest Editor
Information Security and Applied Computing Department, Eastern Michigan University, Ypsilanti, MI, USA
Interests: cybersecurity; IoT; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Belal Alsinglawi

E-Mail Website
Guest Editor
School of Computer Data and Mathematical Sciences, Western Sydney University, Rydalmere, NSW 2116, Australia
Interests: computer, data & mathematical sciences
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Health informatics is an evolving interdisciplinary domain that comprises the use of emerging ICT technologies to support and improve healthcare and health.

The convergence of assistive technologies, artificial intelligence, machine learning, cloud computing and IoT technologies, digitization of health processes and systems, 3D bioprinting, nanomedicine, and robotics is about to occur. The year of the coronavirus disease 2019 (COVID-19) pandemic (2020) witnessed the rise of virtual healthcare. Virtual healthcare technologies, also known as telehealth or telemedicine, have helped provide access to care at a time when cities and even countries were in lockdown.

Apart from the telehealth trends, the healthcare sector is rapidly adopting IoT and cloud computing technologies as well, creating what is known as the Internet of Medical Things (IoMT). In IoMT, IoT systems and devices such as wearables devices, smart homes, personalized healthcare systems—with data analytics capabilities and including machine learning algorithms—are poised to offer healthcare providers access to a range of information that can be used for monitoring patients, continuous analysis, remote configuration of systems, and importantly, early diagnosis and therapeutic interventions.

This Special Issue seeks commentaries, original research articles, short reports, and reviews on all emerging technologies in healthcare. This Special Issue aims to report on recent and emerging trends in health and medical informatics. It is now more important than ever to bring to light ICT solutions that can improve and automate healthcare services, minimize costs, simplify the management and deployment of healthcare services, preserve patients’ privacy, and improve the security of data collection, processing, storage as well as the that of data analytics.

The “Emerging Technologies in Health Informatics and Management” Special Issue is jointly organized between “Healthcare” and “International Journal of Environmental Research and Public Health” journals. You may choose our Joint Special Issue in International Journal of Environmental Research and Public Health.

We look forward to your contribution.

Dr. Mahmoud Elkhodr
Dr. Omar Darwish
Dr. Belal Alsinglawi
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 submissions that pass pre-check are 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. Healthcare 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 2700 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

  • Health Informatics
  • AI and ML applications in health
  • Health innovations
  • emerging technologies in ICT health
  • IoT healthcare, internet of medical things (IoMT)
  • Persuasive Technologies in Healthcare
  • HCI in Healthcare
  • Ethical issues in Healthcare applications
  • Telehealth
  • Security and Privacy of healthcare systems
  • Ethical issues in healthcare systems

Related Special Issue

Published Papers (6 papers)

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Research

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14 pages, 1520 KiB  
Article
DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups
by Md. Mohaimenul Islam, Guo-Hung Li, Tahmina Nasrin Poly and Yu-Chuan (Jack) Li
Healthcare 2021, 9(12), 1632; https://doi.org/10.3390/healthcare9121632 - 25 Nov 2021
Cited by 6 | Viewed by 4031
Abstract
Nowadays, the use of diagnosis-related groups (DRGs) has been increased to claim reimbursement for inpatient care. The overall benefits of using DRGs depend upon the accuracy of clinical coding to obtain reasonable reimbursement. However, the selection of appropriate codes is always challenging and [...] Read more.
Nowadays, the use of diagnosis-related groups (DRGs) has been increased to claim reimbursement for inpatient care. The overall benefits of using DRGs depend upon the accuracy of clinical coding to obtain reasonable reimbursement. However, the selection of appropriate codes is always challenging and requires professional expertise. The rate of incorrect DRGs is always high due to the heavy workload, poor quality of documentation, and lack of computer assistance. We therefore developed deep learning (DL) models to predict the primary diagnosis for appropriate reimbursement and improving hospital performance. A dataset consisting of 81,486 patients with 128,105 episodes was used for model training and testing. Patients’ age, sex, drugs, diseases, laboratory tests, procedures, and operation history were used as inputs to our multiclass prediction model. Gated recurrent unit (GRU) and artificial neural network (ANN) models were developed to predict 200 primary diagnoses. The performance of the DL models was measured by the area under the receiver operating curve, precision, recall, and F1 score. Of the two DL models, the GRU method, had the best performance in predicting the primary diagnosis (AUC: 0.99, precision: 83.2%, and recall: 66.0%). However, the performance of ANN model for DRGs prediction achieved AUC of 0.99 with a precision of 0.82 and recall of 0.57. The findings of our study show that DL algorithms, especially GRU, can be used to develop DRGs prediction models for identifying primary diagnosis accurately. DeepDRGs would help to claim appropriate financial incentives, enable proper utilization of medical resources, and improve hospital performance. Full article
(This article belongs to the Special Issue Emerging Technologies in Health Informatics and Management)
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14 pages, 2084 KiB  
Article
Morton Filter-Based Security Mechanism for Healthcare System in Cloud Computing
by Sugandh Bhatia and Jyoteesh Malhotra
Healthcare 2021, 9(11), 1551; https://doi.org/10.3390/healthcare9111551 - 15 Nov 2021
Cited by 4 | Viewed by 2788
Abstract
Electronic health records contain the patient’s sensitive information. If these data are acquired by a malicious user, it will not only cause the pilferage of the patient’s personal data but also affect the diagnosis and treatment. One of the most challenging tasks in [...] Read more.
Electronic health records contain the patient’s sensitive information. If these data are acquired by a malicious user, it will not only cause the pilferage of the patient’s personal data but also affect the diagnosis and treatment. One of the most challenging tasks in cloud-based healthcare systems is to provide security and privacy to electronic health records. Various probabilistic data structures and watermarking techniques were used in the cloud-based healthcare systems to secure patient’s data. Most of the existing studies focus on cuckoo and bloom filters, without considering their throughputs. In this research, a novel cloud security mechanism is introduced, which supersedes the shortcomings of existing approaches. The proposed solution enhances security with methods such as fragile watermark, least significant bit replacement watermarking, class reliability factor, and Morton filters included in the formation of the security mechanism. A Morton filter is an approximate set membership data structure (ASMDS) that proves many improvements to other data structures, such as cuckoo, bloom, semi-sorting cuckoo, and rank and select quotient filters. The Morton filter improves security; it supports insertions, deletions, and lookups operations and improves their respective throughputs by 0.9× to 15.5×, 1.3× to 1.6×, and 1.3× to 2.5×, when compared to cuckoo filters. We used Hadoop version 0.20.3, and the platform was Red Hat Enterprise Linux 6; we executed five experiments, and the average of the results has been taken. The results of the simulation work show that our proposed security mechanism provides an effective solution for secure data storage in cloud-based healthcare systems, with a load factor of 0.9. Furthermore, to aid cloud security in healthcare systems, we presented the motivation, objectives, related works, major research gaps, and materials and methods; we, thus, presented and implemented a cloud security mechanism, in the form of an algorithm and a set of results and conclusions. Full article
(This article belongs to the Special Issue Emerging Technologies in Health Informatics and Management)
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21 pages, 4753 KiB  
Article
The Expansion Mechanism of the Cooperative Networks of Supply Support Organizations in a Public Health Emergency
by Chenxi Lian, Jian Wang and Jida Liu
Healthcare 2021, 9(8), 1041; https://doi.org/10.3390/healthcare9081041 - 13 Aug 2021
Cited by 5 | Viewed by 1802
Abstract
The outbreak of COVID-19 has significantly restricted the productive capacity of society and resulted in a shortage of supplies to maintain survival. Lightening the burden not only depends on government agencies, but also needs extensive social organization participation. However, few studies focus on [...] Read more.
The outbreak of COVID-19 has significantly restricted the productive capacity of society and resulted in a shortage of supplies to maintain survival. Lightening the burden not only depends on government agencies, but also needs extensive social organization participation. However, few studies focus on how to promote social cooperation to support the provision of emergency supplies. This study aimed to find out the theoretical mechanism to expand the cooperative networks of supply support organizations during the epidemic. Data from the emergency response to the COVID-19 pandemic in China were used. Three cooperative networks from a progressive perspective were constructed based on the cooperative relationships among organizations. The expansion mechanism was verified by the exponential random graph model. The results show that when the institutional network expands into an interactive network, the composition of organization types has changed, but the cooperative network’s efficiency does not improve much. The matching effect of the organizational type and the Matthew effect of nodes are both effective paths to promote cooperative network expansion, however, the structure effect shows that complex relationship structure is not a critical factor. Our findings highlight the importance of core organizations and the function of different types of organizations in building cooperative network as well as providing theoretical frameworks for policymakers to use in guiding and motivating social cooperation in emergency supplies. Full article
(This article belongs to the Special Issue Emerging Technologies in Health Informatics and Management)
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12 pages, 2341 KiB  
Article
An Efficient Agent Based Data Management Method of NoSQL Environments for Health Care Applications
by Theodore Kotsilieris
Healthcare 2021, 9(3), 322; https://doi.org/10.3390/healthcare9030322 - 13 Mar 2021
Cited by 2 | Viewed by 1692
Abstract
Background: As medical knowledge is continuously expanding and diversely located, Health Information Technology (HIT) applications are proposed as a good prospect for improving not only the efficiency and the effectiveness but also the quality of healthcare services delivery. The technologies expected to shape [...] Read more.
Background: As medical knowledge is continuously expanding and diversely located, Health Information Technology (HIT) applications are proposed as a good prospect for improving not only the efficiency and the effectiveness but also the quality of healthcare services delivery. The technologies expected to shape such innovative HIT architectures include: Mobile agents (Mas) and NoSQL technologies. Mobile agents provide an inherent way of tackling distributed problems of accessing heterogeneous and spatially diverse data sources. NoSQL technology gains ground for the development of scalable applications with non-static and open data schema from complex and diverse sources. Methods and Design: This paper conducts a twofold study: It attempts a literature review of the applications based on the mobile agent (MA) and NoSQL technologies for healthcare support services. Subsequently, a pilot system evaluates the NoSQL technology against the relational one within a distributed environment based on mobile agents for information retrieval. Its objective is to study the feasibility of developing systems that will employ ontological data representation and task implementation through mobile agents towards flexible and transparent health data monitoring. Results and Discussion: The articles studied focus on applying mobile agents for patient support and healthcare services provision thus as to make a positive contribution to the treatment of chronic diseases. In addition, attention is put on the design of platform neutral techniques for clinical data gathering and dissemination over NoSQL. The experimental environment was based on the Apache Jena Fuseki NoSQL server and the JAVA Agent DEvelopment Framework -JADE agent platform. The results reveal that the NoSQL implementation outperforms the standard relational one. Full article
(This article belongs to the Special Issue Emerging Technologies in Health Informatics and Management)
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Review

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19 pages, 1515 KiB  
Review
Application of Smartphone Technologies in Disease Monitoring: A Systematic Review
by Jeban Chandir Moses, Sasan Adibi, Sheikh Mohammed Shariful Islam, Nilmini Wickramasinghe and Lemai Nguyen
Healthcare 2021, 9(7), 889; https://doi.org/10.3390/healthcare9070889 - 14 Jul 2021
Cited by 35 | Viewed by 7176
Abstract
Technologies play an essential role in monitoring, managing, and self-management of chronic diseases. Since chronic patients rely on life-long healthcare systems and the current COVID-19 pandemic has placed limits on hospital care, there is a need to explore disease monitoring and management technologies [...] Read more.
Technologies play an essential role in monitoring, managing, and self-management of chronic diseases. Since chronic patients rely on life-long healthcare systems and the current COVID-19 pandemic has placed limits on hospital care, there is a need to explore disease monitoring and management technologies and examine their acceptance by chronic patients. We systematically examined the use of smartphone applications (apps) in chronic disease monitoring and management in databases, namely, Medline, Web of Science, Embase, and Proquest, published from 2010 to 2020. Results showed that app-based weight management programs had a significant effect on healthy eating and physical activity (p = 0.002), eating behaviours (p < 0.001) and dietary intake pattern (p < 0.001), decreased mean body weight (p = 0.008), mean Body Mass Index (BMI) (p = 0.002) and mean waist circumference (p < 0.001). App intervention assisted in decreasing the stress levels (paired t-test = 3.18; p < 0.05). Among cancer patients, we observed a high acceptance of technology (76%) and a moderately positive correlation between non-invasive electronic monitoring data and questionnaire (r = 0.6, p < 0.0001). We found a significant relationship between app use and standard clinical evaluation and high acceptance of the use of apps to monitor the disease. Our findings provide insights into critical issues, including technology acceptance along with regulatory guidelines to be considered when designing, developing, and deploying smartphone solutions targeted for chronic patients. Full article
(This article belongs to the Special Issue Emerging Technologies in Health Informatics and Management)
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12 pages, 581 KiB  
Review
An Examination of COVID-19 Medications’ Effectiveness in Managing and Treating COVID-19 Patients: A Comparative Review
by Mahmoud Al-Masaeed, Mohammad Alghawanmeh, Ashraf Al-Singlawi, Rawan Alsababha and Muhammad Alqudah
Healthcare 2021, 9(5), 557; https://doi.org/10.3390/healthcare9050557 - 10 May 2021
Cited by 1 | Viewed by 2373
Abstract
Background: The review seeks to shed light on the administered and recommended COVID-19 treatment medications through an evaluation of their efficacy. Methods: Data were collected from key databases, including Scopus, Medline, Google Scholar, and CINAHL. Other platforms included WHO and FDA [...] Read more.
Background: The review seeks to shed light on the administered and recommended COVID-19 treatment medications through an evaluation of their efficacy. Methods: Data were collected from key databases, including Scopus, Medline, Google Scholar, and CINAHL. Other platforms included WHO and FDA publications. The review’s literature search was guided by the WHO solidarity clinical trials for COVID-19 scope and trial-assessment parameters. Results: The findings indicate that the use of antiretroviral drugs as an early treatment for COVID-19 patients has been useful. It has reduced hospital time, hastened the clinical cure period, delayed and reduced the need for mechanical and invasive ventilation, and reduced mortality rates. The use of vitamins, minerals, and supplements has been linked to increased immunity and thus offering the body a fighting chance. Nevertheless, antibiotics do not correlate with improving patients’ wellbeing and are highly discouraged from the developed clinical trials. Conclusions: The review demonstrates the need for additional clinical trials with a randomized, extensive sample base and over a more extended period to examine the potential side effects of the medications administered. Critically, the findings underscore the need for vaccination as the only viable medication to limit the SARS-CoV-2 virus spread. Full article
(This article belongs to the Special Issue Emerging Technologies in Health Informatics and Management)
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