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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Health, Well-Being and Sustainability".

Deadline for manuscript submissions: closed (27 March 2023) | Viewed by 15504

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Special Issue Editors

Prof. Dr. Miltiadis D. Lytras

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Guest Editor

1. School of Business, Deree—The American College of Greece, 6 Gravias Street, GR-153 42 Aghia Paraskevi Athens, Greece
2. College of Engineering, Effat University, Jeddah, Saudi Arabia
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management
Special Issues, Collections and Topics in MDPI journals

Dr. Abdulrahman Housawi

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Guest Editor

Planning and Organizational Excellence Administration, Saudi Commission for Health Specialties, Riyadh, Saudi Arabia
Interests: medical education; quality in training; innovation; saudi commission for health specialties; smart healthcare
Special Issues, Collections and Topics in MDPI journals

Dr. Basim Alsaywid

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Guest Editor

1. Planning and Organizational Excellence Administration, Saudi Commission for Health Specialties, Riyadh, Saudi Arabia
2. College of Medicine, King Saud Bin-Abdul-Aziz University for Health Sciences, Jeddah, Saudi Arabia
3. Urology Section, Department of Surgery, King Abdulaziz Medical City, Ministry of National Guard, Jeddah, Saudi Arabia
Interests: medical education; quality in training; innovation; saudi commission for health specialties; smart healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The recent pandemic has created new challenges for the discussion around sustainability in healthcare. The sophisticated management of structured and unstructured knowledge requires new strategies, methodological frameworks, and innovative systems capable of delivering value-based healthcare and bold responses to diverse aspects of inefficiencies in current approaches. Our Special Issue aims to initiate scientific dialogue and welcomes contributions from colleagues representing diverse communities of stakeholders, namely, healthcare specialists, computer scientists, policy makers, innovators, and government officers. In the focus of our discussion, we consider the following six themes of critical importance:

Structured and unstructured knowledge management in the healthcare domain
Knowledge management strategies
Knowledge repositories, integrated data warehouses, and data lakes in healthcare
Knowledge networks and sophisticated profiling
Value added knowledge-intensive services for innovation
Research as a catalyst for knowledge creation and utilization

Our Special Issue focuses on communicating to diverse audiences the quest and vision of application of knowledge management in healthcare. Topics in our agenda include but are not limited to the following:

Management of structured and unstructured data in healthcare
Knowledge networks
Data warehouses and data mining in healthcare
Social networks and adoption in healthcare
Knowledge-based performance
Knowledge sharing and knowledge utilization in healthcare
Patient-centric health
Management of medical records
Security and privacy issues in health information systems
Decision making and enhancement
Workflow management
Analytics and dashboards
Interoperability issues
Knowledge creation
Innovation and research in healthcare
Artificial intelligence
Case studies, use cases
R&D projects
Next-generation knowledge management for personalized medicine

Prof. Dr. Miltiadis D. Lytras
Dr. Abdulrahman Housawi
Dr. Basim Alsaywid
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. Sustainability 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 2400 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

knowledge management
health care
knowledge networks
communities of expertise in healthcare
Q&A platforms
healthcare analytics, unstructured knowledge
tacit knowledge
research excellence

Published Papers (8 papers)

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Research

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24 pages, 3378 KiB

Open AccessArticle

The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directions

by Lily Popova Zhuhadar and Miltiadis D. Lytras

Sustainability 2023, 15(18), 13484; https://0-doi-org.brum.beds.ac.uk/10.3390/su151813484 - 08 Sep 2023

Cited by 2 | Viewed by 2682

Abstract

Artificial Intelligence (AI) has experienced rapid advancements in recent years, facilitating the creation of innovative, sustainable tools and technologies across various sectors. Among these applications, the use of AI in healthcare, particularly in the diagnosis and management of chronic diseases like diabetes, has shown significant promise. Automated Machine Learning (AutoML), with its minimally invasive and resource-efficient approach, promotes sustainability in healthcare by streamlining the process of predictive model creation. This research paper delves into advancements in AutoML for predictive modeling in diabetes diagnosis. It illuminates their effectiveness in identifying risk factors, optimizing treatment strategies, and ultimately improving patient outcomes while reducing environmental footprint and conserving resources. The primary objective of this scholarly inquiry is to meticulously identify the multitude of factors contributing to the development of diabetes and refine the prediction model to incorporate these insights. This process fosters a comprehensive understanding of the disease in a manner that supports the principles of sustainable healthcare. By analyzing the provided dataset, AutoML was able to select the most fitting model, emphasizing the paramount importance of variables such as Glucose, BMI, DiabetesPedigreeFunction, and BloodPressure in determining an individual’s diabetic status. The sustainability of this process lies in its potential to expedite treatment, reduce unnecessary testing and procedures, and ultimately foster healthier lives. Recognizing the importance of accuracy in this critical domain, we propose that supplementary factors and data be rigorously evaluated and incorporated into the assessment. This approach aims to devise a model with enhanced accuracy, further contributing to the efficiency and sustainability of healthcare practices. Full article

(This article belongs to the Special Issue Knowledge Management in Healthcare)

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13 pages, 2335 KiB

Open AccessArticle

KMS as a Sustainability Strategy during a Pandemic

by George Maramba, Hanlie Smuts, Funmi Adebesin, Marie Hattingh and Tendani Mawela

Sustainability 2023, 15(12), 9158; https://0-doi-org.brum.beds.ac.uk/10.3390/su15129158 - 06 Jun 2023

Cited by 1 | Viewed by 1046

Abstract

The 21st century world never anticipated a scenario in which it would be thrown into disarray by a fast-spreading viral disease, during which governments hastily had to enforce curfews by imposing travel and social gathering restrictions in order to contain it. The coronavirus disease of 2019 disrupted global supply chains and economies and caused death in every part of the world. Health departments and hospitals became the centres of attention as healthcare workers battled to save the lives of the infected. Governments struggled to calm citizens as the spread of incorrect and, sometimes, malicious information dominated all social media channels. The absence of established knowledge-sharing strategies and channels, knowledge about the disease or how to deal with the pandemic exacerbated the situation. This study investigates knowledge management systems as a sustainability strategy during a pandemic from three perspectives: understanding the disease, sourcing the required drugs and communicating with the citizens during a pandemic. The researchers adopted a survey research strategy for the study. The study makes an essential contribution to the value of KMS and the need to adopt them in the healthcare sector, particularly when faced with pandemics such as COVID-19. Full article

(This article belongs to the Special Issue Knowledge Management in Healthcare)

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18 pages, 4250 KiB

Open AccessArticle

Novel Features and Neighborhood Complexity Measures for Multiclass Classification of Hybrid Data

by Francisco J. Camacho-Urriolagoitia, Yenny Villuendas-Rey, Cornelio Yáñez-Márquez and Miltiadis Lytras

Sustainability 2023, 15(3), 1995; https://0-doi-org.brum.beds.ac.uk/10.3390/su15031995 - 20 Jan 2023

Cited by 1 | Viewed by 1088

Abstract

The present capabilities for collecting and storing all kinds of data exceed the collective ability to analyze, summarize, and extract knowledge from this data. Knowledge management aims to automatically organize a systematic process of learning. Most meta-learning strategies are based on determining data characteristics, usually by computing data complexity measures. Such measures describe data characteristics related to size, shape, density, and other factors. However, most of the data complexity measures in the literature assume the classification problem is binary (just two decision classes), and that the data is numeric and has no missing values. The main contribution of this paper is that we extend four data complexity measures to overcome these drawbacks for characterizing multiclass, hybrid, and incomplete supervised data. We change the formulation of Feature-based measures by maintaining the essence of the original measures, and we use a maximum similarity graph-based approach for designing Neighborhood measures. We also use ordering weighting average operators to avoid biases in the proposed measures. We included the proposed measures in the EPIC software for computational availability, and we computed the measures for publicly available multiclass hybrid and incomplete datasets. In addition, the performance of the proposed measures was analyzed, and we can confirm that they solve some of the biases of previous ones and are capable of natively handling mixed, incomplete, and multiclass data without any preprocessing needed. Full article

(This article belongs to the Special Issue Knowledge Management in Healthcare)

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16 pages, 1914 KiB

Open AccessArticle

Healthcare Recommender System Based on Medical Specialties, Patient Profiles, and Geospatial Information

by Miguel Torres-Ruiz, Rolando Quintero, Giovanni Guzman and Kwok Tai Chui

Sustainability 2023, 15(1), 499; https://0-doi-org.brum.beds.ac.uk/10.3390/su15010499 - 28 Dec 2022

Cited by 3 | Viewed by 1985

Abstract

The global outburst of COVID-19 introduced severe issues concerning the capacity and adoption of healthcare systems and how vulnerable citizen classes might be affected. The pandemic generated the most remarkable transformation of health services, appropriating the increase in new information and communication technologies to bring sustainability to health services. This paper proposes a novel, methodological, and collaborative approach based on patient-centered technology, which consists of a recommender system architecture to assist the health service level according to medical specialties. The system provides recommendations according to the user profile of the citizens and a ranked list of medical facilities. Thus, we propose a health attention factor to semantically compute the similarity between medical specialties and offer medical centers with response capacity, health service type, and close user geographic location. Thus, considering the challenges described in the state-of-the-art, this approach tackles issues related to recommenders in mobile devices and the diversity of items in the healthcare domain, incorporating semantic and geospatial processing. The recommender system was tested in diverse districts of Mexico City, and the spatial visualization of the medical facilities filtering by the recommendations is displayed in a Web-GIS application. Full article

(This article belongs to the Special Issue Knowledge Management in Healthcare)

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16 pages, 1122 KiB

Open AccessArticle

Customized Instance Random Undersampling to Increase Knowledge Management for Multiclass Imbalanced Data Classification

by Claudia C. Tusell-Rey, Oscar Camacho-Nieto, Cornelio Yáñez-Márquez and Yenny Villuendas-Rey

Sustainability 2022, 14(21), 14398; https://0-doi-org.brum.beds.ac.uk/10.3390/su142114398 - 03 Nov 2022

Cited by 1 | Viewed by 1109

Abstract

Imbalanced data constitutes a challenge for knowledge management. This problem is even more complex in the presence of hybrid (numeric and categorical data) having missing values and multiple decision classes. Unfortunately, health-related information is often multiclass, hybrid, and imbalanced. This paper introduces a novel undersampling procedure that deals with multiclass hybrid data. We explore its impact on the performance of the recently proposed customized naïve associative classifier (CNAC). The experiments made, and the statistical analysis, show that the proposed method surpasses existing classifiers, with the advantage of being able to deal with multiclass, hybrid, and incomplete data with a low computational cost. In addition, our experiments showed that the CNAC benefits from data sampling; therefore, we recommend using the proposed undersampling procedure to balance data for CNAC. Full article

(This article belongs to the Special Issue Knowledge Management in Healthcare)

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14 pages, 1496 KiB

Open AccessArticle

Public Health System and Socio-Economic Development Coupling Based on Systematic Theory: Evidence from China

by Jian Zhou, Chuhan Wang, Xinyu Zhang and Shuang Wang

Sustainability 2022, 14(19), 12757; https://0-doi-org.brum.beds.ac.uk/10.3390/su141912757 - 07 Oct 2022

Cited by 1 | Viewed by 1519

Abstract

This paper focus on the quantitative measurement of public health systems and its mismatch with socio-economic development. Based on systematic theory, we divide the public health system into four sub-systems: resource inputs, planning in decision-making, operations, and service outputs. We also provide a method to analyse the ability to match between the public health system and social-economic development by using the grey correlation and coupling method. Then we introduce data from China as a case of empirical research. The main findings are as follows: (1) China’s public health system has progressed from 2012 to 2019, and the development of China’s public health system is typically “input-driven”. Second, the level of public health management in China lacks sustainability. (2) The main reason for this problem is the mismatch between the central and local governments in China in terms of public health management authority. (3) Third, the coupling between China’s public health system and socio-economics development has shown a decreasing trend, which indicates an increasingly significant mismatch problem between public health and economic growth, urbanization, and population aging. Our study will enrich the understanding of the relationship between the public health system and socio-economics development. Full article

(This article belongs to the Special Issue Knowledge Management in Healthcare)

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14 pages, 822 KiB

Open AccessArticle

Knowledge, Attitude and Practice towards the Use of Over-the-Counter Medicines: An Online Survey among Bruneian Adults amid the COVID-19 Pandemic

by Faiqah Batrisyia Syaza Bahrin Dzulkharnain, Naeem Shafqat, Andi Hermansyah, Ching Siang Tan, David Koh, Khang Wen Goh and Long Chiau Ming

Sustainability 2022, 14(15), 9033; https://0-doi-org.brum.beds.ac.uk/10.3390/su14159033 - 23 Jul 2022

Cited by 2 | Viewed by 2885

Abstract

Globally, self-medication has increased, where 25% of adults use OTC medicines. This research is intended to assess the knowledge, attitude and practice regarding OTC medicines among adults in Brunei Darussalam. An online cross-sectional survey was performed using a questionnaire adapted from similar research conducted among students in Brunei Darussalam. A total of 364 responses were collected, where the median age of the study participants was 23 years. The mean knowledge score was 7.3 out of 9, with most respondents (77.7%) having good knowledge of OTC medicines. Almost all (92.9%) showed a positive attitude towards OTC use. A statistically significant difference (p ≤ 0.05) was observed in attitude scores between age groups and education levels. Most of the study participants (88.2%) have practiced self-medication with OTC medicines, mainly due to their easy accessibility (79.4%). A small number practiced improper habits, such as consuming more than the recommended dose (6.0%) and not checking the expiry date (0.5%). The practice of self-medicating with OTC medicines can be advantageous when patients fully know the medications and nature of their disease. Knowledge of proper OTC medicine use among adults in Brunei Darussalam is essential to avoid improper user practices and potential health hazards associated with the misuse of medications. Full article

(This article belongs to the Special Issue Knowledge Management in Healthcare)

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Review

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39 pages, 957 KiB

Open AccessReview

Current Trends in Interprofessional Shared Decision-Making Programmes in Health Professions Education: A Scoping Review

by Lama Sultan, Basim Alsaywid, Nynke De Jong and Jascha De Nooijer

Sustainability 2022, 14(20), 13157; https://0-doi-org.brum.beds.ac.uk/10.3390/su142013157 - 13 Oct 2022

Cited by 1 | Viewed by 2170

Abstract

Background: Shared decision-making (SDM) is considered a patient-centred approach that requires interprofessional collaboration among healthcare professionals. Teaching interprofessional shared decision-making (IP-SDM) to students preparing for clinical practice facilitates the accomplishment of collaboration. Objective: This review seeks to provide an overview of current IP-SDM educational interventions with respect to their theoretical frameworks, delivery, and outcomes in healthcare. Methods: A scoping review was undertaken using PRISMA. Electronic databases, including OVID-MEDLINE, PubMed, OVID- EMBASE, ERIC, EBSCO-CINAHL, Cochrane Trails, APA PsycINFO, NTLTD, and MedNar, were searched for articles published between 2000 and 2020 on IP-SDM education and evaluation. Grey literature was searched for additional articles. Quality assessment and data extraction were independently completed by two reviewers, piloted on a random sample of specific articles, and revised iteratively. Results: A total of 63 articles met the inclusion criteria. The topics included various SDM models (26 articles) and educational frameworks and learning theories (20 articles). However, more than half of the studies did not report a theoretical framework. Students involved in the studies were postgraduates (22 articles) or undergraduates (18 articles), and 11 articles included both. The teaching incorporated active educational methods, including evaluation frameworks (18 articles) and Kirkpatrick’s model (6 articles). The mean educational intervention duration was approximately 4 months. Most articles did not include summative or formative assessments. The outcomes assessed most often included collaboration and communication, clinical practice and outcome, patients’ preferences, and decision-making skills. Conclusions: Overall, these articles demonstrate interest in teaching IP-SDM knowledge, skills, and attitudes in health professions education. However, the identified educational interventions were heterogeneous in health professionals’ involvement, intervention duration, educational frameworks, SDM models, and evaluation frameworks. Practice implications: We need more homogeneity in both theoretical frameworks and validated measures to assess IP-SDM. Full article

(This article belongs to the Special Issue Knowledge Management in Healthcare)

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