Special Issue "Network Analytics in Healthcare Decision Making"

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

Deadline for manuscript submissions: closed (30 November 2019).

Special Issue Editor

Dr. Shahadat Uddin
E-Mail Website
Guest Editor
Faculty of Engineering and IT, The University of Sydney, Sydney NSW 2006, Australia
Interests: health informatics; disease network; data science and complex network

Special Issue Information

Dear Colleagues,

We would like to invite papers to this Special Issue of the International Journal of Environmental Research and Public Health, which will explore the application of different measures, methods and models of network analytics to the decision-making process of healthcare systems.
The principal goal of a well-structured healthcare system is to provide the best possible care to its consumers. Various healthcare stakeholders including general practitioners, specialists, hospitals, radiology and image providers, pathology service providers, pharmacies, aged care facilities and funders work together to keep the whole healthcare system running smoothly. These healthcare entities generate a large amount of data, which become valuable resources that help in making evidence-based decisions and understanding how the whole system is performing. Since these entities and the data they generate are inherently connected, network analytics has a significant potential to offer insights into the hidden relationships across these data elements, which can eventually facilitate the process of healthcare decision-making procedure. Due to their strong potential in revealing hidden insights of any networks, the measures (e.g., network centrality, centralization and density), methods (e.g., community detection, sub-group and core-periphery analysis) and models (e.g., exponential random graph and structural equivalence) of network analytics have gained wide acceptability in healthcare research in recent years.
In this Special Issue, we welcome the submission of methodological, empirical and review papers that use methods, measures and models of data analytics and have a clear implication for healthcare decision-making. The submitted papers can be based on primary (e.g., based on study design) and/or secondary research data (e.g., administrative claim data and electronic medical records).
Papers of a high academic standard addressing any healthcare decision-making issues using network analytics are invited for submission to this Special Issue.

Dr. Shahadat Uddin
Guest Editor

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 2000 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

  • Network analysis, visualization and comparison
  • Healthcare collaboration and coordination
  • Professional network in healthcare
  • Disease comorbidity and disease network
  • Health and healthcare trajectory
  • Healthcare policy

Published Papers (6 papers)

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Research

Open AccessArticle
A Framework to Understand the Progression of Cardiovascular Disease for Type 2 Diabetes Mellitus Patients Using a Network Approach
Int. J. Environ. Res. Public Health 2020, 17(2), 596; https://doi.org/10.3390/ijerph17020596 (registering DOI) - 16 Jan 2020
Abstract
The prevalence of chronic disease comorbidity has increased worldwide. Comorbidity—i.e., the presence of multiple chronic diseases—is associated with adverse health outcomes in terms of mobility and quality of life as well as financial burden. Understanding the progression of comorbidities can provide valuable insights [...] Read more.
The prevalence of chronic disease comorbidity has increased worldwide. Comorbidity—i.e., the presence of multiple chronic diseases—is associated with adverse health outcomes in terms of mobility and quality of life as well as financial burden. Understanding the progression of comorbidities can provide valuable insights towards the prevention and better management of chronic diseases. Administrative data can be used in this regard as they contain semantic information on patients’ health conditions. Most studies in this field are focused on understanding the progression of one chronic disease rather than multiple diseases. This study aims to understand the progression of two chronic diseases in the Australian health context. It specifically focuses on the comorbidity progression of cardiovascular disease (CVD) in patients with type 2 diabetes mellitus (T2DM), as the prevalence of these chronic diseases in Australians is high. A research framework is proposed to understand and represent the progression of CVD in patients with T2DM using graph theory and social network analysis techniques. Two study cohorts (i.e., patients with both T2DM and CVD and patients with only T2DM) were selected from an administrative dataset obtained from an Australian health insurance company. Two baseline disease networks were constructed from these two selected cohorts. A final disease network from two baseline disease networks was then generated by weight adjustments in a normalized way. The prevalence of renal failure, fluid and electrolyte disorders, hypertension and obesity was significantly higher in patients with both CVD and T2DM than patients with only T2DM. This showed that these chronic diseases occurred frequently during the progression of CVD in patients with T2DM. The proposed network-based model may potentially help the healthcare provider to understand high-risk diseases and the progression patterns between the recurrence of T2DM and CVD. Also, the framework could be useful for stakeholders including governments and private health insurers to adopt appropriate preventive health management programs for patients at a high risk of developing multiple chronic diseases. Full article
(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)
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Open AccessArticle
VIKOR Method for MAGDM Based on Q-Rung Interval-Valued Orthopair Fuzzy Information and Its Application to Supplier Selection of Medical Consumption Products
Int. J. Environ. Res. Public Health 2020, 17(2), 525; https://doi.org/10.3390/ijerph17020525 - 14 Jan 2020
Abstract
The VIKOR model has been considered a viable tool for many decision-making applications in the past few years, given the advantages of considering the compromise between maximizing the utility of group and minimizing personal regrets. The q-rung interval-valued orthopair fuzzy set (q-RIVOFS) is [...] Read more.
The VIKOR model has been considered a viable tool for many decision-making applications in the past few years, given the advantages of considering the compromise between maximizing the utility of group and minimizing personal regrets. The q-rung interval-valued orthopair fuzzy set (q-RIVOFS) is a generalization of intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS) and has emerged to solve more complex and uncertain decision making problems which IFS and PFS cannot handle. In this manuscript, the key innovation is to combine the traditional VIKOR model with q-RIVOFS to develop the q-rung interval-valued orthopair fuzzy VIKOR model. In the new developed model, to express more information, the attribute’s values in MAGDM problems are depicted by q-RIVOFNs. First of all, some basic theories and aggregation operators of q-RIVOFNs are simply introduced. Then we develop the origin VIKOR model to q-RIVOFS environment and briefly express the computing steps of this new established model. Thereafter, the effectiveness of the model is verified by an example of supplier selection of medical consumer products and through comparative analysis, the superiority of the new method is further illustrated. Full article
(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)
Open AccessArticle
Potential Confounders in the Analysis of Brazilian Adolescent’s Health: A Combination of Machine Learning and Graph Theory
Int. J. Environ. Res. Public Health 2020, 17(1), 90; https://doi.org/10.3390/ijerph17010090 - 21 Dec 2019
Abstract
The prevalence of health problems during childhood and adolescence is high in developing countries such as Brazil. Social inequality, violence, and malnutrition have strong impact on youth health. To better understand these issues we propose to combine machine-learning methods and graph analysis to [...] Read more.
The prevalence of health problems during childhood and adolescence is high in developing countries such as Brazil. Social inequality, violence, and malnutrition have strong impact on youth health. To better understand these issues we propose to combine machine-learning methods and graph analysis to build predictive networks applied to the Brazilian National Student Health Survey (PenSE 2015) data, a large dataset that consists of questionnaires filled by the students. By using a combination of gradient boosting machines and centrality hub metric, it was possible to identify potential confounders to be considered when conducting association analyses among variables. The variables were ranked according to their hub centrality to predict the other variables from a directed weighted-graph perspective. The top five ranked confounder variables were “gender”, “oral health care”, “intended education level”, and two variables associated with nutrition habits—“eat while watching TV” and “never eat fast-food”. In conclusion, although causal effects cannot be inferred from the data, we believe that the proposed approach might be a useful tool to obtain novel insights on the association between variables and to identify general factors related to health conditions. Full article
(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)
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Open AccessArticle
Supplier Selection of Medical Consumption Products with a Probabilistic Linguistic MABAC Method
Int. J. Environ. Res. Public Health 2019, 16(24), 5082; https://doi.org/10.3390/ijerph16245082 - 12 Dec 2019
Abstract
In order to obtain an optimal medical consumption product supplier, the integration of combined weights and multi-attributive border approximation area comparison (MABAC) under probabilistic linguistic sets (PLTSs) has offered a novel integrated model in which the CRiteria Importance Through Intercriteria Correlation (CRITIC) method [...] Read more.
In order to obtain an optimal medical consumption product supplier, the integration of combined weights and multi-attributive border approximation area comparison (MABAC) under probabilistic linguistic sets (PLTSs) has offered a novel integrated model in which the CRiteria Importance Through Intercriteria Correlation (CRITIC) method is employed for calculating the objective weights of various attributes and the MABAC method with PLTSs is used to acquire the final ranking result of a medical consumption product supplier. Additionally, so as to indicate the applicability of the devised method, this model is confirmed by a numerical case for the supplier selection of medical consumption products. Some comparative studies are made with some existing methods. The proposed method can also successfully select suitable alternatives in other selection problems. Full article
(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)
Open AccessArticle
Pythagorean 2-Tuple Linguistic Taxonomy Method for Supplier Selection in Medical Instrument Industries
Int. J. Environ. Res. Public Health 2019, 16(23), 4875; https://doi.org/10.3390/ijerph16234875 - 03 Dec 2019
Abstract
Supplier selection in medical instrument industries is a classical multiple attribute group decision making (MAGDM) problem. The Pythagorean 2-tuple linguistic sets (P2TLSs) can reflect uncertain or fuzzy information well and solve the supplier selection in medical instrument industries, and the original Taxonomy is [...] Read more.
Supplier selection in medical instrument industries is a classical multiple attribute group decision making (MAGDM) problem. The Pythagorean 2-tuple linguistic sets (P2TLSs) can reflect uncertain or fuzzy information well and solve the supplier selection in medical instrument industries, and the original Taxonomy is very appropriate for comparing different alternatives with respect to their advantages from studied attributes. In this study, we present an algorithm that combines Pythagorean 2-tuple linguistic numbers (P2TLNs) with the Taxonomy method, where P2TLNs are applied to express the evaluation of decision makers on alternatives. Relying on the Pythagorean 2-tuple linguistic weighted average (P2TLWA) operator or Pythagorean 2-tuple linguistic weighted geometric (P2TLWG) operator to fuse P2TLNs, the new general framework is established for Pythagorean 2-tuple linguistic multiple attribute group decision making (MAGDM) under the classical Taxonomy method. Ultimately, an application case for supplier selection in medical instrument industries is designed to test the novel method’s applicability and practicality and a comparative analysis with three other methods is used to elaborate further. Full article
(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)
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Open AccessArticle
Healthcare Supply Chain Network Coordination Through Medical Insurance Strategies with Reference Price Effect
Int. J. Environ. Res. Public Health 2019, 16(18), 3479; https://doi.org/10.3390/ijerph16183479 - 18 Sep 2019
Abstract
China has established the universal medical insurance system and individual out of pocket costs have decreased, however, the average healthcare expenditure of the Chinese population and the expenses of the whole society have increased substantially. One major challenge which impedes the progress of [...] Read more.
China has established the universal medical insurance system and individual out of pocket costs have decreased, however, the average healthcare expenditure of the Chinese population and the expenses of the whole society have increased substantially. One major challenge which impedes the progress of attaining sustainable development of the social healthcare system in China is that the number of hospital admissions is disproportionate. Superior hospitals are overcrowded, whereas subordinate hospitals are experiencing low admissions. In this paper, we apply the game theory model to coordinate the healthcare supply chain network, which is composed of the government, medical insurance fund, superior hospitals, subordinate hospitals and patients. Especially by taking the reference price effect into account, this paper analyzes different medical insurance reimbursement strategies and their influence on patient choice and the healthcare supply chain network. The result shows that the reference price effect increases the leverage of medical insurance, guides patients’ choice, optimizes the allocation of medical resources and reduces the medical expends. In comparison to a decentralized decision- making strategy, a centralized decision- making strategy can stimulate both superior hospital and subordinate hospital’s cooperative intentions which benefits the social healthcare system. Full article
(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)
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