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Information
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Data Science in Health Services
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Data Science in Health Services

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 15857

Special Issue Editors

Dr. Philippe J. Giabbanelli

E-Mail Website
Guest Editor
Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
Interests: health behaviors; machine learning; modeling; simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Jennifer M. Badham

E-Mail Website
Guest Editor
Department of Sociology, Durham University, Durham DH1 3LE, UK
Interests: network diffusion; social simulation; participatory methods
Dr. Teresa B. Gibson

E-Mail Website
Guest Editor
School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA
Interests: healthcare utilization and insurance; quantitative analysis and machine learning; incentives; impact assessment

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue on “Data Science for Health Services”. Health services have been transformed by the emergence and increased applications of data science methods such as predictive modelling, visualization, and artificial intelligence. These methods routinely contribute to planning and managing health services and delivering care, thus improving the health of individuals and communities. Research on data science methods for health services covers several stages:

  • At the collection stage, data needs to be acquired, stored safely and effectively, and occasionally combined. Data broadly construed includes demographic and clinical information in electronic medical records (EMRs), insurance claims, and other administrative data, as well as data continuously flowing from devices grouped under the Internet of Things (IoT). Recent innovations include virtual hospitals, wearable biosensors, digital health apps, and smart monitors. New data warehouse designs are often sought to handle constraints such as the handling of identifiable records, the large scale of the records, and the need to efficiently support various queries. Finally, data fusion is required to augment common sources with value-added information or derive comprehensive measures for health services (e.g., quality index).
  • At the analysis and forecasting stage, artificial intelligence (AI) allows for the exploration of patterns or the assessment of possible future scenarios. Machine learning (ML) techniques can serve to predict healthcare outcomes such as quality, utilization, or cost. Modeling and simulation (M&S) provides estimates for scenarios, such as the impact of a vaccination scheme on the number of beds in intensive care units. ML and M&S both face challenges in terms of data (e.g., insufficient data for emerging problems, conflicting measures) and algorithmic efficiency (e.g., scaling to big data).
  • The adoption of data science methods in health services sheds light on how to translate results into actions that improve the care for individuals and better meet the health needs of communities. Such translational efforts include novel multidisciplinary initiatives which bridge academic or organizational silos, for example when social scientists, epidemiologists, and modelers create joint frameworks. Adoption also needs to navigate regulatory and legal frameworks, particularly in a changing ecosystem (e.g., new laws on data protection) and given the emergence of new approaches to safely perform computations (e.g., federated learning, secure enclaves).

We solicit papers for this Special Issue that broadly deal with such challenges by addressing open questions, providing novel case studies, or encourage interesting and challenging debates. Papers can be reviews, syntheses, viewpoints, meta-analyses, or original research articles.

Dr. Philippe J. Giabbanelli
Dr. Jennifer M. Badham
Dr. Teresa B. Gibson
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. Information is an international peer-reviewed open access monthly 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 1600 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 decision support
  • clinical care models
  • health informatics
  • quality of care
  • population health planning
  • digital health

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Editorial

Jump to: Research

2 pages, 159 KiB  
Editorial
Data Science in Health Services
by Philippe J. Giabbanelli and Jennifer Badham
Information 2023, 14(6), 344; https://doi.org/10.3390/info14060344 - 17 Jun 2023
Viewed by 1135
Abstract
Data have been fundamental to the scientific practice of medicine since at least the time of Hippocrates around 2500 years ago, relying on the detailed observation of cases and rigorous comparison between cases [...] Full article
(This article belongs to the Special Issue Data Science in Health Services)

Research

Jump to: Editorial

36 pages, 17666 KiB  
Article
Atlas-Based Shared-Boundary Deformable Multi-Surface Models through Multi-Material and Two-Manifold Dual Contouring
by Tanweer Rashid, Sharmin Sultana, Mallar Chakravarty and Michel Albert Audette
Information 2023, 14(4), 220; https://doi.org/10.3390/info14040220 - 3 Apr 2023
Cited by 1 | Viewed by 2034
Abstract
This paper presents a multi-material dual “contouring” method used to convert a digital 3D voxel-based atlas of basal ganglia to a deformable discrete multi-surface model that supports surgical navigation for an intraoperative MRI-compatible surgical robot, featuring fast intraoperative deformation computation. It is vital [...] Read more.
This paper presents a multi-material dual “contouring” method used to convert a digital 3D voxel-based atlas of basal ganglia to a deformable discrete multi-surface model that supports surgical navigation for an intraoperative MRI-compatible surgical robot, featuring fast intraoperative deformation computation. It is vital that the final surface model maintain shared boundaries where appropriate so that even as the deep-brain model deforms to reflect intraoperative changes encoded in ioMRI, the subthalamic nucleus stays in contact with the substantia nigra, for example, while still providing a significantly sparser representation than the original volumetric atlas consisting of hundreds of millions of voxels. The dual contouring (DC) algorithm is a grid-based process used to generate surface meshes from volumetric data. The DC method enables the insertion of vertices anywhere inside the grid cube, as opposed to the marching cubes (MC) algorithm, which can insert vertices only on the grid edges. This multi-material DC method is then applied to initialize, by duality, a deformable multi-surface simplex model, which can be used for multi-surface atlas-based segmentation. We demonstrate our proposed method on synthetic and deep-brain atlas data, and a comparison of our method’s results with those of traditional DC demonstrates its effectiveness. Full article
(This article belongs to the Special Issue Data Science in Health Services)
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29 pages, 6259 KiB  
Article
Human Factors in Leveraging Systems Science to Shape Public Policy for Obesity: A Usability Study
by Philippe J. Giabbanelli and Chirag X. Vesuvala
Information 2023, 14(3), 196; https://doi.org/10.3390/info14030196 - 20 Mar 2023
Cited by 7 | Viewed by 2465
Abstract
Background: despite a broad consensus on their importance, applications of systems thinking in policymaking and practice have been limited. This is partly caused by the longstanding practice of developing systems maps and software in the intention of supporting policymakers, but without knowing [...] Read more.
Background: despite a broad consensus on their importance, applications of systems thinking in policymaking and practice have been limited. This is partly caused by the longstanding practice of developing systems maps and software in the intention of supporting policymakers, but without knowing their needs and practices. Objective: we aim to ensure the effective use of a systems mapping software by policymakers seeking to understand and manage the complex system around obesity, physical, and mental well-being. Methods: we performed a usability study with eight policymakers in British Columbia based on a software tool (ActionableSystems) that supports interactions with a map of obesity. Our tasks examine different aspects of systems thinking (e.g., unintended consequences, loops) at several levels of mastery and cover common policymaking needs (identification, evaluation, understanding). Video recordings provided quantitative usability metrics (correctness, time to completion) individually and for the group, while pre- and post-usability interviews yielded qualitative data for thematic analysis. Results: users knew the many different factors that contribute to mental and physical well-being in obesity; however, most were only familiar with lower-level systems thinking concepts (e.g., interconnectedness) rather than higher-level ones (e.g., feedback loops). Most struggles happened at the lowest level of the mastery taxonomy, and predominantly on network representation. Although participants completed tasks on loops and multiple pathways mostly correctly, this was at the detriment of spending significant time on these aspects. Results did not depend on the participant, as their experiences with the software were similar. The thematic analysis revealed that policymakers did not have a typical workflow and did not use any special software or tools in their policy work; hence, the integration of a new tool would heavily depend on individual practices. Conclusions: there is an important discrepancy between what constitutes systems thinking to policymakers and what parts of systems thinking are supported by software. Tools may be more successfully integrated when they include tutorials (e.g., case studies), facilitate access to evidence, and can be linked to a policymaker’s portfolio. Full article
(This article belongs to the Special Issue Data Science in Health Services)
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24 pages, 1968 KiB  
Article
Public Health Policy Monitoring through Public Perceptions: A Case of COVID-19 Tweet Analysis
by Chih-yuan Li, Michael Renda, Fatima Yusuf, James Geller and Soon Ae Chun
Information 2022, 13(11), 543; https://doi.org/10.3390/info13110543 - 16 Nov 2022
Cited by 6 | Viewed by 2832
Abstract
Since the start of the COVID-19 pandemic, government authorities have responded by issuing new public health policies, many of which were intended to contain its spread but ended up limiting economic and social activities. The citizen responses to these policies are diverse, ranging [...] Read more.
Since the start of the COVID-19 pandemic, government authorities have responded by issuing new public health policies, many of which were intended to contain its spread but ended up limiting economic and social activities. The citizen responses to these policies are diverse, ranging from goodwill to fear and anger. It is challenging to determine whether or not these public health policies achieved the intended impact. This requires systematic data collection and scientific studies, which can be very time-consuming. To overcome such challenges, in this paper, we provide an alternative approach to continuously monitor and dynamically make sense of how public health policies impact citizens. Our approach is to continuously collect Twitter posts related to COVID-19 policies and to analyze the public reactions. We have developed a web-based system that collects tweets daily and generates timelines and geographical displays of citizens’ “concern levels”. Tracking the public reactions towards different policies can help government officials assess the policy impacts in a more dynamic and real-time manner. For this paper, we collected and analyzed over 16 million tweets related to ten policies over a 10-month period. We obtained several findings; for example, the “COVID-19 (General)” and ”Ventilators” policies engendered the highest concern levels, while the “Face Coverings” policy caused the lowest. Nine out of ten policies exhibited significant changes in concern levels during the observation period. Full article
(This article belongs to the Special Issue Data Science in Health Services)
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12 pages, 1865 KiB  
Article
Poor Compliance of Diabetic Patients with AI-Enabled E-Health Self-Care Management in Saudi Arabia
by Fuhid Alanazi, Valerie Gay and Ryan Alturki
Information 2022, 13(11), 509; https://doi.org/10.3390/info13110509 - 25 Oct 2022
Cited by 4 | Viewed by 3536
Abstract
Still in its nascent stage, the Kingdom of Saudi Arabia’s self-care system lacks most features of a state-of-the-art e-health care system. With the Industrial Revolution 4.0 and the expanding use of artificial intelligence (AI), e-health initiatives in Saudi Arabia are increasing, which is [...] Read more.
Still in its nascent stage, the Kingdom of Saudi Arabia’s self-care system lacks most features of a state-of-the-art e-health care system. With the Industrial Revolution 4.0 and the expanding use of artificial intelligence (AI), e-health initiatives in Saudi Arabia are increasing, which is compelling academics, clinicians, and policymakers to develop a better understanding of e-health trends, their efficacy, and their high impact areas. An increase in the number of diabetic patients in the Kingdom demands improvements to the current e-health care system, where the capability to manage diabetic patients is still in its infancy. In this survey, a total of 210 valid responses were obtained for analysis. SPSS version 27.0 was used for the quantitative analysis. The main technique used to address the aims of the data analysis was Spearman’s correlation analysis. This study indicated that the compliance rate with prescribed medication, blood glucose monitoring, and insulin injections from hospitals is increasing, with the highest rates found for Jeddah City. However, diet control and physical activity compliance levels were found to be poorly combined, predominantly due to the lower number of registered patients in the e-health care system. This non-compliance trends with selected variables (education and income) and highlights the dire need for improvement to the current health system by the inclusion of the latest technology, including big data, cloud computing, and the Internet of Things (IoT). Hence, this study suggests the implementation of government-regulated e-health care systems on mobile-based policies. The study revealed the experience of patients using e-health systems, which could be used to improve their efficacy and durability. More research needs to be conducted to address the deficiencies in the current e-health care system regarding diabetes care, and how it can be integrated into the healthcare system in general. Full article
(This article belongs to the Special Issue Data Science in Health Services)
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15 pages, 2901 KiB  
Article
Identifying the Early Signs of Preterm Birth from U.S. Birth Records Using Machine Learning Techniques
by Alireza Ebrahimvandi, Niyousha Hosseinichimeh and Zhenyu James Kong
Information 2022, 13(7), 310; https://doi.org/10.3390/info13070310 - 25 Jun 2022
Cited by 2 | Viewed by 2287
Abstract
Preterm birth (PTB) is the leading cause of infant mortality in the U.S. and globally. The goal of this study is to increase understanding of PTB risk factors that are present early in pregnancy by leveraging statistical and machine learning (ML) techniques on [...] Read more.
Preterm birth (PTB) is the leading cause of infant mortality in the U.S. and globally. The goal of this study is to increase understanding of PTB risk factors that are present early in pregnancy by leveraging statistical and machine learning (ML) techniques on big data. The 2016 U.S. birth records were obtained and combined with two other area-level datasets, the Area Health Resources File and the County Health Ranking. Then, we applied logistic regression with elastic net regularization, random forest, and gradient boosting machines to study a cohort of 3.6 million singleton deliveries to identify generalizable PTB risk factors. The response variable is preterm birth, which includes spontaneous and indicated PTB, and we performed a binary classification. Our results show that the most important predictors of preterm birth are gestational and chronic hypertension, interval since last live birth, and history of a previous preterm birth, which explains 10.92, 5.98, and 5.63% of the predictive power, respectively. Parents’ education is one of the influential variables in predicting PTB, explaining 7.89% of the predictive power. The relative importance of race declines when parents are more educated or have received adequate prenatal care. The gradient boosting machines outperformed with an AUC of 0.75 (sensitivity: 0.64, specificity: 0.73) for the validation dataset. In this study, we compare our results with seminal and most related studies to demonstrate the superiority of our results. The application of ML techniques improved the performance measures in the prediction of preterm birth. The results emphasize the importance of socioeconomic factors such as parental education as one of the most important indicators of preterm birth. More research is needed on these mechanisms through which socioeconomic factors affect biological responses. Full article
(This article belongs to the Special Issue Data Science in Health Services)
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