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

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 30267

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Special Issue Editors

Dr. Yichuan Wang

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Guest Editor

Newcastle University Business School, Newcastle University, United Kingdom
Interests: Digital Health; Healthcare Information Technologies; Healthcare Analytics

Prof. Dr. William (Yu Chuang) Wang

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Co-Guest Editor

Waikato Management School, University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand
Interests: systems architect; databases; enterprise systems; supply chain management; digital innovation; business intelligence and data management; e-Heath management

Special Issue Information

Dear Colleagues,

The massive amounts of digital health data such as electronic healthcare records (EHRs), sensor data, and patient-generated content stored in social media could be a valuable source of supporting healthcare organizations’ clinical practices and operations, public health, and medical research if it is analyzed in meaningful ways. Big data analytics is increasingly advocated as an emerging technology in health care to fill this growing need. However, the adoption of big data analytics in health care usually lags behind other industries, as some major technological and managerial obstacles still remain. Obstacles include the lack of health data integration, data overload issues, data privacy and security, and limited or inefficient data visualization. In addition, the value of public health data is rarely discovered, analyzed, and visualized, either for improving meaning use or establishing, implementing, and assessing public health policies.

As a result, there is an urgent need for further research to (1) technologically explore how to utilize digital health data to support evidence-based medicine using analytics approaches and (2) demonstrate how big-data analytics enables healthcare practitioners and policy makers to sufficiently address societal health concerns and challenges. To this end, this Special Issue is seeking conceptual, empirical, or technological papers offering new insights into, but not limited to, the following topics:

The applications of descriptive, predictive, and prescriptive analytics to capture hidden patterns and insights from digital health data and public health data.
Big data analytics for evidence-informed healthcare.
Big data analytics for optimising healthcare operations.
Case studies of utilizing big data analytics for better decision-making.
Organizational learning and culture impact on big data analytics adoption.
Data governance and data security in digital health.
The visualization of health data for improving the accuracy of decision-making.

Dr. Yichuan Wang
Dr. William Yu Chung Wang
Guest Editors

Manuscript Submission Information

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Keywords

big data analtyics
digital health
data visualization
evidence-based medicine
social media analtyics
analtyics for public health
data governance in digital health

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Published Papers (6 papers)

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Research

23 pages, 3142 KiB

Open AccessArticle

Tripartite Data Analysis for Optimizing Telemedicine Operations: Evidence from Guizhou Province in China

by Jinna Yu, Tingting Zhang, Zhen Liu, Assem Abu Hatab and Jing Lan

Int. J. Environ. Res. Public Health 2020, 17(1), 375; https://doi.org/10.3390/ijerph17010375 - 6 Jan 2020

Cited by 8 | Viewed by 7207

Abstract

Telemedicine is an innovative approach that helps alleviate the health disparity in developing countries and improve health service accessibility, affordability, and quality. Few studies have focused on the social and organizational issues involved in telemedicine, despite in-depth studies of and significant improvements in these technologies. This paper used evolutionary game theory to analyze behavioral strategies and their dynamic evolution in the implementation and operation of telemedicine. Further, numerical simulation was carried out to develop management strategies for promoting telemedicine as a new way of delivering health services. The results showed that: (1) When the benefits are greater than the costs, the higher medical institutions (HMIs), primary medical institutions (PMIs), and patients positively promote telemedicine with benign interactions; (2) when the costs are greater than the benefits, the stability strategy of HMIs, PMIs, and patients is, respectively, ‘no efforts’, ‘no efforts’, and ‘non-acceptance’; and (3) promotion of telemedicine is influenced by the initial probability of the ‘HMI efforts’, ‘PMI efforts’, and ‘patients’ acceptance’ strategy chosen by the three stakeholders, telemedicine costs, and the reimbursement ratio of such costs. Based on theoretical analysis, in order to verify the theoretical model, this paper introduces the case study of a telemedicine system integrated with health resources at provincial, municipal, county, and township level in Guizhou. The findings of the case study were consistent with the theoretical analysis. Therefore, the central Chinese government and local governments should pay attention to the running cost of MIs and provide financial support when the costs are greater than the benefits. At the same time, the government should raise awareness of telemedicine and increase participation by all three stakeholders. Lastly, in order to promote telemedicine effectively, it is recommended that telemedicine services are incorporated within the scope of medical insurance and the optimal reimbursement ratio is used. Full article

(This article belongs to the Special Issue Analytics in Digital Health)

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14 pages, 4462 KiB

Open AccessArticle

Improving the Efficiency of an Emergency Department Based on Activity-Relationship Diagram and Radio Frequency Identification Technology

by Shao-Jen Weng, Ming-Che Tsai, Yao-Te Tsai, Donald F. Gotcher, Chih-Hao Chen, Shih-Chia Liu, Yeong-Yuh Xu and Seung-Hwan Kim

Int. J. Environ. Res. Public Health 2019, 16(22), 4478; https://doi.org/10.3390/ijerph16224478 - 14 Nov 2019

Cited by 7 | Viewed by 4412

Abstract

Emergency department crowding has been one of the main issues in the health system in Taiwan. Previous studies have usually targeted the process improvement of patient treatment flow due to the difficulty of collecting Emergency Department (ED) staff data. In this study, we have proposed a hybrid model with Discrete Event Simulation, radio frequency identification applications, and activity-relationship diagrams to simulate the nurse movement flows and identify the relationship between different treatment sections. We used the results to formulate four facility layouts. Through comparing four scenarios, the simulation results indicated that 2.2 km of traveling distance or 140 min of traveling time reduction per nurse could be achieved from the best scenario. Full article

(This article belongs to the Special Issue Analytics in Digital Health)

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11 pages, 613 KiB

Open AccessArticle

Characteristics of Traditional Chinese Medicine Use for Carpal Tunnel Syndrome

by Meng-Chuan Tsai, Yu-Hsien Kuo, Chih-Hsin Muo, Li-Wei Chou and Chung-Yen Lu

Int. J. Environ. Res. Public Health 2019, 16(21), 4086; https://doi.org/10.3390/ijerph16214086 - 24 Oct 2019

Cited by 2 | Viewed by 3837

Abstract

Carpal tunnel syndrome (CTS) is a common musculoskeletal disorder and an occupational disease caused by repeated exercise or overuse of the hand. We investigated the characteristics of traditional Chinese medicine (TCM) use by practitioners in CTS patients, including demographic variables, socioeconomic status, previous medical conditions, health care use, and hospital characteristics for TCM health care. This cross-sectional study identified 25,965 patients newly diagnosed with CTS based on the first medical diagnosis recorded between 1999 and 2013 in the nationwide representative insurance database of Taiwan. The date of initial CTS diagnosis in outpatient data was defined as the index date, and four patients were excluded because of missing gender-related information. Patients who used TCM care as the first option at their diagnosis were classified as TCM users (n = 677; 2.61%), and all others were TCM non-users (n = 25,288; 97.4%). In the all variables-adjusted model, female patients had an adjusted odds ratio (OR; 95% CI) of TCM use of 1.35 (1.11–1.66). National Health Insurance (NHI) registration was associated with higher odds ratios of TCM use in central, southern, and eastern Taiwan than in northern Taiwan (ORs = 1.43, 1.86, and 1.82, respectively). NHI registration was associated with higher odds ratios of TCM use in rural cities than in urban cities (OR (95% CI) = 1.33 (1.02–1.72)). The TCM group had a 20% less likelihood of exhibiting symptoms, signs, and ill-defined conditions and injury and poisoning. The TCM group had a 56% lower likelihood of having diseases of the musculoskeletal system and connective tissue. Multi-level model outcomes were similar to the results of the all variables-adjusted model, except for the NHI registration outcome in rural and urban cities (OR [95% CI] = 1.33 [0.98–1.81]). Significant associations between the number of TCM visits and TCM use were observed in all logistic regression models. The study presented key demographic characteristics, health care use, and medical conditions associated with TCM use for CTS. Previous experience of TCM use may affect the use of TCM for CTS treatment. This information provides a reference for the allocations of relevant medical resources and health care providers. Full article

(This article belongs to the Special Issue Analytics in Digital Health)

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18 pages, 1652 KiB

Open AccessArticle

Identification of the Differential Effect of City-Level on the Gini Coefficient of Health Service Delivery in Online Health Community

by Hai-Yan Yu, Jing-Jing Chen, Jying-Nan Wang, Ya-Ling Chiu, Hang Qiu and Li-Ya Wang

Int. J. Environ. Res. Public Health 2019, 16(13), 2314; https://doi.org/10.3390/ijerph16132314 - 29 Jun 2019

Cited by 16 | Viewed by 3659

Abstract

Inequality of health services for different specialty categories not only occurs in different areas in the world, but also happens in the online service platform. In the online health community (OHC), health services often display inequality for different specialty categories, including both online views and medical consultations for offline registered services. Moreover, how the city-level factors impact the inequality of health services in OHC is still unknown. We designed a causal inference study with data on distributions of serviced patients and online views in over 100 distinct specialty categories on one of the largest OHCs in China. To derive the causal effect of the city-levels (two levels inducing 1 and 0) on the Gini coefficient, we matched the focus cases in cities with rich healthcare resources with the potential control cities. For each of the specialty categories, we first estimated the average treatment effect of the specialty category’s Gini coefficient (SCGini) with the balanced covariates. For the Gini coefficient of online views, the average treatment effect of level-1 cities is 0.573, which is 0.016 higher than that of the matched group. Similarly, for the Gini coefficient of serviced patients, the average treatment effect of level-1 cities is 0.470, which is 0.029 higher than that of the matched group. The results support the argument that the total Gini coefficient of the doctors in OHCs shows that the inequality in health services is still very serious. This study contributes to the development of a theoretically grounded understanding of the causal effect of city-level factors on the inequality of health services in an online to offline health service setting. In the future, heterogeneous results should be considered for distinct groups of doctors who provide different combinations of online contributions and online attendance. Full article

(This article belongs to the Special Issue Analytics in Digital Health)

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10 pages, 979 KiB

Open AccessArticle

Public Perception on Healthcare Services: Evidence from Social Media Platforms in China

by Guangyu Hu, Xueyan Han, Huixuan Zhou and Yuanli Liu

Int. J. Environ. Res. Public Health 2019, 16(7), 1273; https://doi.org/10.3390/ijerph16071273 - 10 Apr 2019

Cited by 26 | Viewed by 6028

Abstract

Social media has been used as data resource in a growing number of health-related research. The objectives of this study were to identify content volume and sentiment polarity of social media records relevant to healthcare services in China. A list of the key words of healthcare services were used to extract data from WeChat and Qzone, between June 2017 and September 2017. The data were put into a corpus, where content analyses were performed using Tencent natural language processing (NLP). The final corpus contained approximately 29 million records. Records on patient safety were the most frequently mentioned topic (approximately 8.73 million, 30.1% of the corpus), with the contents on humanistic care having received the least social media references (0.43 Million, 1.5%). Sentiment analyses showed 36.1%, 16.4%, and 47.4% of positive, neutral, and negative emotions, respectively. The doctor-patient relationship category had the highest proportion of negative contents (74.9%), followed by service efficiency (59.5%), and nursing service (53.0%). Neutral disposition was found to be the highest (30.4%) in the contents on appointment-booking services. This study added evidence to the magnitude and direction of public perceptions on healthcare services in China’s hospital and pointed to the possibility of monitoring healthcare service improvement, using readily available data in social media. Full article

(This article belongs to the Special Issue Analytics in Digital Health)

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20 pages, 2505 KiB

Open AccessArticle

Merging Data Diversity of Clinical Medical Records to Improve Effectiveness

by Berit I. Helgheim, Rui Maia, Joao C. Ferreira and Ana Lucia Martins

Int. J. Environ. Res. Public Health 2019, 16(5), 769; https://doi.org/10.3390/ijerph16050769 - 3 Mar 2019

Cited by 9 | Viewed by 4436

Abstract

Medicine is a knowledge area continuously experiencing changes. Every day, discoveries and procedures are tested with the goal of providing improved service and quality of life to patients. With the evolution of computer science, multiple areas experienced an increase in productivity with the implementation of new technical solutions. Medicine is no exception. Providing healthcare services in the future will involve the storage and manipulation of large volumes of data (big data) from medical records, requiring the integration of different data sources, for a multitude of purposes, such as prediction, prevention, personalization, participation, and becoming digital. Data integration and data sharing will be essential to achieve these goals. Our work focuses on the development of a framework process for the integration of data from different sources to increase its usability potential. We integrated data from an internal hospital database, external data, and also structured data resulting from natural language processing (NPL) applied to electronic medical records. An extract-transform and load (ETL) process was used to merge different data sources into a single one, allowing more effective use of these data and, eventually, contributing to more efficient use of the available resources. Full article

(This article belongs to the Special Issue Analytics in Digital Health)

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