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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 August 2023) | Viewed by 16098

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Prof. Dr. Carolina Del Valle Soto

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Guest Editor

Academy of Computing, School of Engineering, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Mexico
Interests: algorithm design; optimization techniques; wireless sensor networks; jamming detection; artificial intelligence; routing in complex networks; wearable and IoT systems; energy-efficient protocols
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Dr. Luis Rizo-Dominguez

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Guest Editor

ITESO, Universidad Jesuita en Guadalajara, San Pedro Tlaquepaque 45604, Mexico
Interests: communications; 5G; machine learning; stochastic modeling; network traffic modeling; embedded systems

Special Issue Information

Dear Colleagues,

Medicine and technological advances often go hand in hand, which has allowed this industry to be transformed through technologies such as artificial intelligence, big data, or the Internet of Things. Information and communications technologies have integrated into the field of health. Clinical practice revolves around data, information, and knowledge. The Internet is the largest source of health information for professionals and patients because the Internet and hypermedia/multimedia technologies allow interactive communication in real time. In addition, technology is essential in medicine because of the development of these tools and their benefits, such as increased accessibility to medical care or life expectancy and quality of life for people. The development of corporate-type digital communications network infrastructures and widespread access to the Internet allow the flow of information between all parties, through using electronic medical records in a secure environment, improving the quality of services, and facilitating more efficient management and comfort for citizens. System-generated databases store vast amounts of information used for medical research, and with vast patient histories, scientists can better study disease trends and causes to generate predictive models.

Prof. Dr. Carolina Del Valle Soto
Dr. Luis Rizo-Dominguez
Guest Editors

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Keywords

medical information systems
health information technology
medical computing
usability engineering
artificial intelligence in medicine
smart medical information
wearable technology in medicine
health information interoperability
technology in medicine and healthcare

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Published Papers (5 papers)

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Research

25 pages, 1466 KiB

Open AccessArticle

The Large-Scale Implementation of a Health Information System in Brazilian University Hospitals: Process and Outcomes

by Clarissa Carneiro Mussi, Ricardo Luz, Dioni da Rosa Damázio, Ernani Marques dos Santos, Violeta Sun, Beatriz Silvana da Silveira Porto, Gabriel Oscar Cremona Parma, Luiz Alberto Cordioli, Robert Samuel Birch and José Baltazar Salgueirinho Osório de Andrade Guerra

Int. J. Environ. Res. Public Health 2023, 20(21), 6971; https://doi.org/10.3390/ijerph20216971 - 25 Oct 2023

Cited by 3 | Viewed by 2715

Abstract

Governments around the globe are paving the way for healthcare services that can have a profound impact on the overall well-being and development of their nations. However, government programs to implement health information technologies on a large-scale are challenging, especially in developing countries. In this article, the process and outcomes of the large-scale implementation of a hospital information system for the management of Brazilian university hospitals are analyzed. Based on a qualitative approach, this research involved 21 hospitals and comprised a documentary search, interviews with 24 hospital managers and two system user focus groups, and a questionnaire of 736 respondents. Generally, we observed that aspects relating to the wider context of system implementation (macro level), the managerial structure, cultural nuances, and political dynamics within each hospital (meso level), as well as the technology, work activities, and individuals themselves (micro level) acted as facilitators and/or obstacles to the implementation process. The dynamics and complex interactions established between these aspects had repercussions on the process, including the extended time necessary to implement the national program and the somewhat mixed outcomes obtained by hospitals in the national network. Mostly positive, these outcomes were linked to the eight emerging dimensions of practices and work processes; planning, control, and decision making; transparency and accountability; optimization in the use of resources; productivity of professionals; patient information security; safety and quality of care; and improvement in teaching and research. We argued here that to maximize the potential of information technology in healthcare on a large-scale, an integrative and cooperative vision is required, along with a high capacity for change management, considering the different regional, local, and institutional contexts. Full article

(This article belongs to the Special Issue Application of Information Technology in Medicine and Healthcare)

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22 pages, 2092 KiB

Open AccessArticle

Comparison of Collaborative and Cooperative Schemes in Sensor Networks for Non-Invasive Monitoring of People at Home

by Carolina Del-Valle-Soto, Leonardo J. Valdivia, Juan Carlos López-Pimentel and Paolo Visconti

Int. J. Environ. Res. Public Health 2023, 20(7), 5268; https://doi.org/10.3390/ijerph20075268 - 27 Mar 2023

Cited by 6 | Viewed by 2134

Abstract

This paper looks at wireless sensor networks (WSNs) in healthcare, where they can monitor patients remotely. WSNs are considered one of the most promising technologies due to their flexibility and autonomy in communication. However, routing protocols in WSNs must be energy-efficient, with a minimal quality of service, so as not to compromise patient care. The main objective of this work is to compare two work schemes in the routing protocol algorithm in WSNs (cooperative and collaborative) in a home environment for monitoring the conditions of the elderly. The study aims to optimize the performance of the algorithm and the ease of use for people while analyzing the impact of the sensor network on the analysis of vital signs daily using medical equipment. We found relationships between vital sign metrics that have a more significant impact in the presence of a monitoring system. Finally, we conduct a performance analysis of both schemes proposed for the home tracking application and study their usability from the user’s point of view. Full article

(This article belongs to the Special Issue Application of Information Technology in Medicine and Healthcare)

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20 pages, 8883 KiB

Open AccessArticle

Influence of Hand Tracking in Immersive Virtual Reality for Memory Assessment

by José Varela-Aldás, Jorge Buele, Irene López and Guillermo Palacios-Navarro

Int. J. Environ. Res. Public Health 2023, 20(5), 4609; https://doi.org/10.3390/ijerph20054609 - 5 Mar 2023

Cited by 5 | Viewed by 3877

Abstract

Few works analyze the parameters inherent to immersive virtual reality (IVR) in applications for memory evaluation. Specifically, hand tracking adds to the immersion of the system, placing the user in the first person with full awareness of the position of their hands. Thus, this work addresses the influence of hand tracking in memory assessment with IVR systems. For this, an application based on activities of daily living was developed, where the user must remember the location of the elements. The data collected by the application are the accuracy of the answers and the response time; the participants are 20 healthy subjects who pass the MoCA test with an age range between 18 to 60 years of age; the application was evaluated with classic controllers and with the hand tracking of the Oculus Quest 2. After the experimentation, the participants carried out presence (PQ), usability (UMUX), and satisfaction (USEQ) tests. The results indicate no difference with statistical significance between both experiments; controller experiments have 7.08% higher accuracy and 0.27 ys. faster response time. Contrary to expectations, presence was 1.3% lower for hand tracking, and usability (0.18%) and satisfaction (1.43%) had similar results. The findings indicate no evidence to determine better conditions in the evaluation of memory in this case of IVR with hand tracking. Full article

(This article belongs to the Special Issue Application of Information Technology in Medicine and Healthcare)

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16 pages, 2620 KiB

Open AccessArticle

Deep Learning Multi-Class Approach for Human Fall Detection Based on Doppler Signatures

by Jorge D. Cardenas, Carlos A. Gutierrez and Ruth Aguilar-Ponce

Int. J. Environ. Res. Public Health 2023, 20(2), 1123; https://doi.org/10.3390/ijerph20021123 - 8 Jan 2023

Cited by 10 | Viewed by 2911

Abstract

Falling events are a global health concern with short- and long-term physical and psychological implications, especially for the elderly population. This work aims to monitor human activity in an indoor environment and recognize falling events without requiring users to carry a device or sensor on their bodies. A sensing platform based on the transmission of a continuous wave (CW) radio-frequency (RF) probe signal was developed using general-purpose equipment. The CW probe signal is similar to the pilot subcarriers transmitted by commercial off-the-shelf WiFi devices. As a result, our methodology can easily be integrated into a joint radio sensing and communication scheme. The sensing process is carried out by analyzing the changes in phase, amplitude, and frequency that the probe signal suffers when it is reflected or scattered by static and moving bodies. These features are commonly extracted from the channel state information (CSI) of WiFi signals. However, CSI relies on complex data acquisition and channel estimation processes. Doppler radars have also been used to monitor human activity. While effective, a radar-based fall detection system requires dedicated hardware. In this paper, we follow an alternative method to characterize falling events on the basis of the Doppler signatures imprinted on the CW probe signal by a falling person. A multi-class deep learning framework for classification was conceived to differentiate falling events from other activities that can be performed in indoor environments. Two neural network models were implemented. The first is based on a long-short-term memory network (LSTM) and the second on a convolutional neural network (CNN). A series of experiments comprising 11 subjects were conducted to collect empirical data and test the system’s performance. Falls were detected with an accuracy of 92.1% for the LSTM case, while for the CNN, an accuracy rate of 92.1% was obtained. The results demonstrate the viability of human fall detection based on a radio sensing system such as the one described in this paper. Full article

(This article belongs to the Special Issue Application of Information Technology in Medicine and Healthcare)

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17 pages, 3126 KiB

Open AccessArticle

Drug Recommendation from Diagnosis Codes: Classification vs. Collaborative Filtering Approaches

by Apichat Sae-Ang, Sawrawit Chairat, Natchada Tansuebchueasai, Orapan Fumaneeshoat, Thammasin Ingviya and Sitthichok Chaichulee

Int. J. Environ. Res. Public Health 2023, 20(1), 309; https://doi.org/10.3390/ijerph20010309 - 25 Dec 2022

Cited by 15 | Viewed by 3407

Abstract

Over time, large amounts of clinical data have accumulated in electronic health records (EHRs), making it difficult for healthcare professionals to navigate and make patient-centered decisions. This underscores the need for healthcare recommendation systems that help medical professionals make faster and more accurate decisions. This study addresses drug recommendation systems that generate an appropriate list of drugs that match patients’ diagnoses. Currently, recommendations are manually prepared by physicians, but this is difficult for patients with multiple comorbidities. We explored approaches to drug recommendations based on elderly patients with diabetes, hypertension, and cardiovascular disease who visited primary-care clinics and often had multiple conditions. We examined both collaborative filtering approaches and traditional machine-learning classifiers. The hybrid model between the two yielded a recall at 5 of 76.61%, a precision at 5 of 46.20%, a macro-averaged area under the curve of 74.52%, and an average physician agreement of 47.50%. Although collaborative filtering is widely used in recommendation systems, our results showed that it consistently underperformed traditional classification. Collaborative filtering was sensitive to class imbalances and favored the more popular classes. This study highlighted challenges that need to be addressed when developing recommendation systems in EHRs. Full article

(This article belongs to the Special Issue Application of Information Technology in Medicine and Healthcare)

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