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Special Issue "Healthcare System Innovation"

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A special issue of Applied System Innovation (ISSN 2571-5577).

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 57842

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Special Issue Editors

Prof. Dr. Wenbing Zhao

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Guest Editor

Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, USA
Interests: fault-tolerant computing; computer and network security; peer-to-peer and grid computing; performance evaluation of distributed systems
Special Issues, Collections and Topics in MDPI journals

Dr. Longzhi Yang

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Guest Editor

Department of Computer Science and Digital Technologies, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
Interests: computational/artificial intelligence; reasoning under uncertainty; intelligent control

Dr. Hao Qiu

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Guest Editor

Department of Engineering Technology, Fort Valley State University, Fort Valley, GA 31030, USA
Interests: Internet of Things; wearable sensors; pulsed power; simulation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yonghong Peng

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Guest Editor

Faculty of Computer Science, University of Sunderland, St Peter Campus, Sunderland SR6 0DD, UK
Interests: data science; machine learning; artificial intelligence; digital health and medical informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Healthcare is undergoing a sector-wide transformation thanks to advances in computing, networking technologies, big data and artificial intelligence. Healthcare is, not only changing from reactive and hospital-centered to preventive and personalized, but is also changing from disease focused to well-being centered. Healthcare systems, as well as fundamental medicine research, are becoming smarter, enabled by technological innovations. We anticipate significant improvements in areas ranging from decision support for healthcare professionals through big data analytics to supporting behavior changes through technology-enabled self-management, as well as social and motivational support. Furthermore, with cutting edge sensors and computer technologies, healthcare delivery could also yield better efficiency, higher quality and lower cost. In this Special Issue, we welcome original research, as well as review articles, in all areas of healthcare system innovation.

Potential topics include, but are not limited to:

Smart healthcare system analysis and design
Computer-aided methods for design procedure and manufacture of healthcare system
Computer and human-machine interaction of healthcare system
Internet technology on healthcare system innovation
Application of IoT (Internet of Things) on healthcare system
Big data and artificial intelligence enabled healthcare systems

Prof. Dr. Wenbing Zhao
Dr. Longzhi Yang
Dr. Hao Qiu
Prof. Dr. Yonghong Peng
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. Applied System Innovation 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 1400 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

Smart healthcare system analysis and design
Computer and human-machine interaction of healthcare system
Application of IoT (Internet of Things) on healthcare system
Big data and artificial intelligence in healthcare.

Published Papers (11 papers)

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Research

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Open AccessEditor’s ChoiceArticle

Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition

and

Appl. Syst. Innov. 2019, 2(1), 7; https://doi.org/10.3390/asi2010007 - 19 Feb 2019

Cited by 7 | Viewed by 3488

Abstract

Egocentric video analysis is an important tool in healthcare that serves a variety of purposes, such as memory aid systems and physical rehabilitation, and feature extraction is an indispensable process for such analysis. Local feature descriptors have been widely applied due to their simple implementation and reasonable efficiency and performance in applications. This paper proposes an enhanced spatial and temporal local feature descriptor extraction method to boost the performance of action classification. The approach allows local feature descriptors to take advantage of saliency maps, which provide insights into visual attention. The effectiveness of the proposed method was validated and evaluated by a comparative study, whose results demonstrated an improved accuracy of around 2%. Full article

(This article belongs to the Special Issue Healthcare System Innovation)

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Open AccessArticle

Comparison of the Changes in the Structure of the Transverse Arch of the Normal and Hallux Valgus Feet under Different Loading Positions

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Appl. Syst. Innov. 2019, 2(1), 3; https://doi.org/10.3390/asi2010003 - 15 Jan 2019

Cited by 4 | Viewed by 5206

Abstract

The transverse arch of the foot receives and transfers loads during gait. We aim to identify the difference in its structure between normal feet and hallux valgus (HV) feet and the effects of loading. Two groups, Without-HV and With-HV (HV ≥ 20°), were assessed using a weight-bearing plantar ultrasound imaging device to view the structure of the transverse arch. Measurements were recorded in sitting, quiet standing, and 90% weight-shift (90% W.S.) loading positions on the tested foot. Images were then processed using ImageJ software to analyze the transverse arch length (TAL), the length between the metatarsal heads (MTHs), transverse arch height (TAH), and the height of each MTH. TAL significantly increased in all positions in the With-HV group compared to that in the Without-HV group. It also increased in both groups under loading. TAH was not significantly higher in the With-HV group than in the Without-HV group in sitting and standing positions, except in the 90% W.S position, where both groups showed similar results. TAH decreased in both groups under loading. In summary, the structure of the transverse arch changes in HV feet and under loading conditions. This finding will help understand the structural differences between normal and HV feet and help resolve shoe fit problems in individuals with HV deformity. Full article

(This article belongs to the Special Issue Healthcare System Innovation)

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Open AccessArticle

Design and Development of a Web Application for Matching Drug Addiction Treatment Services with Substance Users

Sachin Hiriyanna

Miyuki F. Tedor

Patricia A. Stoddard-Dare

and

Wenbing Zhao

Appl. Syst. Innov. 2018, 1(4), 47; https://doi.org/10.3390/asi1040047 - 30 Nov 2018

Cited by 1 | Viewed by 2769

Abstract

One of the current and biggest problems in the system of emergency care for the drug overdose epidemic is the failure of information delivery on nearby treatment facilities. Even though some initiatives have tried to solve this issue, they either failed in delivering the information or in providing good usability. This paper presents the design and development of a web application that we refer to as DrugHelp.Care. This application delivers highly accurate, easy-to-understand, and targeted information in a timely manner for substance users and their well-wishers. It also provides an ecosystem for the treatment facilities with an easy-to-use interface to constantly update their complex information along with automatic email reminders and data completion progress indicators. Based on the requirements we have collected from substance users and treatment facilities, the application is designed and developed using the LAMP stack. A search engine for the substance users and their well-wishers preserves complete anonymity, which is very important to ensure the confidentiality of substance users. Full article

(This article belongs to the Special Issue Healthcare System Innovation)

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Open AccessArticle

Active Compact Wearable Body Area Networks for Wireless Communication, Medical and IoT Applications

Albert Sabban

Appl. Syst. Innov. 2018, 1(4), 46; https://doi.org/10.3390/asi1040046 - 23 Nov 2018

Cited by 9 | Viewed by 4127

Abstract

The development of compact wearable antennas and transceivers for communication, IoT (Internet of Things), and biomedical systems will be presented in this paper. Development of Compact efficient wearable antennas is one of the major challenges in development of wearable communication, IoT, and medical systems. The main goal of wireless body area networks (BANs), WBANs, is to provide continuously medical data to the physician. Body area network (BAN) antennas should be flexible, lightweight, compact, and have low production cost. However, low efficiency is the major disadvantage of small printed antennas. Microstrip antennas resonant frequency is altered, due to environment conditions, different antenna locations, and different system operation modes. These disadvantages may be solved by using compact active and tunable antennas. A new class of wideband active wearable antennas for medical applications is presented in this paper. Amplifiers may be connected to the wearable antenna feed line to increase the system dynamic range. Small lightweight batteries supply the bias voltage to the active components. An active dual polarized antenna is presented in this paper. The active dual polarized antenna gain is 14 ± 3 dB for frequencies ranging from 380 to 600 MHz. The active transmitting dual polarized antenna output power is around 18 dBm. A voltage-controlled diode, varactor, may be used to control the antenna electrical performance at different environments. For example, an antenna located in patient stomach area has VSWR (Voltage Standing Wave Ratio) better than 2:1 at 434 MHz. However, if the antenna will be placed on the patient back, it may resonate at 420 MHz. By varying the varactor bias voltage, the antenna resonant frequency may be shifted from 420 to 434 MHz. An ultra-wideband passive and active printed slot antenna may be employed in wideband wearable communication systems. The active slot antenna gain is 13 ± 2 dB for frequencies from 800 MHz to 3 GHz. Full article

(This article belongs to the Special Issue Healthcare System Innovation)

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Open AccessArticle

Data Governance in the Health Industry: Investigating Data Quality Dimensions within a Big Data Context

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Appl. Syst. Innov. 2018, 1(4), 43; https://doi.org/10.3390/asi1040043 - 01 Nov 2018

Cited by 9 | Viewed by 5637

Abstract

In the health industry, the use of data (including Big Data) is of growing importance. The term ‘Big Data’ characterizes data by its volume, and also by its velocity, variety, and veracity. Big Data needs to have effective data governance, which includes measures to manage and control the use of data and to enhance data quality, availability, and integrity. The type and description of data quality can be expressed in terms of the dimensions of data quality. Well-known dimensions are accuracy, completeness, and consistency, amongst others. Since data quality depends on how the data is expected to be used, the most important data quality dimensions depend on the context of use and industry needs. There is a lack of current research focusing on data quality dimensions for Big Data within the health industry; this paper, therefore, investigates the most important data quality dimensions for Big Data within this context. An inner hermeneutic cycle research approach was used to review relevant literature related to data quality for big health datasets in a systematic way and to produce a list of the most important data quality dimensions. Based on a hierarchical framework for organizing data quality dimensions, the highest ranked category of dimensions was determined. Full article

(This article belongs to the Special Issue Healthcare System Innovation)

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Open AccessArticle

An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search

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Appl. Syst. Innov. 2018, 1(3), 33; https://doi.org/10.3390/asi1030033 - 03 Sep 2018

Cited by 4 | Viewed by 3517

Abstract

Some machine learning algorithms have shown a better overall recognition rate for facial recognition than humans, provided that the models are trained with massive image databases of human faces. However, it is still a challenge to use existing algorithms to perform localized people search tasks where the recognition must be done in real time, and where only a small face database is accessible. A localized people search is essential to enable robot–human interactions. In this article, we propose a novel adaptive ensemble approach to improve facial recognition rates while maintaining low computational costs, by combining lightweight local binary classifiers with global pre-trained binary classifiers. In this approach, the robot is placed in an ambient intelligence environment that makes it aware of local context changes. Our method addresses the extreme unbalance of false positive results when it is used in local dataset classifications. Furthermore, it reduces the errors caused by affine deformation in face frontalization, and by poor camera focus. Our approach shows a higher recognition rate compared to a pre-trained global classifier using a benchmark database under various resolution images, and demonstrates good efficacy in real-time tasks. Full article

(This article belongs to the Special Issue Healthcare System Innovation)

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Open AccessArticle

Modeling the 2013 Zika Outbreak in French Polynesia: Intervention Strategies

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Appl. Syst. Innov. 2018, 1(3), 31; https://doi.org/10.3390/asi1030031 - 24 Aug 2018

Cited by 2 | Viewed by 3104

Abstract

The ongoing Zika virus (ZIKV) in the Americas has been a serious public health emergency since 2015. Since Zika is a vector-borne disease, the size of the vector population in the affected area plays a key role in controlling the scale of the outbreak. The primary vectors for Zika, the Aedes Agypti and Aedes Albopictus species of mosquitoes, are highly sensitive to climatic conditions for survival and reproduction. Additionally, increased international travel over the years has caused the disease outbreak to turn into a pandemic affecting five continents. The mosquito population and the human travel patterns are the two main driving forces affecting the persistence and resurgence of Zika and other vector-borne diseases. This paper presents an enhanced dynamic model that simulates the 2013–2014 French Polynesia Zika outbreak incorporating the temperature dependent mosquito ecology and the local transit network (flights and ferries). The study highlights the importance of human travel patterns and mosquito population dynamics in a disease outbreak. The results predict that more than 85% of the population was infected by the end of the outbreak and it lasted for more than five months across the islands. The basic reproduction number (

R_{0}

) for the outbreak is also calculated using the next-generation-matrix for validation purposes. Additionally, this study is focused on measuring the impact of intervention strategies like reducing the mosquito population, preventing mosquito bites and imposing travel bans. French Polynesia was chosen as the region of interest for the study because of available demographic, climate and transit data. Additionally, results from similar studies for the region are available for validation and comparison. However, the proposed system can be used to study the transmission dynamics of any vector-borne disease in any geographic region by altering the climatic and demographic data, and the transit network. Full article

(This article belongs to the Special Issue Healthcare System Innovation)

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Open AccessFeature PaperArticle

Design and Feasibility of a Safe Pill Bottle

Emil Jovanov

B.M.S. Bahar Talukder

David C. Schwebel

and

W. Douglas Evans

Appl. Syst. Innov. 2018, 1(2), 13; https://doi.org/10.3390/asi1020013 - 06 May 2018

Cited by 9 | Viewed by 5519

Abstract

Ubiquitous intelligence of Internet of Things (IoT) objects and new sensors provide innovative solutions for a variety of health issues. Unintentional child poisoning represents an increasingly important health issue worldwide, partially because of an increase in the use of drugs and food supplements. Although child-resistant bottle caps have probably saved many lives, they are not foolproof and do not provide warnings for parents and caregivers when children try to access the bottles. In this paper we present a design, implementation, and feasibility analysis of an intelligent “safe pill bottle” that can identify when a child is trying to open a bottle and then generate an immediate warning to deter a child from opening the bottle and send alerts to parents/guardians. The bottle controller uses capacitive sensing to identify the class of user. We present the results of pilot testing with eight adults and eight children using neural networks (NN). With 474 bottle-opening events, our NN had 96.4% accuracy of predicting whether the user was a child or an adult. Preliminary results demonstrate that smart pill bottles may be an effective tool to prevent unintentional child poisoning. Full article

(This article belongs to the Special Issue Healthcare System Innovation)

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Open AccessArticle

Health Symptom Checking System for Elderly People Using Fuzzy Analytic Hierarchy Process

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Appl. Syst. Innov. 2018, 1(2), 10; https://doi.org/10.3390/asi1020010 - 10 Apr 2018

Cited by 13 | Viewed by 4392

Abstract

The ever-escalating rise in numbers of the aging population has preempted a revolutionary change in the healthcare sector and serves as a major counterpoint to modern life in the 21st century. Increasing demand being placed on the health sector is almost certainly an inevitable process. However, providing appropriate healthcare services is requisite for senior citizens who suffer from various health issues and conditions. To minimize these health risks, we derived an intuitive technique for determining the incongruity of health symptoms by using a symptom checker, which is embedded into a versatile mobile app named Help-to-You (H2U). The designed app helps the users and carers to determine and identify conceivable reasons for elderly ailments and to assist users in deciding when to counsel a health practitioner. The intention of this empirical study was to further analyze and foresee certain variations of infections based on the symptoms accounted for by the patient. The recommended solution consolidated conceptual design with multi-criteria decision analysis (MCDA) technique and an analytic hierarchy process (AHP) with fuzzy weights to deal with the uncertainty of imprecision and ambiguity resulting from various disease factors. Experimental results verified the effectiveness of the proposed model, subsequently providing a variety of life assistance services. Full article

(This article belongs to the Special Issue Healthcare System Innovation)

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Open AccessFeature PaperArticle

Design, Implementation, and Field Testing of a Privacy-Aware Compliance Tracking System for Bedside Care in Nursing Homes

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Appl. Syst. Innov. 2018, 1(1), 3; https://doi.org/10.3390/asi1010003 - 22 Dec 2017

Cited by 14 | Viewed by 6124

Abstract

Lower back musculoskeletal disorders are pervasive in workplaces. In the United States alone, the total cost of such injuries exceed $100 billion a year. The lower-back injury rate in the healthcare sector is one of the highest among all industry sectors. A main risk factor for lower-back injuries is the use of improper body mechanics when doing lifting and pulling activities. In healthcare venues, nursing homes in particular, nursing assistants are on the front line to take care of patients. Even in places where ceiling-mounted lifting equipment is installed, they are still required to handle the patient for bedside care, such as sliding the sling underneath the patient, scooping up the patient, putting on compression socks, etc. To help nursing assistants get into the habit of using proper body mechanics, we designed and implemented a privacy-aware compliance tracking system (PACTS). PACTS can track a nursing assistant for possible violation of proper body mechanics while doing bedside care and provide realtime feedback via a smart wearable device such as a smart watch worn by the nursing assistant. The system was deployed in a local nursing home for an 80-day field study in six rooms with seven participating nursing assistants. The test exposed several issues with the original design of the system. The primary issue is how to balance the privacy requirement and the usability of the system. Over-emphasizing the former would negatively impact the latter. This issue is partially resolved with a leasing mechanism where the system would automatically register a nursing assistant within the lease period once she or he has manually registered with the system. Full article

(This article belongs to the Special Issue Healthcare System Innovation)

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Review

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Open AccessFeature PaperReview

A Review of Medication Adherence Monitoring Technologies

Murtadha Aldeer

Mehdi Javanmard

and

Richard P. Martin

Appl. Syst. Innov. 2018, 1(2), 14; https://doi.org/10.3390/asi1020014 - 06 May 2018

Cited by 75 | Viewed by 12857

Abstract

Medication non-adherence is a prevalent, complex problem. Failure to follow medication schedules may lead to major health complications, including death. Proper medication adherence is thus required in order to gain the greatest possible drug benefit during a patient’s treatment. Interventions have been proven to improve medication adherence if deviations are detected. This review focuses on recent advances in the field of technology-based medication adherence approaches and pays particular attention to their technical monitoring aspects. The taxonomy space of this review spans multiple techniques including sensor systems, proximity sensing, vision systems, and combinations of these. As each technique has unique advantages and limitations, this work describes their trade-offs in accuracy, energy consumption, acceptability and user’s comfort, and user authentication. Full article

(This article belongs to the Special Issue Healthcare System Innovation)

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