Special Issue "Healthcare System Innovation"

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

Deadline for manuscript submissions: 31 August 2018

Special Issue Editors

Guest Editor
Prof. Dr. Wenbing Zhao

Department of Electrical Engineering and Computer Science, Cleveland State University, Ohio, 44011, USA
Website | E-Mail
Interests: human–computer interaction; rehabilitation; computer vision; distributed systems
Guest Editor
Dr. Longzhi Yang

Department of Computer Science and Digital Technologies, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
Website | E-Mail
Interests: computational/artificial intelligence; reasoning under uncertainty; intelligent control
Guest Editor
Dr. Hao Qiu

Department of Engineering Technology, Fort Valley State University, Fort Valley, GA 31030, USA
Website | E-Mail
Interests: Internet of Things, Wearable Sensors, Pulsed Power, Simulation
Guest Editor
Prof. Dr. Yonghong Peng

Faculty of Computer Science, University of Sunderland, St Peters Campus, Sunderland, SR6 0DD, UK
Website | E-Mail
Interests: data science; big data; artificial intelligence; bioinformatics; healthcare informatics; digital health

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:

  1. Smart healthcare system analysis and design
  2. Computer-aided methods for design procedure and manufacture of healthcare system
  3. Computer and human-machine interaction of healthcare system
  4. Internet technology on healthcare  system innovation
  5. Application of IoT (Internet of Things) on healthcare system
  6. 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 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. Applied System Innovation is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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 (4 papers)

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Research

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Open AccessFeature PaperArticle Design and Feasibility of a Safe Pill Bottle
Appl. Syst. Innov. 2018, 1(2), 13; https://doi.org/10.3390/asi1020013
Received: 5 April 2018 / Revised: 28 April 2018 / Accepted: 3 May 2018 / Published: 6 May 2018
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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.
[...] Read more.
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
Appl. Syst. Innov. 2018, 1(2), 10; https://doi.org/10.3390/asi1020010
Received: 16 January 2018 / Revised: 4 April 2018 / Accepted: 4 April 2018 / Published: 10 April 2018
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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
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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
Appl. Syst. Innov. 2018, 1(1), 3; https://doi.org/10.3390/asi1010003
Received: 29 November 2017 / Revised: 18 December 2017 / Accepted: 18 December 2017 / Published: 22 December 2017
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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
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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 AccessReview A Review of Medication Adherence Monitoring Technologies
Appl. Syst. Innov. 2018, 1(2), 14; https://doi.org/10.3390/asi1020014
Received: 1 April 2018 / Revised: 26 April 2018 / Accepted: 27 April 2018 / Published: 6 May 2018
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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
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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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