Role of Emerging Technology for Building Smart Hospital Information System

Thakare, Vaibhav; Khire, Gauri

doi:10.1016/s2212-5671(14)00223-8

Cited by 20 publications

(10 citation statements)

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“…The primary application space for point of need sensors has been the analysis of unique targets using relatively low cost, rapid detection platforms [37], including small molecules [38,39], viruses [40,41] and cells [42,43] (amongst other targets). Recent works have focused on enhancing the mobility of point of need sensors for rapid on site applications [44] by limiting the requirement for equipment or post hoc methodologies that depend on a formal laboratory. Most portable/handheld sensors are not designed to compete with standard analytical laboratory diagnostics, but rather as a parallel tool to trigger new questions or provide additional sampling to improve resolution.…”

Section: Sensor Engineeringmentioning

confidence: 99%

SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems

McLamore

Datta

Morgan

et al. 2019

Sensors

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In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced complexity machine-assisted decision support tool. We summarize the approaches used for mobile phone-based chemical/biological sensors, including general hardware and software requirements for signal transduction and acquisition. We introduce SNAPS, part of a platform approach to converge sensor data and analytics. The platform is designed to consist of a portfolio of modular tools which may lend itself to dynamic composability by enabling context-specific selection of relevant units, resulting in case-based working modules. SNAPS is an element of this platform where data analytics, statistical characterization and algorithms may be delivered to the data either via embedded systems in devices, or sourced, in near real-time, from mist, fog or cloud computing resources. Convergence of the physical systems with the cyber components paves the path for SNAPS to progress to higher levels of artificial reasoning tools (ART) and emerge as data-informed decision support, as a service for general societal needs. Proof of concept examples of SNAPS are demonstrated both for quantitative data and qualitative data, each operated using a mobile device (smartphone or tablet) for data acquisition and analytics. We discuss the challenges and opportunities for SNAPS, centered around the value to users/stakeholders and the key performance indicators users may find helpful, for these types of machine-assisted tools.

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Section: Sensor Engineeringmentioning

confidence: 99%

SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems

McLamore

Datta

Morgan

et al. 2019

Sensors

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“…One of the major BIM uses towards the management of physical assets is its lifecycle contribution towards the asset information management system (AIMS), which contains geometric and non-geometric information derived from the information accumulated throughout the project lifecycle prior to the completion and commissioning of the physical built assets [7]. As the management of smart buildings from an organizational perspective is a key component of Industry 4.0 development, smart hospitals are among those institutions that can benefit from an evolved information system, as such systems would cover information pertaining to physical and digital assets, as well as information from clients (patients) or suppliers that would impact the management of operations of a smart hospital [8,9].…”

Section: Introductionmentioning

confidence: 99%

BIM for Smart Hospital Management during COVID-19 Using MCDM

Lin

Chen

2021

Sustainability

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In context of the recent COVID-19 pandemic, smart hospitals’ contributions to pre-medical, remote diagnosis, and social distancing has been further vetted. Smart hospital management evolves with new technology and knowledge management, which needs an evaluation system to prioritize its associated criteria and sub-criteria. The global effect of the COVID-19 pandemic further necessitates a comprehensive research of smart hospital management. This paper will utilize Analytical Hierarchy Process (AHP) within Multiple Criteria Decision Making (MCDM) to establish a smart hospital evaluation system with evaluation criteria and sub-criteria, which were then further prioritized and mapped to BIM-related alternatives to inform asset information management (AIM) practices. This context of this study included the expert opinions of six professionals in the smart hospital field and collected 113 responses from hospital-related personnel. The results indicated that functionalities connected to end users are critical, in particular IoT’s Network Core Functionalities, AI’s Deep Learning and CPS’s Special Network Technologies. Furthermore, BIM’s capability to contribute to the lifecycle management of assets can relate and contribute to the asset-intensive physical criteria of smart hospitals, in particular IoT, service technology innovations and their sub-criteria.

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“…One of the necessary conditions for developing of a smart hospital is creation and optimization of clinical and administrative processes along with designing and using novel technologies and equipment. These conditions cannot be met without interconnected assets [15][16][17][18]. In this regard, Big Data is an important part in smart hospitals since a large volume of data is automatically produced each moment.…”

Section: Introductionmentioning

confidence: 99%

Smart hospitals around the world: a systematic review

Rasoulian-Kasrineh

Sharifzadeh

Tabatabaei

2021

Preprint

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Background It is almost impossible to think about a non-digital world today. digital technologies have changed our life style and they have also affected many domains including health domain. We are all aware that paper-based hospitals have changed a lot to be paper-less. Then a new term was born which is “smart hospital” and studies have been conducted in this field, so this study was designed to conduct a comprehensive review about smart hospitals. Methods 741 studies were identified using unique keywords through searching in the PubMed, ScienceDirect, Embase and Scopus databases. Overall, after applying the inclusion and exclusion criteria (647 based on abstract title and 33 after reading full text) and removing duplicates (43), 18 studies were included in this review. Results Geographically, most of the articles were from Asia (50%). The highest number of publications were observed in 2012. A multidisciplinary team were involved in 77% of the researches and 61.12% of them were conducted in more than one research center. The majority of articles have been published in Q1 quality journals (33.34%) and high-income countries accounted for the largest percentage (66.67) of publishing smart hospital related articles. 27.78% of the studies were aimed at patient care, which had the highest percentage. Among the technologies used, RFID was the most and 66.67% of the articles came from researches about the implementation of a smart hospital. Conclusions it should be noted that development or implementation of a smart device in a hospital should not be considered as implementing a smart hospital, while many studies have used the term smart hospital in their study. Many Asian researchers have used the term “smart hospital” in their articles, but on the other hand, most of related articles published in Q1 journals were from America and Europe. Also, it was also observed that most studies focused on the two concepts including resource management and patient care.

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Role of Emerging Technology for Building Smart Hospital Information System

Cited by 20 publications

References 0 publications

SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems

SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems

BIM for Smart Hospital Management during COVID-19 Using MCDM

Smart hospitals around the world: a systematic review

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