RFID in Healthcare – Current Trends and the Future

Sundaresan, Saravanan; Doss, Robin; Zhou, Wanlei

doi:10.1007/978-3-319-12817-7_36

Cited by 9 publications

(8 citation statements)

References 75 publications

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“…Although there is potential for computational resources to account for layout-related workload, capturing distances between objects to estimate travel time such as patient beds assigned to one nurse or from bed-tolinen cart, represents a diffi cult task, made increasingly complex by the variable placement of mobile items. Resource monitoring using radio frequency identifi cation is a promising technology; however, ethnical concerns and issues of scalability must be addressed (Sundaresan et al, 2015). Although data are available to link specifi c infants to bed assignments, data linking infants to nursing assignments are often maintained in separate staffi ng software.…”

Section: Features Commentsmentioning

confidence: 99%

Data-Driven Nurse Staffing in the Neonatal Intensive Care Unit

Feldman

Rohan

2022

McN: The American Journal of Maternal/Child Nursing

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The challenge of nurse staffi ng is amplifi ed in the acute care neonatal intensive care unit (NICU) setting, where a wide range of highly variable factors affect staffi ng. A comprehensive overview of infant factors (severity, intensity), nurse factors (education, experience, preferences, team dynamics), and unit factors (structure, layout, shift length, care model) infl uencing pre-shift NICU staffi ng is presented, along with how intra-shift variability of these and other factors must be accounted for to maintain effective and effi cient assignments. There is opportunity to improve workload estimations and acuity measures for pre-shift staffi ng using technology and predictive

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Section: Features Commentsmentioning

confidence: 99%

Data-Driven Nurse Staffing in the Neonatal Intensive Care Unit

Feldman

Rohan

2022

McN: The American Journal of Maternal/Child Nursing

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“…With the increase in the number of patients worldwide, chances of mistreatment due to medical errors also increase, therefore patient safety must be highlighted, as a dominant and life threatening health concern [25]. A majority of medical errors are due to misidentification of specimen and incorrect blood and drug transmission which results in mistreatment of the patients [26]. To address these issues RFID technology is being used to automatically identify each patient within the hospital.…”

Section: Related Workmentioning

confidence: 99%

Genetic Algorithm Based Automatic Out-Patient Experience Management System (GAPEM) Using RFIDs and Sensors

et al. 2021

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This paper introduces a novel framework which combines the outputs from Radio Frequency Identification (RFID) technology, the automated outpatient feedback survey form, Hospital Management Information System (HMIS) and sensors to develop an automated patient experience management system (PEMS) using a Genetic algorithm (GA). The output from the RFID tag is the time spent by a patient at various stations in the hospital. While the output from the automated survey is an overall satisfaction index (OSI), which is the overall experience (in the form of a number) a patient has during his/her stay in the hospital. HMIS has details regarding the structure of the hospital; this includes details about doctors, nurses, rooms, location of various departments, etc. In addition, environmental conditions (temperature and humidity) from installed sensors are used to capture the physical context of the patient's experience. To develop an automated PEMS GA is used for computing the patient experience. The collected data (timing information, HMIS and sensor data) is given as input so that the GA generates optimized weights which are then applied to the final PEMS to automatically produce the overall satisfaction index best matching with OSI. This proposed framework reduces the time taken by manual statistics by automating the complete interaction of patient and hospital staff at all stations. The experiments are performed using the developed tool, in a local hospital, and the results demonstrate an accuracy of 80.3%. This accuracy gives a good indication to hospital management in real-time to take measures in areas where the patient experience is going relatively low. INDEX TERMSGenetic Algorithm (GA), Hospital management information system (HMIS), Overall satisfaction index (OSI), Patient experience management index (PEMI), Radio Frequency Identification device (RFID)

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“…Attacks mostly raise confidentiality issues on shared data and location privacy concerns. Attackers can often compromise sensitive health data related to treatments stored in tag data [ 136 ]. Even solutions such as encrypted RFID implementations are known to be susceptible to side channel attacks due to their passive nature [ 137 ].…”

Section: Security In Iomt Communication Protocolsmentioning

confidence: 99%

Security in IoMT Communications: A Survey

Koutras

Stergiopoulos

Dasaklis

et al. 2020

Sensors

106

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The Internet of Medical Things (IoMT) couples IoT technologies with healthcare services in order to support real-time, remote patient monitoring and treatment. However, the interconnectivity of critical medical devices with other systems in various network layers creates new opportunities for remote adversaries. Since most of the communication protocols have not been specifically designed for the needs of connected medical devices, there is a need to classify the available IoT communication technologies in terms of security. In this paper we classify IoT communication protocols, with respect to their application in IoMT. Then we describe the main characteristics of IoT communication protocols used at the perception, network and application layer of medical devices. We examine the inherent security characteristics and limitations of IoMT-specific communication protocols. Based on realistic attacks we identify available mitigation controls that may be applied to secure IoMT communications, as well as existing research and implementation gaps.

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RFID in Healthcare – Current Trends and the Future

Cited by 9 publications

References 75 publications

Data-Driven Nurse Staffing in the Neonatal Intensive Care Unit

Data-Driven Nurse Staffing in the Neonatal Intensive Care Unit

Genetic Algorithm Based Automatic Out-Patient Experience Management System (GAPEM) Using RFIDs and Sensors

Security in IoMT Communications: A Survey

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