Patient Identification, A Review of the Use of Biometrics in the ICU

Jonas, Max; Solangasenathirajan, S.; Hett, D.A.

doi:10.1007/978-3-319-03746-2_50

Cited by 3 publications

(5 citation statements)

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“…Biometrics datasets: containing measurable features related to human characteristics. Biometrics data is used as a form of identification and access control [ 29 ].…”

Section: Reviewmentioning

confidence: 99%

Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends

2014

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The emergence of massive datasets in a clinical setting presents both challenges and opportunities in data storage and analysis. This so called “big data” challenges traditional analytic tools and will increasingly require novel solutions adapted from other fields. Advances in information and communication technology present the most viable solutions to big data analysis in terms of efficiency and scalability. It is vital those big data solutions are multithreaded and that data access approaches be precisely tailored to large volumes of semi-structured/unstructured data.The MapReduce programming framework uses two tasks common in functional programming: Map and Reduce. MapReduce is a new parallel processing framework and Hadoop is its open-source implementation on a single computing node or on clusters. Compared with existing parallel processing paradigms (e.g. grid computing and graphical processing unit (GPU)), MapReduce and Hadoop have two advantages: 1) fault-tolerant storage resulting in reliable data processing by replicating the computing tasks, and cloning the data chunks on different computing nodes across the computing cluster; 2) high-throughput data processing via a batch processing framework and the Hadoop distributed file system (HDFS). Data are stored in the HDFS and made available to the slave nodes for computation.In this paper, we review the existing applications of the MapReduce programming framework and its implementation platform Hadoop in clinical big data and related medical health informatics fields. The usage of MapReduce and Hadoop on a distributed system represents a significant advance in clinical big data processing and utilization, and opens up new opportunities in the emerging era of big data analytics. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools. This paper is concluded by summarizing the potential usage of the MapReduce programming framework and Hadoop platform to process huge volumes of clinical data in medical health informatics related fields.

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“…Biometrics datasets: containing measurable features related to human characteristics. Biometrics data is used as a form of identification and access control [ 29 ].…”

Section: Reviewmentioning

confidence: 99%

Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends

2014

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“…Biometrics refers to the recognition of individuals based on their anatomical, physiological, and/or behavioral characteristics, which permits identification without physical objects. Biometric options are not limited to fingerprint scanners but also include palm scanners, iris scanners, etc for patient verification [14,15]. However, there are limitations and disadvantages associated with these measures.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, unconscious patients are not able to place their finger onto the scanner by themselves. Although iris scans seem to be the ideal option for patient verification compared with other various verification methods, the main drawback is that it is difficult to scan the iris of an unconscious patient, and therefore, it is virtually impossible to verify unconscious patients with an iris scan [15,17]. Furthermore, when a surgical operation is required, patients must be put under anesthesia, negating the iris scanner as a valid option.…”

Section: Introductionmentioning

confidence: 99%

A Facial Recognition Mobile App for Patient Safety and Biometric Identification: Design, Development, and Validation

Jeon¹,

Jeong²,

Jee³

et al. 2019

JMIR Mhealth Uhealth

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Background Patient verification by unique identification is an important procedure in health care settings. Risks to patient safety occur throughout health care settings by failure to correctly identify patients, resulting in the incorrect patient, incorrect site procedure, incorrect medication, and other errors. To avoid medical malpractice, radio-frequency identification (RFID), fingerprint scanners, iris scanners, and other technologies have been implemented in care settings. The drawbacks of these technologies include the possibility to lose the RFID bracelet, infection transmission, and impracticality when the patient is unconscious. Objective The purpose of this study was to develop a mobile health app for patient identification to overcome the limitations of current patient identification alternatives. The development of this app is expected to provide an easy-to-use alternative method for patient identification. Methods We have developed a facial recognition mobile app for improved patient verification. As an evaluation purpose, a total of 62 pediatric patients, including both outpatient and inpatient, were registered for the facial recognition test and tracked throughout the facilities for patient verification purpose. Results The app was developed to contain 5 main parts: registration, medical records, examinations, prescriptions, and appointments. Among 62 patients, 30 were outpatients visiting plastic surgery department and 32 were inpatients reserved for surgery. Whether patients were under anesthesia or unconscious, facial recognition verified all patients with 99% accuracy even after a surgery. Conclusions It is possible to correctly identify both outpatients and inpatients and also reduce the unnecessary cost of patient verification by using the mobile facial recognition app with great accuracy. Our mobile app can provide valuable aid to patient verification, including when the patient is unconscious, as an alternative identification method.

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“…Light passes easily through the tissue in the finger and is absorbed by the haemoglobin in the blood, rendering an image from which vein pattern can be derived and matched to a patient profile in a database. Finger vein scanners are portable, the technology is safe and noninvasive, and several studies conducted in healthcare settings, including in emergency departments, have shown the high reliability of vascular biometrics [3].…”

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confidence: 99%

“…"There has been only limited penetration [of biometric identification] into the health technology arena and one reason for this is the relative ignorance of biometric systems by healthcare professionals", wrote Max Jonas and colleagues at the University Hospital Southampton, UK, in a recent review on the topic [3]. "It is a certain prediction that with the accelerating development of health informatics in hospitals, biometric identification will have to become embedded within our clinical information systems".…”

mentioning

confidence: 99%