A Case Study of Bluetooth Technology as a Supplemental Tool in Contact Tracing.

IF 5.9 Q1 Computer Science Journal of Healthcare Informatics Research Pub Date : 2022-01-19 eCollection Date: 2022-06-01 DOI:10.1007/s41666-021-00112-9
Ryan Admiraal, Jules Millen, Ankit Patel, Tim Chambers
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引用次数: 2

Abstract

We present results from a 7-day trial of a Bluetooth-enabled card by the New Zealand Ministry of Health to investigate its usefulness in contact tracing. A comparison of the card with traditional contact tracing, which relies on self-reports of contacts to case investigators, demonstrated significantly higher levels of internal consistency in detected contact events by Bluetooth-enabled cards with 88% of contact events being detected by both cards involved in an interaction as compared to 64% for self-reports of contacts to case investigators. We found no clear evidence of memory recall worsening in reporting contact events that were further removed in time from the date of a case investigation. Roughly 66% of contact events between trial participants that were indicated by cards went unreported to case investigators, simultaneously highlighting the shortcomings of traditional contact tracing and the value of Bluetooth technology in detecting contact events that may otherwise go unreported. At the same time, cards detected only 65% of self-reported contact events, in part due to increasing non-compliance as the study progressed. This would suggest that Bluetooth technology can only be considered as a supplemental tool in contact tracing and not a viable replacement to traditional contact tracing unless measures are introduced to ensure greater compliance.

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蓝牙技术作为接触追踪补充工具的案例研究。
我们介绍了新西兰卫生部对蓝牙卡进行的为期7天的试验结果,以调查其在接触者追踪中的有用性。传统的接触者追踪依赖于向案件调查员自我报告的接触者,将这种卡与传统的接触者追踪进行比较,结果表明,蓝牙卡在检测到的接触事件中具有明显更高的内部一致性,参与互动的两张卡都检测到88%的接触事件,而向案件调查员自我报告的接触者为64%。我们没有发现明确的证据表明,在报告接触事件时,从病例调查之日起进一步删除的记忆回忆恶化。通过卡片显示的试验参与者之间的接触事件中,大约66%没有报告给案件调查员,同时突出了传统接触追踪的缺点,以及蓝牙技术在检测可能未报告的接触事件方面的价值。与此同时,卡片只检测到65%的自我报告的接触事件,部分原因是随着研究的进展,不遵守规定的情况越来越多。这表明蓝牙技术只能被视为接触追踪的补充工具,而不是传统接触追踪的可行替代品,除非采取措施确保更大的合规性。
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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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