AUDIME: Augmented Disaster Medicine

Paulus, Alexander; Meisen, Philipp; Meisen, Tobias; Jeschke, Sabina; Czaplik, Michael; Hirsch, F.

doi:10.1007/978-3-319-42620-4_47

Cited by 5 publications

(7 citation statements)

References 3 publications

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“…A medical team must implement proper actions, especially when casualties are involved, due to challenges encountered within an area where an accident occurs. This procedure often results in an inefficient treatment of such casualties or the dissemination of wrong or misleading information to an executive emergency physician [74,75]. The acceptance and usability of wearable devices within the context of MCI management have also been evaluated [75].…”

Section: Literature Reviewmentioning

confidence: 99%

“…This procedure often results in an inefficient treatment of such casualties or the dissemination of wrong or misleading information to an executive emergency physician [74,75]. The acceptance and usability of wearable devices within the context of MCI management have also been evaluated [75]. A project named AUDIME has been proposed on the basis of a hands-free approach for mobile or wearable devices [76].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The acceptance and usability of wearable devices within the context of MCI management have also been evaluated [75]. A project named AUDIME has been proposed on the basis of a hands-free approach for mobile or wearable devices [76]. Similarly, the approach can be used for evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

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Development of IoT-based mhealth framework for various cases of heart disease patients

et al. 2021

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A newly distributed fault-tolerant mHealth framework-based Internet of things (IoT) is proposed in this study to resolve the essential problems of healthcare service provision during the occurrence of frequent failures in a telemedicine architecture. Two models are presented to support the telehealth development of chronic heart disease (CHD) in a distant environment. In model-1, a new local multisensor fusion triage algorithm known as three-level localisation triage (3LLT) is proposed. In 3LLT, a group of heterogeneous sources is applied to triage patients as a clinical process, and the emergency levels inside/ outside the home of a patient with CHD are determined. Failures related to sensor fusion can also be detected. In model-2, a centralised IoT connection towards distributed smart hospitals is employed by mHealth based on two attributes: (1) healthcare service packages and (2) time of arrival of a patient at a hospital. Three decision matrices have been used to overcome several issues on hospital selection based on multi-criteria decision-making by using an analytic hierarchy process. Two datasets are utilised: (1) a clinical CHD dataset, which includes 572 patients for testing model-1, and (2) a nonclinical dataset, which includes hospital healthcare service packages for testing model-2. Consequently, patients with CHD can be triaged into different emergency levels (risk, urgent and sick) with mHealth, and a final decision is made by selecting an appropriate hospital. Results are obtained through the clinical triage of patients, and different scenarios are provided for hospital selection. The verification of statistical results indicates that the proposed mHealth framework is systematically valid. The contribution of the mHealth framework is presented to provide an improved triage process, afford timely services and treatment for CVD patients and minimise the chances of error. These health sectors and policymakers can also recognise the evaluation benefits of smart hospitals by using the presented framework and move forward to fully automated mHealth applications.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Development of IoT-based mhealth framework for various cases of heart disease patients

et al. 2021

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“…To provide technical support for this important phase in the case of mass casualty incidents (MCI), we have developed a triage app running on Smart Glasses (Recon Jet, Recon Instruments, Vancouver, BC, Canada) within the framework of the project Augmented Disaster Medicine (AUDIME), which was financed by the German Federal Government. The AUDIME project aims to develop new high-tech strategies, including apps for Smart Glasses, for the technical support of an MCI [10]. Data are displayed on a small monitor on the Smart Glasses, the video stream of an integrated camera is used for telemedical support.…”

Section: Introductionmentioning

confidence: 99%

Technical Support by Smart Glasses During a Mass Casualty Incident: A Randomized Controlled Simulation Trial on Technically Assisted Triage and Telemedical App Use in Disaster Medicine

Follmann¹,

Ohligs²,

Hochhausen³

et al. 2019

J Med Internet Res

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Background To treat many patients despite lacking personnel resources, triage is important in disaster medicine. Various triage algorithms help but often are used incorrectly or not at all. One potential problem-solving approach is to support triage with Smart Glasses. Objective In this study, augmented reality was used to display a triage algorithm and telemedicine assistance was enabled to compare the duration and quality of triage with a conventional one. Methods A specific Android app was designed for use with Smart Glasses, which added information in terms of augmented reality with two different methods—through the display of a triage algorithm in data glasses and a telemedical connection to a senior emergency physician realized by the integrated camera. A scenario was created (ie, randomized simulation study) in which 31 paramedics carried out a triage of 12 patients in 3 groups as follows: without technical support (control group), with a triage algorithm display, and with telemedical contact. Results A total of 362 assessments were performed. The accuracy in the control group was only 58%, but the assessments were quicker (on average 16.6 seconds). In contrast, an accuracy of 92% ( P =.04) was achieved when using technical support by displaying the triage algorithm. This triaging took an average of 37.0 seconds. The triage group wearing data glasses and being telemedically connected achieved 90% accuracy ( P =.01) in 35.0 seconds. Conclusions Triage with data glasses required markedly more time. While only a tally was recorded in the control group, Smart Glasses led to digital capture of the triage results, which have many tactical advantages. We expect a high potential in the application of Smart Glasses in disaster scenarios when using telemedicine and augmented reality features to improve the quality of triage.

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“…The aim of the AUDIME project was to develop new high-tech strategies including apps for Smart Glasses for the technical support of a MCI. (Paulus et al, 2016) Data are displayed on a small monitor on the Smart Glasses, the video stream of an integrated camera is used for telemedical support. With simple operating gestures provided on an optical touchpad on the Smart Glasses, menu items can be selected and simple manual entries can be made.…”

Section: Introductionmentioning

confidence: 99%

Technical Support by Smart Glasses During a Mass Casualty Incident: A Randomized Controlled Simulation Trial on Technically Assisted Triage and Telemedical App Use in Disaster Medicine (Preprint)

Follmann¹,

Ohligs²,

Hochhausen³

et al. 2018

Preprint

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AUDIME: Augmented Disaster Medicine

Cited by 5 publications

References 3 publications

Development of IoT-based mhealth framework for various cases of heart disease patients

Development of IoT-based mhealth framework for various cases of heart disease patients

Technical Support by Smart Glasses During a Mass Casualty Incident: A Randomized Controlled Simulation Trial on Technically Assisted Triage and Telemedical App Use in Disaster Medicine

Technical Support by Smart Glasses During a Mass Casualty Incident: A Randomized Controlled Simulation Trial on Technically Assisted Triage and Telemedical App Use in Disaster Medicine (Preprint)

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