SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest

Rao, Gaurav; Choudhury, Salimur; Lingras, Pawan; Savage, David; Mago, Vijay

doi:10.1186/s12911-020-01334-4

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…The selection of the nearest AED can be performed either by the user manually or by using the responder network system that has been developed earlier by the authors [ 61 ]. By providing only the AED location, the user may have to spend significant time finding the AED within the premises.…”

Section: Discussionmentioning

confidence: 99%

AEDNav: indoor navigation for locating automated external defibrillator

Rao

Mago

Lingras

et al. 2022

BMC Med Inform Decis Mak

Self Cite

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Background In a sudden cardiac arrest, starting CPR and applying an AED immediately are the two highest resuscitation priorities. Many existing mobile applications have been developed to assist users in locating a nearby AED. However, these applications do not provide indoor navigation to the AED location. The time required to locate an AED inside a building due to a lack of indoor navigation systems will reduce the patient’s chance of survival. The existing indoor navigation solutions either require special hardware, a large dataset or a significant amount of initial work. These requirements make these systems not viable for implementation on a large-scale. Methods The proposed system collects Wi-Fi information from the existing devices and the path’s magnetic information using a smartphone to guide the user from a starting point to an AED. The information collected is processed using four techniques: turn detection method, Magnetic data pattern matching method, Wi-Fi fingerprinting method and Closest Wi-Fi location method to estimate user location. The user location estimations from all four techniques are further processed to determine the user’s location on the path, which is then used to guide the user to the AED location. Results The four techniques used in the proposed system Turn detection, Magnetic data pattern matching, Closest Wi-Fi location and Wi-Fi fingerprinting can individually achieve the accuracy of 80% with the error distance ± 9.4 m, ± 2.4 m, ± 4.6 m, and ± 4.6 m respectively. These four techniques, applied individually, may not always provide stable results. Combining these techniques results in a robust system with an overall accuracy of 80% with an error distance of ± 2.74 m. In comparison, the proposed system’s accuracy is higher than the existing systems that use Wi-Fi and magnetic data. Conclusion This research proposes a novel approach that requires no special hardware, large scale data or significant initial work to provide indoor navigation. The proposed system AEDNav can achieve an accuracy similar to the existing indoor navigation systems. Implementing this indoor navigation system could reduce the time to locate an AED and ultimately increase patient survival during sudden cardiac arrest.

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Section: Discussionmentioning

confidence: 99%

AEDNav: indoor navigation for locating automated external defibrillator

Rao

Mago

Lingras

et al. 2022

BMC Med Inform Decis Mak

Self Cite

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“…• The smart device sends an alert to its service provider and then the service provider calls the emergency services 26,27 .…”

Section: Ohca Workflowmentioning

confidence: 99%

“…The emergency resources activated for the patient depend on the services available at the emergency center. The services include: 1) dispatching emergency medical services, 2) dispatching fire and police units, and 3) activating a Responder Network System (RNS) 28,26 . RNS is a notification system that identifies registered users near the emergency location and alerts them to assist the patient.…”

Section: Ohca Workflowmentioning

confidence: 99%

“…While one of the bystanders or responders is performing CPR, others assist in bringing a nearby AED to apply on the patient. There are currently multiple methods that can assist in finding the AED, such as mobile application or dispatcher assistance systems 26,31,27 .…”

Section: Ohca Workflowmentioning

confidence: 99%

“…When sending alerts to the users, RNS can find the best match among the responders, AEDs and the patient's location. The system can then calculate an optimal route for the bystanders to travel to access the AED and then proceed to the patient to minimize the response time 21,31,26 . This automated system of alerting nearby users, finding an AED and matching it with users can help save time for both the caller and the dispatcher.…”

Section: Finding Aedmentioning

confidence: 99%

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A survey on technologies used during Out of Hospital Cardiac Arrest

Rao¹,

Savage

Mago

et al. 2022

Preprint

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Background: Out of hospital cardiac arrest (OHCA) causes close to 400,000 deaths every year in North America, and it is also a leading cause of death among young athletes.OHCA is a treatable medical condition, and the patient's survival chances can be increased if immediate treatment is provided to the patient. However, non-treatment of the patient leads to a dramatic decline in survival chances at 10% per minute. Currently, various technologies are being used, and many more are being researched to reduce the time to provide early treatment to the patient.Objective: This survey focuses on summarizing various available technologies for use during OHCA. This survey focuses on evaluating technologies used in each step of the OHCA process.Methods: In this survey, articles were searched using the term "ohca" on Google Scholar and more than 18,000 articles were found. The articles were further filtered using keywords for each stage of the OHCA process (2,128). For each step, articles were filtered again using author developed method to select articles relevant to each technology for each step of the OHCA process (339). Finally, articles were manually filtered by authors based on their relevance and 112 articles were used in this survey.Results: The technologies that exist today work independently and are not linked with the other steps of the OHCA process. Integration between these technologies could help in reducing time and increase the survival chances of the patient. Also, if it is found that some of the proposed solutions are experimental in nature and not meant to be used in the real-world OHCA scenarios.

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Informatics and machine learning methods for health applications

Shen

Shi

Zhao

et al. 2020

BMC Med Inform Decis Mak

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The 2020 International Conference on Intelligent Biology and Medicine (ICIBM 2020) provided a multidisciplinary forum for computational scientists and experimental biologists to share recent advances on all aspects of intelligent computing, informatics and data science in biology and medicine. ICIBM 2020 was held as a virtual conference on August 9–10, 2020, including four live sessions with forty-one oral presentations over video conferencing. In this special issue, ten high-quality manuscripts were selected after peer-review from seventy-five submissions to represent the medical informatics and decision making aspect of the conference. In this editorial, we briefly summarize these ten selected manuscripts.

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SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest

Cited by 4 publications

References 13 publications

AEDNav: indoor navigation for locating automated external defibrillator

AEDNav: indoor navigation for locating automated external defibrillator

A survey on technologies used during Out of Hospital Cardiac Arrest

Informatics and machine learning methods for health applications

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