Could the Internet of Things Be Used to Enhance Student Nurses’ Experiences in a Disaster Simulation?

Citation:

Laplante, N., Laplante, P. & Voas, J. (Feb 2018). Could the Internet of Things be used to enhance student nurses’ experiences in a disaster simulation?  Online Journal of Nursing Informatics (OJNI), 22(1), Available at http://www.himss.org/ojni

Abstract

Disaster nursing gained interest after the terrorist attacks of September 11, 2001. Schools of nursing recognized the need to prepare future nurses to care for victims, and function as part of the interprofessional team for all stages of disasters. Challenges for nursing education have been to add this content to an already over-packed curriculum and demonstrate the importance to students who are very acute care-focused. Simulation has been one means to do this, with many schools investing much time and energy to create extensive disaster simulations.

A recent undergraduate disaster simulation sparked an interest as to whether the Internet of Things (IoT) could be included to further challenge and engage students. The simulation was well received by students and faculty, and opportunities to collaborate with first responders provided a more realistic experience for students. Upon reflection, the authors wondered if the experience could be further enhanced with the use of technologies, such as the tracking of victims with a different system other than paper triage tags.  

IoT has promised to create many opportunities for enhancing human lives, and although IoT has gained in popularity, nursing has been slow to adopt these technologies, in particular with regard to undergraduate nursing education. The authors previously introduced a structured framework for specifying, designing and implementing healthcare applications in the IoT, and now build on this work for this paper. Disaster simulation could be an opening to introduce IoT in a meaningful way, thus highlighting the application of these technologies for future nurses. 

Introduction

A recent disaster simulation for undergraduate nursing students proved to be a beneficial learning experience. The use of a paper triage tagging system and lack of other technologies, however, prompted thoughts as to whether the addition of Internet of Things (IoT) technologies to the disaster simulation could further enhance the experience. The IoT refers to any computing system that interacts with certain devices via the Internet. Simply stated, it is the concept of connecting any device with an on/off switch to the Internet and/or to each other (Morgan, 2014). This giant network of connecting “things,” estimated to reach 26 billion connected devices by the year 2020, also includes people; therefore, the relationship can be a connection of people-people, people-things, and things-things (Morgan, 2014).  

Disaster nursing has gained renewed attention ever since the events of the terrorist attacks on September 11, 2001. The care of populations affected by disasters requires an interprofessional team approach and specialized knowledge, including the ability to accurately triage and track victims. Disaster nursing content is often taught from a purely theoretical perspective to undergraduate nursing students; however, the use of simulation is gaining popularity. Disaster simulation has been shown to enhance student knowledge of roles and responsibilities in disaster situations, provide an opportunity to work in teams, and increase collaborative efforts with other disciplines (Livingston et al., 2016). The disaster simulation that will be discussed in this paper focused on the impact stage where the disaster had occurred. Nursing students took on various roles, including that of the nurse who responded to the scene, assessed and triaged victims, tracked victims and disseminated information, and met the immediate needs of the community. That experience prompted thoughts as to ways to further develop the simulation and better engage students in future offerings.
Given the scope of IoT technologies, the authors believe this is an area of undergraduate nursing education that has not been fully explored, but one that will impact future students’ clinical practice. Smart hospital systems (SHS) already exist, with the ability to track inpatients and the use of wireless infusion pumps. Due to projected continued growth of IoT technologies, incorporating them into a community setting with the disaster simulation would introduce students to technologies used in the field for triage and tracking victims. These future nurses could be involved in this triage depending on the location of the disaster and/or will care for these victims once they enter the health care system. This paper will draw from previous work regarding IoT health care applications and explore the possible future use of IoT in relation to enhancing a disaster simulation experience for undergraduate nursing students.

Previous and Related IoT Works in Health care

Since 2010 there has been a great deal of research and publication on IoT in health care in general and in nursing, though significantly less on the use of IoT in emergency response. Most of the general research on IoT in health care has related to smart hospitals, remote monitoring, telemedicine, and assisted living. These works are significant as they detail how IoT is already being used, and shed light on areas for further integration.

On the use of IoT in the emergency response domain, there have been even fewer works. Yang, Yang and Plotnick (2013) discussed the use of IoT in the emergency management community. The authors proposed various uses for IoT technology for emergency responders, but did not mention the role of the nurse. Similarly, Liu, Wang, Wan, Xiong, and Zeng (2013) provided a survey of network communications in disaster networks using wearable devices, and highlighted some of the challenges in gaining acceptance for such implementation by the public. Interestingly, they mentioned nurses twice but only as examples of “authorized medical personnel.”

A comprehensive survey of general research on IoT by Islam et al. (2015) explored advances in IoT-based health care technologies and reviewed the state-of-the-art network architectures/platforms, applications, and industrial trends in IoT-based health care solutions. Services identified by Islam and colleagues include community health care, wearable device access, and indirect emergency health care. Although IoT is modernizing health care, the authors reported a lack of research around emergency health care based IoT networks.

With respect to the application of IoT in nursing, Mieronkoski et al. (2017) conducted a comprehensive survey of eight health care/medical databases on nursing and IoT with the intent to introduce IoT to the nursing audience. After retrieving more than 5,000 papers that matched the keyword search, 63 relevant publications were reviewed. Mieronkoski et al. (2017) concluded that “nursing could benefit from a deeper understanding of concepts developed and used by other disciplines” (p. 79) and that the concept of IoT has not currently made its way into nursing research.  
The aforementioned works were general in nature but there has been pioneering work related to using IoT technology to help manage disaster scenarios. For example, Lorincz et al. (2004) developed an RF-based technology called MoteTrack which was used to locate responders and patients within buildings during a disaster. A system, called Code Blue, which dynamically integrated sensors and other wireless devices in a disaster response setting was also developed by Lorincz et al. (2004) at Harvard University.   

Gao et al. (2006) developed a description of a real-time patient monitoring application that “integrates vital signs sensors, location sensors, ad-hoc networking, electronic patient records, and web portal technology to allow remote monitoring of patient status” to facilitate communication at disaster scenes (p. 66). Importantly, it was highlighted that first responders often relied on radio frequencies that could become overcrowded, risking the loss of critical patient information. Based on this issue and others, Gao and colleagues developed technology-based solutions, including electronic triage tags called VitalMote, which built on the CodeBlue wireless sensor network. A test of this technology was completed in collaboration with Johns Hopkins University (White, 2007), which engaged first responders in a disaster simulation. This test highlighted the benefits of the technology over the traditional paper triage tags, and offered encouragement to continue this work. A search for more current applications of VitalMote was unsuccessful, with the related website last being updated in 2007. Still, this work offered encouragement to use similar technologies in the education of nursing students participating in disaster simulations.

Aragüés et al. (2011) surveyed existing wired and wireless technologies for use in health care systems, largely to guide uniform architectural design and interoperability. These works can be used to guide the implementation of any real IoT enabled disaster management system. Chen, Liu, Wang, Dou, Chen, and Li (2013) designed a study to test an early warning system in natural disasters using wireless sensor network (WSN) technology. Emergency response was one element considered in this study, with the researchers exploring issues of data transmission. Much of this study focused on the design; however, a secondary finding focused on the application of WSN beyond tracking of patients, demonstrating another current area of development.

Literature specific to IoT tracking and disaster simulation for undergraduate nursing students could not be located. Most of the literature that explored IoT tracking was written from a technology exploratory focus, leaving the door open for nursing education and others to study the implementation of IoT in disaster simulations. In agreement with Mieronkoski et al. (2017) IoT has been “vaguely adopted” in nursing and nursing science could benefit from “involvement in engineering research in the area of health” (p. 78). This paper is presented from an interprofessional perspective, joining the disciplines of nursing and systems engineering. The authors strongly advocate for this collaboration because all are needed to create safe and reliable technologies.   

Tracking Apps

One cost-effective and simple application of IoT in disaster simulation would be the addition of tracking apps. There are hundreds of free and for purchase applications (apps) on the market that can be used for tracking victims. Some applications track according to a triage system, which is used to categorize victims based on their medical needs, while others can monitor traffic patterns during a disaster. A recent systematic review by Bachmann, Jamison, Martin, Delgado, & Kman (2015) initially uncovered 683 applications which was narrowed to 219 based on relevance for further review. The results highlighted the most useful applications, including an app from the American Red Cross for natural disasters, The Centers for Disease Control app for first responders, and the National Library of Medicine’s Wireless Information System for Emergency Responders (WISER) app, which has been an excellent app for HazMat responders.

The FEMA (2017) mobile app (named as such) has many features, including disaster safety tips, an interactive emergency kit list, storable emergency meeting locations, and a map with open shelters. Also reviewed by Bachman et al. (2015) was the “911 Toolkit for Emergency Responders,” a free app geared mostly towards firefighters, but also useful for paramedics, HazMat teams, and other emergency responders. The researchers of this systematic review provided information on many more apps, including those for the START triage method, social networking to recruit volunteers (“Team Red Cross”), and specific types of disasters including radiologic, blast injuries (e.g. bombings), and natural disasters (hurricanes, fires, earthquakes, etc.). Although this was an extensive systematic review, the researchers stressed how quickly these apps change and new ones are added.  
There was some discussion in this review of apps linking, such as what can be done from the FEMA mobile app. For example, within the FEMA app, there are numerous features that can be accessed: alerts from the National Weather Service for up to five locations, disaster reporter allowing for uploading of photos, maps of disaster resources, the ability to apply for disaster assistance (access DisasterAssistance.gov), custom emergency safety information, and safety tips (FEMA, 2017). The user of this app could link to these features by downloading this one single app, thus accessing and sharing a large amount of information. A future consideration remains on how to link multiple apps for even greater sharing of information and better disaster management. Clearly, this is a field of rapid growth and change, and no one review could be considered an exhaustive list; however, Bachmann et al. (2015) provided a review of the current most useful apps.

The Disaster Simulation

The disaster simulation for senior undergraduate nursing students recently implemented was part of a course requirement for a population health nursing course. The goal was to give students experience in disaster response and management, with students taking on various roles. The roles included: nurse, family member, victim, bystander, and observer. Similar to the experience reported by Livingston et al. (2016), the senior students had the opportunity for interprofessional collaboration in this simulation working with first responders.
The simulation took place outdoors on a metropolitan university campus, during a peak time of day. University members were alerted prior to the simulation as to what was happening, to avoid panic and confusion from bystanders. The simulation began around 9 a.m., and the campus was busy as students, faculty, and staff moved around to classes and other events. The scenario unfolded over the course of four hours, beginning with preparation of “victims,” an overview of triage for students, and assignment of additional roles. Faculty were observers throughout the simulation. Triage of victims began, using a paper tagging system.

The Disaster Scenario

There were about 50 people on the pedestrian walkways adjacent to the University’s School of Nursing. Suddenly, there was a loud screeching sound coming towards them. A large dump truck had gone out of control, and was headed directly into pedestrian traffic. The truck was carrying debris of wooden pallets and loose bricks from a nearby construction site.  

The scene quickly became chaotic as numerous people were struck by the truck, and others were hit by debris that had been launched from the truck upon impact.  The driver of the truck was disoriented and was grabbing his chest, yelling that he was in pain. His co-worker passenger was ejected from the truck upon impact, and was lying motionless on the ground near the truck.  Several other University students, faculty, and staff were also struck by debris, and others appeared to be in shock as to what they had seen.   

Faculty Reflection on the Experience

For a first-time experience, the simulation progressed well. Faculty observed that not all students actively engaged, thus they needed to consider this in assignment of roles in the future. This disaster scenario allowed for multiple roles and varying degrees of injuries. Faculty experienced in moulage techniques had prepared “victims” with bleeding wounds and injured limbs. The victims were set in place outdoors, and then the remainder of the class came on the scene, in their assigned roles, as the simulation started. The students were aware that a disaster had occurred but were not told in advance of the types of injuries to expect. As the simulation unfolded it was interesting to watch the “nurses” attempt to triage “victims.” Some victims had obvious wounds while others did not, requiring more advanced communication techniques. Many nurses were drawn to the most critical victims such as the truck passenger and avoided victims that did not have obvious wounds.

Local first responders participated in this simulation, so once they arrived the nurses worked with them. As is the case in real life, the first responders assumed command of the situation, and the students were informed that this would be the case, making them ready to work together. Paper triage tags were used in this simulation because that is what the first responders used in real life. Paper tags were observed lying on the ground at times, away from “victims,” therefore questioning whether the triage was accurate or complete and if accurate tracking would occur.
Student responses to the simulation experience were positive; anecdotally students expressed gratitude in the debrief session as they believed this experience will be of value in future practice. Ideas to improve the simulation were offered during the debrief session, such as a longer triage introduction before beginning, and addition of counselor roles. The “bystanders” felt anxious at times because they observed “nurses” were not doing what they would have if they were the “nurse”, and “nurses” found it hard to decide at times on a triage tag. Several “nurses” acknowledged that they left the tags after the initial assessment, and did not pay attention to assuring the tag moved with the “victim.”

Once “victims” were triaged they were transferred into ambulances on the scene, and driven away to another location on campus, simulating that the victim was transported to the hospital. Given the number of people involved in this simulation, it was often hard to remember who had been transported; if IoT tracking had been used, could there have been better oversight? If an app such as the FEMA app was used by all participants, could the app links provide better data collection and sharing of information? These were thoughts that occurred while listening to the debrief session.  

A Systems Model for the Application of IoT Technologies in a Simulation Scenario

Engineers use different techniques for modeling systems, including health care systems. The authors previously introduced one simple modeling technique for specifying, designing and implementing health care applications in the IoT (Laplante & Laplante, 2015; Laplante, Laplante, & Voas, 2016). The technique employed Rich Pictures and Use Case Diagrams to help in identifying stakeholders and elicit requirements for IoT health care systems. Rich Pictures are simple cartoon-like pictures that represent the various stakeholders in a system and their interactions. Likewise, Use Case Diagrams are simple drawings that show a slightly more detailed level of stakeholder interaction with the system environment. The authors’ elaboration approach defined an IoT health care system based on the health care settings of acute care, long-term care or community-based care, and systems types in which the IoT tracks people, things, or both (Laplante, Laplante, & Voas, 2016).

The disaster simulation described earlier in this paper unfolded quickly, with nursing students assigned to take on numerous roles. For a first offering the simulation proceeded smoothly; however, as with any experience, there is always room for improvement. To better prepare the students for future experiences and help identify the set of people, the stakeholders, who would be involved in the disaster management system and to define the system boundary, Rich Picture and Use Case Diagrams could be created. The diagrams should show direct and indirect interactions with other entities. These high-level systems models would be more useful when domain terminology was accurately defined, that required robust interaction between the systems engineers and the relevant stakeholders, such as first responders, nurses, and administrators. A Rich Picture diagram could be useful (Laplante, Laplante, & Voas, 2016) to show the most relevant stakeholders for the disaster simulation, along with their representative concerns (see Fig. 1). Rich Pictures can be a useful tool for nurse faculty in disaster simulations, as the pictures provide an intuitive, visual representation of the situation, stakeholders, and their interactions, and require no technical expertise or training to use. These visual presentations in Rich Pictures can assist students in understanding the complexity of communication necessary in disaster management from an interprofessional perspective as they will be able to visualize the multiple interactions that they will be involved in.

 Fig. 1: Rich Picture for Disaster Scenario

From an interprofessional perspective, the value of the Rich Picture would be to ensure that no important stakeholders were excluded from the discussions during system design and implementation, and to maintain correct interaction with entities outside the system (Laplante, Laplante, & Voas, 2016). A set of Use Cases formed the basis for the behavioral specification of the system, and were then developed from the fully elaborated Rich Picture.

A Use Case is an example of how a system is used under different operational profiles. Use Cases have often been depicted via Use Case Diagrams, which employ stick figure actors and bubbles describing the particular actions involving those actors as indicted by directed arrows. A Use Case for the disaster situation in which a nurse triaged a victim, decided where the victim should be sent, and then applied a colored barcode band on the victim and scanned the band into the system is depicted in Figure 2. The nurse could be the first on the scene to begin triaging, or this could be the first responders; in Figure 2 the role of the nurse is highlighted because of the focus on the undergraduate nursing disaster simulation experience.

Fig. 2. A Particular Use Case Diagram for Disaster Scenario

The system now allowed the nurse to track the location of the victim (assuming that the victim’s band is swiped at strategic points during their movement) and to communicate the location to family members and other staff members. Radio frequency identification (RFID) is technology that uses radio waves to automatically identify people or objects and has been widely used in IoT and already in use in many health care organizations. In a disaster situation, RFID technology would allow for more precise geolocation of victims, assuming that RFID readers were located at strategic locations. There would be concerns, however, about electromagnetic interference from the RFID technology and potential destruction of the RFID device and injury to the patient when imaging scans were taken in the hospital. A simple wristband with bar code system could be a safer tracking solution for these reasons.

Bystanders were shown in Figure 2 for they may also communicate with family and others about the location of a victim (if known). In a complex scenario such as a disaster, this iterative process of elaborating Use Cases will need to be repeated to involve one or more stakeholders from each group and uses shown in the Rich Picture of Fig. 1. This process would continue until all perspectives and concerns are heard from all stakeholders and interactions with other entities, such as a database or specialized emergency response equipment, identified.

Depicting the IoT System

Real problems could occur when designing complex systems that bridge multiple disciplines with different prevailing standards, including fire protection, electro-magnetic compatibility, and medical device safety. Such a situation is termed “standards confusion” by engineers (Voas & Laplante, 2007). Therefore, from an interprofessional perspective, there would be value for nurses and other health care providers to collaborate with systems engineering professionals in the design and implementation of these systems. The U.S. .National Institute of Standards and Technology (NIST) DRAFT Special Publication provided a template for IoT implementations (Voas, 2016) that can be applied to the disaster simulation experience, providing a common language for IoT health care systems stakeholders from all disciplines. The NIST SP 800-183 template included the following:

  1. Sensor:  an electronic utility that digitally measures physical properties and outputs raw data. For example, in a disaster response scenario, sensors could be simple barcode tags to identify patients, or more sophisticated devices such as those to measure blood pressure, pulse-oximetry, or location via GPS system.
  2. Aggregator: a software implementation based on mathematical function(s) that transforms/consolidates groups of raw data into intermediate data for transmission. In the disaster response, the barcode reading device would be the aggregator if only passive barcode sensors were being used. However, if victims were connected to more sophisticated sensors, then another type of reading device would be needed to collect this information from victims.
  3. Communication channel: a medium by which the data is communicated between sensors, aggregators, communication channels, decision triggers, or eUtility. In a disaster response setting, for example, there could be a Wi-Fi network of sensors/clusters/aggregator or a barcode beacon to a reader device.
  4. eUtility (external utility): a software or hardware product or service, providing computing power that aggregators will likely need in the IoT. In the disaster response utility, there could be software running on a tablet computer or command center computer that would accept the flow data from the aggregators and help decision makers. Other eUtilities could be accessed from remote computing sites, such as from FEMA.
  5. Decision trigger: creates the final result(s) needed to satisfy the purpose, specification, and requirements of a specific IoT (Laplante, Voas, & Laplante, 2016). In a disaster response scenario, the aggregator function could simply determine the patient location, or could make decisions about the patient’s status based on vital signs.

The examples given above are summarized for future use in a disaster simulation and suggestions for implementation are presented in Table I.

Table I: Partial Model Realization

Model Factor

Application to Simulation

SensorWi-Fi enabled bar code scanner; blood pressure sensor; pulse-ox sensor; GPS locator
AggregatorBarcode reader; blood pressure, pulse-ox sensor reading device; GPS reader.
Communication channelWi-Fi___33 network of sensors/clusters/aggregator, barcode beacon to reader.
eUtilityOnsite – tablet based software or software on board the command center computer; Remote – computing resources from remote sources, such as FEMA.
Decision triggerGeolocate victim; indicate patient in distress.

There are other aspects of describing an IoT system found in SP 800-183, which played a major role in enhancing the trustworthy interoperability built from any IoT components, services and commercial products (Laplante, Voas, & Laplante, 2016). The basic IoT elements described in NIST publication 800-183 can be used to reconcile conflicting standards, providing an application of this advanced technology to the undergraduate nursing disaster simulation experience.  Not all suggestions may be feasible to include, based on availability and cost; however, as the simulation is planned, nurse faculty may look for opportunities for grant funding or other external means to assist in purchasing these technologies. Nurse faculty could also collaborate with their engineering faculty colleagues within their universities to better design and integrate IoT technologies in the disaster simulation.

Future Implications

As mentioned previously, the paper triage tags were left on the ground in many instances, which was not an efficient use of resources, and a potential safety issue for patients. IoT tracking using barcode scanning could be added to a future disaster simulation to address some of these issues. This equipment would include colored barcoded ID tags for triage categories, WiFi enabled readers, and an appropriate information management system that would make it possible to identify victims at the disaster scene, those leaving the scene on their own, often referred to as the walking wounded, or those being taken away in ambulances.

Ultimately, the goal would be faster identification or location of the victims and more effective triage of patients and allocation of resources (Laplante, Voas, & Laplante, 2016). The addition of IoT technologies, such as disaster apps, could further engage students in the simulation, as most are already tech savvy and use some form of smart device on a daily basis. Students will be exposed to more IoT technologies as they begin professional practice; therefore, an introduction at the undergraduate level would be beneficial. IoT tracking devices already exist to monitor traffic in disaster situations, along with the numerous management and response apps discussed at the beginning of this paper. The use of these apps in a future simulation in collaboration with first responders could provide a means for more efficient tracking and enhanced communication for all parties.
The students in the reported simulation played several of the roles depicted earlier in the Rich Picture. For future simulations, the Rich Pictures could be included in the triage introduction as a teaching tool. Possible future scenarios could include a mass shooting on campus, a multi-vehicle crash, airplane crash on campus, and others. Upon completion of the simulation, faculty would continue to debrief students as is the norm in simulation experiences. Part of the debrief could include questions about student experience using IoT in the simulation, including ideas for future use in disaster nursing. Research could focus on comparison of tracking outcomes with and without IoT technologies. Expected outcomes related to IoT would be a better understanding of the issues surrounding victim tagging, tracking, potential for improvement of victim/patient outcomes, enhanced interprofessional communication, and responder/victim dynamics. Students would also experience an interprofessional experience as they were introduced to IoT technologies and their use in health care applications and possibly partnering with the engineering students as well as first responders.

Discussion

As Islam et al. (2015) reported, numerous IoT health care applications are already being used in smart health care systems, including glucose level sensing and blood pressure monitoring that is linked to mobile phones. Nursing students may already be working with some of these technologies, depending upon the clinical agencies they are placed in. However, the addition of IoT in a community setting, such as in disaster response, would be a unique educational experience.

We proposed a basic use of IoT for tracking victims of disasters using barcodes, as this could be the first time that undergraduate nursing students would be exposed to IoT technology in a nursing experience. We suggested the possibility of using tracking apps and offered encouragement for an interprofessional experience for the integration of IoT technologies.  Exposing students to these technologies, even simple ones, through a real-life experience, would be beneficial as they will work with more complex technologies later on. As soon to be registered nurses, these senior-level students enter a health care practice that is reliant on technology; therefore, this experience gives them a glimpse into their future as health care practitioners.

The benefits of an IoT disaster management system will require effective and reliable interoperability of all of the IoT systems involved, including the victims’ personal trackable devices, such as phones or IoT enabled wearables. Other devices in range such as vehicles, businesses, and smart buildings could also interact with the medical and emergency equipment. These devices could be helpful to rapidly obtain a victim’s medical history, or problematic by triggering a security response that could block signals (Laplante, Voas, & Laplante, 2016). It is important to collaborate with first responders and other professionals in this simulation to learn what is already being used and assure there is no interference of different apps or technologies. Experts in IoT technology must also be consulted so that realistic application and integration can take place. The many apps that already exist would be a place to begin, adding IoT tracking to future disaster simulations could provide valuable information for improving the care of victims, and support for families and the health care system as a whole.  

Tracking of supplies and equipment in a disaster situation could also be of significant benefit, especially if these were linked to victim tracking.  In this case, RFID tags or active signal technologies could be employed, further allowing for more precise location of the victims. The simulation described here could easily be expanded to include active technologies, tracking things, exploiting existing technologies such as cell phones and other wearables held by victims, bystanders and responders.

Conclusions

The possibilities for application of IoT in healthcare are endless. Registered nurses account for the highest percentage of employment of healthcare occupations (Bureau of Labor Statistics, 2015); therefore, it is important to ensure nurses are exposed to these technologies that will continue to be part of their professional practice. As Morgan (2014) suggested, conversations about IoT are occurring to explore how these technologies will impact our lives. Nursing education needs to catch up to these conversations and seek ways to add IoT to educational experiences to prepare the next generation of nurses who will use these technologies and be involved in their ongoing development and application.

Disclaimer

Certain commercial entities, equipment, or materials may be identified in this document to describe an experimental procedure or concept adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose.

References

Aragüés, A., Escayola, J., Martinez, I., dell Valle, P., Munoz, P., Trigo, J.D., & Garcia, J. (2011). Trends and challenges of the emerging technologies toward interoperability and standardization in e-health communications,” IEEE Communications Magazine, 49(11), 182-188.

Bachmann, D.J., Jamison, N.K., Martin, A., Delgado, J., & Kman, N.E. (2015). Emergency preparedness and disaster response: There’s an app for that. Prehospital and Disaster Medicine. 30(5), 1-5.

Bureau of Labor Statistics. (2015). Registered nurses have highest employment in healthcare       occupations. Retrieved from: https://www.bls.gov/opub/ted/2015/registered-nurses-have-           highest-employment-in-healthcare-occupations-anesthesiologists-earn-the-most.htm

Chen, D., Liu, Z., Wang, L., Dou, M., Chen, J, & Li, H. (2013). Natural disaster monitoring with wireless sensor networks: A case study of data-intensive applications upon low-cost scalable systems. Mobile Networks and Applications, 18(5), 651–663. DOI 10.1007/s11036-013-0456-9
FEMA (2017). Mobile app. Retrieved from: https://www.fema.gov/mobile-app

Gao, T., Hauenstein, L.K., Alm, A., Crawford, D., Sims, C.K., Husain, A., & White, D.M. (2006). Vital signs monitoring and patient tracking over a wireless network.  Johns Hopkins APL Technical Digest 27(1), 66-74.

Islam, S.M.R., Kwak, D., Kabir, M.D.H, Hossain, M., Kwak, K.S. Islam, S. R., Kwak, D., Kabir, M. H., Hossain, M., & Kwak, K. S. (2015). The internet of things for health care: a comprehensive survey. IEEE Access3, 678-708

Laplante, N. L, Laplante, P.A., & Voas, J.M. (2016). Stakeholder identification and use case   representation for Internet-of-Things applications in health care. IEEE Systems Journal, PP(99), 1-10.

Laplante, P. A. and N. L. Laplante (2015). A Structured approach for describing health care applications for the Internet of Things. 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), 621-625.

Laplante, P. A., Voas J. M. and N. L. Laplante (2016). Standards for the Internet of Things: A case study in disaster response. Computer, 49(5), 87-90.

Liu, J. Wang, Q., Wan, J., Xiong, J., & Zeng, B. (2013). Towards key issues of disaster aid based on wireless body area networks. Transactions on Internet and Information Systems, 7 (5), 1014-1035.

Livingston, L. L., West, C. A., Livingston, J. L., Landry, K. A., Watzak, B. C., & Graham, L. L. (2016). Simulated Disaster Day: Benefit from Lessons Learned Through Years of Transformation from Silos to Interprofessional Education. Simulation in Health care, 11(4), 293–298. doi: 10.1097/SIH.0000000000000173

Lorincz, K., Malan, D.J., Fulford-Jones T. R.F., Nawoj, A., Clavel, A., Shnayder, V., Mainland, G., & Welsh, M. (2004). Sensor networks for emergency response: Challenges and opportunities. Pervasive Computing, 3(4),16-23.

Mieronkoski, R., Azimi, I., Rahmani, A. M., Aantaa, R., Terävä, V., Liljeberg, P., & Salanterä, S. (2017). The internet of things for basic nursing care: A scoping review. International Journal of Nursing Studies, 69, 78-90. doi: 10.1016/j.ijnurstu.2017.01.009

Morgan, J. (2014). A simple explanation of 'The Internet of Things'. Forbes Magazine, May 13. Retrieved from https://www.forbes.com/sites/jacobmorgan/2014/05/13/simple-explanation-internet-things-that-anyone-can-understand/#30d1e7c01d09

Voas, J. (2016). Networks of ‘things’. NIST DRAFT Special Publication 800, 183

Voas, J.M., & Laplante, P.A. (2007). Standards confusion and harmonization, Computer, 40(7), 94-96.

White, D. (2007). Advanced health and disaster aid network final report. Johns Hopkins University. Retrieved from: http://www.jhuapl.edu/AID-N/Pub/AID-N_Final_Report_v_0_7__091807.pdf

Yang, L., Yang, S.H., & Plotnick, L. (2013). How the internet of things technology enhances emergency response operations. Technological Forecasting and Social Change, 80(9), 1854-1867.

Author Bios

Nancy L. Laplante, Ph.D., R.N., AHN-BC is an associate professor of nursing at Widener University, Chester, Pa. She is the coordinator for bachelor and master of science in nursing (BSN, MSN) program options and the director of online programs for the School of Nursing. She earned her BSN from William Paterson University, her MSN from West Chester University, and a Ph.D. in nursing education from Widener University. She is board certified in advanced holistic nursing and teaches nursing courses across the curriculum, with a focus on health policy, technology in nursing, gerontology and holistic self-care. Laplante is a regular contributor to nursing textbooks, an associate editor for the Journal of Holistic Nursing, and has published in the areas of holistic nursing education, IoT, and service-learning.

Phillip A. Laplante, B.S., M.Eng, MBA, Ph.D., PE is a professor of software and systems engineering at Penn State University, Malvern, Pa. He received his B.S., M.Eng., and Ph.D. from Stevens Institute of Technology and an MBA from the University of Colorado. He is a fellow of the Institute of Electrical and Electronics Engineers (IEEE) and SPIE and has won international awards for his teaching, research and service. Since 2010, he has led the effort to develop a national licensing exam for software engineers. He has worked in avionics, computer-assisted design, and software testing systems and he has published 27 books and more than 200 scholarly papers. Laplante is a licensed professional engineer in the Commonwealth of Pennsylvania and a certified software development professional.  

 

Jeffrey M. Voas, B.S., M.S., Ph.D., is a computer scientist at the National Institute of Standards and Technology (NIST), Gaithersburg, Md. Before joining NIST, he was an entrepreneur and co-founded Cigital: www.cigital.com. Voas received his undergraduate degree in computer engineering from Tulane University (1985), and received his M.S. and Ph.D. in computer science from the College of William and Mary (1986, 1990 respectively). He received two U.S. patents and has over 200 publications. He is a fellow of the Institute of Electrical and Electronics Engineers (IEEE), the Institution of Engineering and Technology (IET), and the American Association for the Advancement of Science (AAAS). He received the U.S. Department of Commerce’s gold medal in 2014 for his efforts in vetting apps for smartphones for U.S. soldiers in Middle East conflicts.