A Pilot Study of CPR Quality Comparing an Augmented Reality Application vs. a Standard Audio-Visual Feedback Manikin

Enhancing Cardiopulmonary Resuscitation Training with Mixed Reality: Improving Cardiopulmonary Resuscitation Performance and Enjoyment

Cardiac arrests stand as a leading cause of mortality worldwide. When performed timely, cardiopulmonary resuscitation (CPR) can significantly improve a person's chance of survival during a cardiac arrest. Given that the majority of cardiac arrests happen outside of hospitals, it becomes crucial to equip as many laypeople as possible with CPR skills. Recently, mixed reality has garnered attention as a potential tool for CPR training. This study, with a randomized controlled trial (RCT), tested the effectiveness of a mixed reality CPR training compared to traditional training among laypeople (N = 59). Results revealed that participants in the mixed reality training either showed similar (i.e., compressions per minute, exam scores) or better (i.e., compression depth) CPR performance compared to participants that received the traditional training. Furthermore, the mixed reality training was perceived as more enjoyable than the traditional training. Finally, across conditions, participants reported comparable levels of presence, indicating a similar sense of being in a CPR situation. Based on these findings, we conclude that mixed reality CPR training can serve as a viable alternative for traditional CPR training. Especially, the enjoyable nature of mixed reality can boost motivation and encourage more people to follow or refresh previous CPR training.

Use of augmented and virtual reality in resuscitation training: A systematic review

Objectives

To evaluate the effectiveness of augmented reality (AR) and virtual reality (VR), compared with other instructional methods, for basic and advanced life support training.

Methods

This systematic review was part of the continuous evidence evaluation process of the International Liaison Committee on Resuscitation (ILCOR) and reported based on the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines and registered with PROSPERO (CRD42023376751). MEDLINE, EMBASE, and SCOPUS were searched from inception to January 16, 2024. We included all published studies comparing virtual or augmented reality to other methods of resuscitation training evaluating knowledge acquisition and retention, skills acquisition and retention, skill performance in real resuscitation, willingness to help, bystander CPR rate, and patients' survival.

Results

Our initial literature search identified 1807 citations. After removing duplicates, reviewing the titles and abstracts of the remaining 1301 articles, full text review of 74 articles and searching references lists of relevant articles, 19 studies were identified for analysis. AR was used in 4 studies to provide real-time feedback during CPR, demonstrating improved CPR performance compared to groups trained with no feedback, but no difference when compared to other sources of CPR feedback. VR use in resuscitation training was explored in 15 studies, with the majority of studies that assessed CPR skills favoring other interventions over VR, or showing no difference between groups.

Conclusion

Augmented and virtual reality can be used to support resuscitation training of lay people and healthcare professionals, however current evidence does not clearly demonstrate a consistent benefit when compared to other methods of training.

A Comparison of Virtual Reality to Traditional Simulation in Health Professions Education: A Systematic Review

ABSTRACT

With the increasing availability of virtual reality (VR) and its lower overall costs of use, the objective of this review was to compare VR to traditional simulation in terms of learning outcomes. Studies were included if they met the following criteria: ( a ) research study (of any design), ( b ) focused on learners in health professions, and ( c ) compared VR with traditional simulation. Studies were excluded for the following reasons: ( a ) not a research study, ( b ) focused on learners outside health professions, ( c ) used screen-based or computer-based simulation, ( d ) used a task trainer, and ( e ) did not involve a comparison of VR to traditional simulation. The searches were run on November 11 and 12, 2021, in CINAHL via EBSCO, Ovid Embase, ERIC via EBSCO, IEEE Xplore, Ovid Medline, Ovid PsycINFO, Scopus, and Web of Science Core Collection. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the review. A team of researchers applied Kirkpatrick's Levels, Melnyk's Levels of Evidence, and Critical Appraisal Skills Programme guidelines to assess the level of evidence and look for bias. Fifteen studies were reviewed including 11 randomized controlled trials. The lead researcher synthesized the study results into 3 categories: (1) traditional simulation performed better, (2) VR performed better, and (3) comparable outcomes. There is insufficient evidence to endorse one form of simulation (VR or traditional) as more effective at this time. The body of evidence contained too few studies to draw meaningful conclusions to answer the guiding question. The studies covered a large range of modalities, learner groups, and healthcare topics, preventing a meta-analysis. Based on the literature and experience, we recommend that VR experiences be proctored, include debriefing, have a backup plan for cybersickness or myopia, and have time and costs documented. Use of VR is likely to expand; thus, research is needed to inform the best contexts and applications.

Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review

PURPOSE OF REVIEW

This literature review aims to provide a comprehensive overview of the recent advances in prediction models and the deployment of AI and ML in the prediction of cardiopulmonary resuscitation (CPR) success. The objectives are to understand the role of AI and ML in healthcare, specifically in medical diagnosis, statistics, and precision medicine, and to explore their applications in predicting and managing sudden cardiac arrest outcomes, especially in the context of prehospital emergency care.

RECENT FINDINGS

The role of AI and ML in healthcare is expanding, with applications evident in medical diagnosis, statistics, and precision medicine. Deep learning is gaining prominence in radiomics and population health for disease risk prediction. There's a significant focus on the integration of AI and ML in prehospital emergency care, particularly in using ML algorithms for predicting outcomes in COVID-19 patients and enhancing the recognition of out-of-hospital cardiac arrest (OHCA). Furthermore, the combination of AI with automated external defibrillators (AEDs) shows potential in better detecting shockable rhythms during cardiac arrest incidents. AI and ML hold immense promise in revolutionizing the prediction and management of sudden cardiac arrest, hinting at improved survival rates and more efficient healthcare interventions in the future. Sudden cardiac arrest (SCA) continues to be a major global cause of death, with survival rates remaining low despite advanced first responder systems. The ongoing challenge is the prediction and prevention of SCA. However, with the rise in the adoption of AI and ML tools in clinical electrophysiology in recent times, there is optimism about addressing these challenges more effectively.

[Emergencies-Infinite worlds? : Extended reality as a medium in the education, continuing and advanced training in emergency medicine]

BACKGROUND

Simulation training is indispensable in emergency medicine, especially for enhancing patient safety. Methods and technologies used include a wide spectrum ranging from simple skill trainers to complex full-scale simulated environments integrating standardized patient actors. Limitations include the simulation of dynamic changes of clinical symptoms, the depiction of emotions and patient movements as well as complex environments, such as lively traffic. Extended reality (XR) holds the potential to overcome these limitations.

METHODS/AIMS

Starting with the technological basis and the didactic considerations in the field of XR, the paper reflects the potentials and limitations of this new technology in the domain of medical simulation training. Further focus is put on the integration of XR into existing training curricula.

RESULTS

The XR covers various technologies, ranging from PC-based applications that are similar to conventional computer games, over virtual realities enabling spatially freely navigable 3‑dimensional simulation (using closed 3D glasses: head mounted displays, HMD), to mixed-reality applications that combine virtual elements and real physical objects; however, technology alone does not stimulate learning. As with other simulation methods, it is crucial with XR to implement learning objectives, methods and technologies in a suitable teaching-learning arrangement and to familiarize teachers and students with the new technology. Evidence in the literature with respect to learning success is limited by the heterogeneity of technologies, target groups, teaching-learning arrangements and learning outcomes. Overall, significant increases can be shown for the intrinsic motivation of learners, and for high emotional participation (measured as perceived presence in the virtual environment).

DISCUSSION

Technological developments and the increasing use of digital media in emergency medical education and training favor the leap from XR-based pure demonstration projects to educational practice. Decisive for the educational success are the clear orientation towards concrete learning goals and a thorough familiarization with the new technology.

CONCLUSION

Simulation training based on XR expands the spectrum of existing simulation methods to integrate new dimensions of learning objectives. Further research on the effectiveness of this method is needed.

Pediatric Chest Compression Improvement Via Augmented Reality Cardiopulmonary Resuscitation Feedback in Community General Emergency Departments: A Mixed-Methods Simulation-Based Pilot Study

BACKGROUND

Yearly, more than 20,000 children experience a cardiac arrest. High-quality pediatric cardiopulmonary resuscitation (CPR) is generally challenging for community hospital teams, where pediatric cardiac arrest is infrequent. Current feedback systems are insufficient. Therefore, we developed an augmented reality (AR) CPR feedback system for use in many settings.

OBJECTIVE

We aimed to evaluate whether AR-CPR improves chest compression (CC) performance in non-pediatric-specialized community emergency departments (EDs).

METHODS

We performed an unblinded, randomized, crossover simulation-based study. A convenience sample of community ED nonpediatric nurses and technicians were included. Each participant performed three 2-min cycles of CC during a simulated pediatric cardiac arrest. Participants were randomized to use AR-CPR in one of three CC cycles. Afterward, participants participated in a qualitative interview to inquire about their experience with AR-CPR.

RESULTS

Of 36 participants, 18 were randomized to AR-CPR in cycle 2 (group A) and 18 were randomized to AR-CPR in cycle 3 (group B). When using AR-CPR, 87-90% (SD 12-13%) of all CCs were in goal range, analyzed as 1-min intervals, compared with 18-21% (SD 30-33%) without feedback (p < 0.001). Analysis of qualitative themes revealed that AR-CPR may be usable without a device orientation, be effective at cognitive offloading, and reduce anxiety around and enhance confidence in the CC delivered.

CONCLUSIONS

The novel CPR feedback system, AR-CPR, significantly changed the CC performance in community hospital non-pediatric-specialized general EDs from 18-21% to 87-90% of CC epochs at goal. This study offers preliminary evidence suggesting AR-CPR improves CC quality in community hospital settings.

A scoping review of metaverse in emergency medicine

BACKGROUND

Interest in the metaverse has been growing worldwide as the virtual environment provides opportunities for highly immersive and interactive experiences. Metaverse has gradually gained acceptance in the medical field with the advancement of technologies such as big data, the Internet of Things, and 5 G mobile networks. The demand for and development of metaverse are different in diverse subspecialties owing to patients with varying degrees of clinical disease. Hence, we aim to explore the application of metaverse in acute medicine by reviewing published studies and the clinical management of patients.

METHOD

Our review examined the published articles about the concept of metaverse roadmap, and four additional domains were extracted: education, prehospital and disaster medicine, diagnosis and treatment application, and administrative affairs.

RESULTS

Augmented reality (AR) and virtual reality (VR) integration have broad applications in education and clinical training. VR-related studies surpassed AR-related studies in the emergency medicine field. The metaverse roadmap revealed that lifelogging and mirror world are still developing fields of the metaverse.

CONCLUSION

Our findings provide insight into the features, application, development, and potential of a metaverse in emergency medicine. This study will enable emergency care systems to be better equipped to face future challenges.

Department of Medicine 2040: Implementing a Constrained Disorder Principle-Based Second-Generation Artificial Intelligence System for Improved Patient Outcomes in the Department of Internal Medicine

Internal medicine departments must adapt their structures and methods of operation to accommodate changing healthcare systems. The present paper discusses some challenges departments of medicine face as healthcare providers and consumers continue to change. A co-pilot model is described in this article for augmenting physicians rather than replacing them. The paper presents the co-pilot models to improve diagnoses, treatments, and monitoring. Personalized variability patterns based on the constrained-disorder principle (CDP) are described to assess chronic therapies' effectiveness in improving patient outcomes. Based on CDP-based enhanced digital twins, this paper presents personalized treatments and follow-ups that improve diagnosis accuracy and therapy outcomes. While maintaining their professional values, departments of internal medicine must respond proactively to the needs of patients and healthcare systems. To meet the needs of patients and healthcare systems, they must strive for medical professionalism and adapt to the dynamic environment.

Can nurses in clinical practice ascribe responsibility to intelligent robots?

BACKGROUND

The twenty first- century marked the exponential growth in the use of intelligent robots and artificial intelligent in nursing compared to the previous decades. To the best of our knowledge, this article is first in responding to question, "Can nurses in clinical practice ascribe responsibility to intelligent robots and artificial intelligence when they commit errors?".

PURPOSE

The objective of this article is to present two worldviews (anthropocentrism and biocentrism) in responding to the question at hand chosen based on the roles of the entities involved in the use of intelligent robots and artificial intelligence in nursing.

METHODS

The development of this article was motivated by the immense discoveries, the current landscape, and nurses' role in relation to advanced technologies in healthcare. The paper begins the discussion by situating the use of intelligent robots and artificial intelligence in nursing and healthcare and presenting its ethical and moral implications. Then, we presented the two worldviews: anthropocentrism and biocentrism which are used to respond to the task at hand.

RESULTS

Anthropocentrism puts humans in the center in terms of moral standing and thus responsibility rests on them alone. Biocentrism declares that all creations deserve moral consideration and thus responsibility is equally allocated to all entities. Within these two worldviews, consensus development was offered to resolve these issues. Consensus provides clarity and democracy between and among the societies.

CONCLUSIONS

The findings of this article can be basis in (1) instituting mechanisms of robust peer review and a rigorous series of simulation before adopting or implementing intelligent robots and artificial intelligence in clinical practice; (2) education and training of highly specialized nurse practitioners who can be focal persons in responding to ethical and moral issues with regard to these advanced technologies; and (3) harmonization of robotics research, manufacturing, and clinical practice.

Viewpoint: Virtual and Augmented Reality in Basic and Advanced Life Support Training

The use of augmented reality (AR) and virtual reality (VR) for life support training is increasing. These technologies provide an immersive experience that supports learning in a safe and controlled environment. This review focuses on the use of AR and VR for emergency care training for health care providers, medical students, and nonprofessionals. In particular, we analyzed (1) serious games, nonimmersive games, both single-player and multiplayer; (2) VR tools ranging from semi-immersive to immersive virtual and mixed reality; and (3) AR applications. All the toolkits have been investigated in terms of application goals (training, assessment, or both), simulated procedures, and skills. The main goal of this work is to summarize and organize the findings of studies coming from multiple research areas in order to make them accessible to all the professionals involved in medical simulation. The analysis of the state-of-the-art technologies reveals that tools and studies related to the multiplayer experience, haptic feedback, and evaluation of user's manual skills in the foregoing health care-related environments are still limited and require further investigation. Also, there is an additional need to conduct studies aimed at assessing whether AR/VR-based systems are superior or, at the minimum, comparable to traditional training methods.

Augmented Reality in Pediatric Septic Shock Simulation: Randomized Controlled Feasibility Trial

BACKGROUND

Septic shock is a low-frequency but high-stakes condition in children requiring prompt resuscitation, which makes it an important target for simulation-based education.

OBJECTIVE

In this study, we aimed to design and implement an augmented reality app (PediSepsisAR) for septic shock simulation, test the feasibility of measuring the timing and volume of fluid administration during septic shock simulation with and without PediSepsisAR, and describe PediSepsisAR as an educational tool. We hypothesized that we could feasibly measure our desired data during the simulation in 90% of the participants in each group. With regard to using PediSepsisAR as an educational tool, we hypothesized that the PediSepsisAR group would report that it enhanced their awareness of simulated patient blood flow and would more rapidly verbalize recognition of abnormal patient status and desired management steps.

METHODS

We performed a randomized controlled feasibility trial with a convenience sample of pediatric care providers at a large tertiary care pediatric center. Participants completed a prestudy questionnaire and were randomized to either the PediSepsisAR or control (traditional simulation) arms. We measured the participants' time to administer 20, 40, and 60 cc/kg of intravenous fluids during a septic shock simulation using each modality. In addition, facilitators timed how long participants took to verbalize they had recognized tachycardia, hypotension, or septic shock and desired to initiate the sepsis pathway and administer antibiotics. Participants in the PediSepsisAR arm completed a poststudy questionnaire. We analyzed data using descriptive statistics and a Wilcoxon rank-sum test to compare the median time with event variables between groups.

RESULTS

We enrolled 50 participants (n=25 in each arm). The timing and volume of fluid administration were captured in all the participants in each group. There was no statistically significant difference regarding time to administration of intravenous fluids between the two groups. Similarly, there was no statistically significant difference between the groups regarding time to verbalized recognition of patient status or desired management steps. Most participants in the PediSepsisAR group reported that PediSepsisAR enhanced their awareness of the patient's perfusion.

CONCLUSIONS

We developed an augmented reality app for use in pediatric septic shock simulations and demonstrated the feasibility of measuring the volume and timing of fluid administration during simulation using this modality. In addition, our findings suggest that PediSepsisAR may enhance participants' awareness of abnormal perfusion.

The use of virtual reality and augmented reality to enhance cardio-pulmonary resuscitation: a scoping review

BACKGROUND AND OBJECTIVE

Virtual reality (VR) and augmented reality (AR) have been proposed as novel methods to enhance cardio-pulmonary resuscitation (CPR) performance and increase engagement with CPR training. A scoping review was conducted to map the global evolution of these new approaches to CPR training, to assess their efficacy and determine future directions to meet gaps in current knowledge.

METHODS

A standardised five-stage scoping methodology was used to (1) identify the research question, (2) identify relevant studies, (3) select the studies, (4) chart the data and (5) summarise the findings. The Kirkpatrick model levels of evidence were used to chart and assess the efficacy of each intervention reported. A multi-pronged search term strategy was used to search the Web of Science, PubMed, CINAHL and EMBASE databases up to June 2020.

RESULTS

A total of 42 articles were included in this review. The first relevant paper identified was published in 2009 and based on VR, from 2014 onwards there was a large increase in the volume of work being published regarding VR and AR uses in CPR training. This review reports Kirkpatrick level one to three evidence for the use of VR/AR-CPR. Inconsistencies in the specific language, keywords used and methodologies are highlighted.

CONCLUSION

VR and AR technologies have shown great potential in the area of CPR, and there is continuing evidence of new novel applications and concepts. As VR/AR research into CPR reaches an inflection point, it is key to bring collaboration and consistency to the wider research community, to enable the growth of the area and ease of access to the wider medical community.

Smart materials-integrated sensor technologies for COVID-19 diagnosis

After the first case has appeared in China, the COVID-19 pandemic continues to pose an omnipresent threat to global health, affecting more than 70 million patients and leading to around 1.6 million deaths. To implement rapid and effective clinical management, early diagnosis is the mainstay. Today, real-time reverse transcriptase (RT)-PCR test is the major diagnostic practice as a gold standard method for accurate diagnosis of this disease. On the other side, serological assays are easy to be implemented for the disease screening. Considering the limitations of today's tests including lengthy assay time, cost, the need for skilled personnel, and specialized infrastructure, both strategies, however, have impediments to be applied to the resource-scarce settings. Therefore, there is an urgent need to democratize all these practices to be applicable across the globe, specifically to the locations comprising of very limited infrastructure. In this regard, sensor systems have been utilized in clinical diagnostics largely, holding great potential to have pivotal roles as an alternative or complementary options to these current tests, providing crucial fashions such as being suitable for point-of-care settings, cost-effective, and having short turnover time. In particular, the integration of smart materials into sensor technologies leverages their analytical performances, including sensitivity, linear dynamic range, and specificity. Herein, we comprehensively review major smart materials such as nanomaterials, photosensitive materials, electrically sensitive materials, their integration with sensor platforms, and applications as wearable tools within the scope of the COVID-19 diagnosis.