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BACKGROUND Cybersecurity incidents affecting hospitals have grown in prevalence and consequence over the last two decades, increasing the importance of cybersecurity preparedness and response training to minimize clinical disruptions. OBJECTIVE This work describes the development, execution, and post-exercise assessment of a novel simulation scenario consisting of four interlocking intensive care unit (ICU) patient scenarios. This simulation was designed to demonstrate the management of acute pathologies without access to conventional treatment methods during a cybersecurity incident in order to raise clinician awareness of the increasing incidence and patient safety implications of such events. METHODS The simulation was developed by a multidisciplinary team of physicians, simulation experts, and medical education experts at UCSD School of Medicine. The simulation was first executed as part of the proceedings of CyberMed Summit, a healthcare cybersecurity conference in La Jolla, California, on November 19th, 2022. Following the simulation, a debrief session was held with the learner in front of conference attendees, with additional questioning and discussion prompted by attendee input. RESULTS The physician successfully identified the acute etiologies and managed the patients’ acute decompensations while lacking access to the hospital’s electronic medical records (EMRs), laboratory results, imaging, and communication systems. Review of footage of the event and post-experience interviews yielded numerous insights on the specific physician-focused challenges and possible solutions to a hospital-infrastructure-crippling cyber attack. CONCLUSIONS Healthcare cybersecurity incidents are known to result in significant disruption of clinical activities and can be viewed through a patient-safety oriented perspective. Simulation training may be a particularly effective method for raising clinician awareness of and preparedness for these events.

Background and objectivesCyberattacks against healthcare institutions are an increasing threat and have the potential to impair health outcomes. Current research is limited and focuses mainly on the technical consequences, whereas little is known about the healthcare staff experiences and the impact on emergency care, both during the incident and in the recovery phase. This study aims to explore the impact of a sample of large ransomware attacks against hospitals between 2017 and 2022 on acute care delivery and patient care during the recovery phase.MethodsThis interview-based qualitative study assessed the experiences of emergency healthcare professionals and Information and Communication Technology (ICT) staff and investigated the challenges faced when struck by a major hospital ransomware attack. The semi-structured interview guideline was based on current literature and cybersecurity expert consultation. Transcripts were anonymized and information tracing back to participants and/or their organizations was removed for privacy purposes.ResultsNine participants were interviewed, including emergency healthcare providers and ICT-focused staff. Five themes were constructed from the data: impact and challenges regarding patient care continuity, challenges during the recovery process, personal impact on healthcare staff, preparedness, and lessons identified and future recommendations.ConclusionsAccording to the participants of this qualitative study, ransomware attacks have a significant impact on emergency department (ED) workflow, acute patient care and the personal wellbeing of healthcare providers. Preparedness for such incidents is often limited and many challenges are encountered during the acute and recovery phase of the attack. Proactive preparedness efforts are essential to improve contingency planning and to develop response strategies for hospital ransomware attacks.

BackgroundHealthcare organizations are becoming increasingly dependent on Information technology (IT) for the provision of routine services. IT failures within hospital systems can impact acute patient care, including emergency department (ED) closure and the cancellation of operations. This study aims to gain insight into the impact of hospital IT failures in the Netherlands on acute care delivery and to provide recommendations for future preparedness.MethodsThis is a systematic scoping review of major acute care disruptions in Dutch hospitals between 2000 and 2020. Incidence of etiology, duration, ED diversion, and closure of multiple locations was evaluated. IT failures were included when associated with the closure of hospital departments and/or evacuations.ResultsThirty-nine IT failures resulting in acute care disruption were identified. The majority occurred between 2010 and 2020 (n=37, 95%). Of the 39 events, 33 (85%) were primary IT failures and were mainly caused by computer network and/or hospital software failure. Secondary events predominantly resulted from power failure. Most events (n= 36, 92%) were resolved within minutes to hours. All events were associated with an ED closure, 27 (69%) with an operating room (OR) stop and two (5%) with external hospital evacuation of one or more patients. Furthermore, 17 incidents (44%) involved multiple hospital locations, and seven (41%) of these involved closure of multiple locations with an ED.ConclusionThe impact of IT failures on acute care disruptions in the Netherlands has considerably increased since 2010. This stresses the urge to improve IT security and business continuity in today’s hospitals.PUBLIC INTEREST SUMMARYAs the healthcare industry is becoming increasingly digitalized, technological failure potentially has a major impact on hospitals and patient care continuity. The current study found that Information Technology (IT) failures in Dutch hospitals between 2000 and 2020 are increasingly associated with acute care disruptions. IT failures were mainly caused by software or hardware failures or by power outages. Preventive measures and emergency planning may lessen the impact and ensure improved business- and patient care continuity.

Objective: Cyberattacks on healthcare systems are increasing in frequency and severity. Hospitals need to integrate cybersecurity preparedness into their emergency operations planning and response to mitigate adverse outcomes during increasingly likely cyber events. No data currently exist regarding the level of preparedness of United States hospital systems for cybersecurity attacks. We surveyed hospital emergency managers to assess cybersecurity preparedness for these events. Methods: Fifty-seven emergency managers representing hospitals across the United States participated in an online Qualtrics survey regarding current preparedness and response procedures for cybersecurity hazards. Results: Survey responses between April 2019 and May 2021 demonstrated that a majority of hospital systems surveyed included cybersecurity disasters in their HVA (82.4%; 47/57), and most ranked it as 1 of their top 5 priorities (57.4%; 27/47). However, over half denied specifically mentioning cybersecurity in their Emergency Operations Plans (EOPs; 52.6%; 30/57). Fourteen of the 57 hospital systems (24.5%) endorsed previously activating an emergency response for a cybersecurity incident unrelated to information technology (IT) failure. Conclusions: The survey results suggest that American hospitals are currently underprepared for cybersecurity disasters. We emphasize the importance of prioritizing cybersecurity in Hazard Vulnerability Analyses (HVAs) and implementing specific EOP annexes for cybersecurity emergencies.

ImportanceCyberattacks on health care delivery organizations are increasing in frequency and sophistication. Ransomware infections have been associated with significant operational disruption, but data describing regional associations of these cyberattacks with neighboring hospitals have not been previously reported, to our knowledge.ObjectiveTo examine an institution’s emergency department (ED) patient volume and stroke care metrics during a month-long ransomware attack on a geographically proximal but separate health care delivery organization.Design, Setting, and ParticipantsThis before and after cohort study compares adult and pediatric patient volume and stroke care metrics of 2 US urban academic EDs in the 4 weeks prior to the ransomware attack on May 1, 2021 (April 3-30, 2021), as well as during the attack and recovery (May 1-28, 2021) and 4 weeks after the attack and recovery (May 29 to June 25, 2021). The 2 EDs had a combined mean annual census of more than 70 000 care encounters and 11% of San Diego County’s total acute inpatient discharges. The health care delivery organization targeted by the ransomware constitutes approximately 25% of the regional inpatient discharges.ExposureA month-long ransomware cyberattack on 4 adjacent hospitals.Main Outcomes and MeasuresEmergency department encounter volumes (census), temporal throughput, regional diversion of emergency medical services (EMS), and stroke care metrics.ResultsThis study evaluated 19 857 ED visits at the unaffected ED: 6114 (mean [SD] age, 49.6 [19.3] years; 2931 [47.9%] female patients; 1663 [27.2%] Hispanic, 677 [11.1%] non-Hispanic Black, and 2678 [43.8%] non-Hispanic White patients) in the preattack phase, 7039 (mean [SD] age, 49.8 [19.5] years; 3377 [48.0%] female patients; 1840 [26.1%] Hispanic, 778 [11.1%] non-Hispanic Black, and 3168 [45.0%] non-Hispanic White patients) in the attack and recovery phase, and 6704 (mean [SD] age, 48.8 [19.6] years; 3326 [49.5%] female patients; 1753 [26.1%] Hispanic, 725 [10.8%] non-Hispanic Black, and 3012 [44.9%] non-Hispanic White patients) in the postattack phase. Compared with the preattack phase, during the attack phase, there were significant associated increases in the daily mean (SD) ED census (218.4 [18.9] vs 251.4 [35.2]; P P P = .01), patients leaving without being seen (158 [2.6] vs 360 [5.1]; P P = .03). There were also significant associated increases during the attack phase compared with the preattack phase in median waiting room times (21 minutes [IQR, 7-62 minutes] vs 31 minutes [IQR, 9-89 minutes]; P P P = .01) as well as confirmed strokes (22 vs 47; P = .02).Conclusions and RelevanceThis study found that hospitals adjacent to health care delivery organizations affected by ransomware attacks may see increases in patient census and may experience resource constraints affecting time-sensitive care for conditions such as acute stroke. These findings suggest that targeted hospital cyberattacks may be associated with disruptions of health care delivery at nontargeted hospitals within a community and should be considered a regional disaster.

Study Objectives: The surge and long tail of patients in acute respiratory distress during the coronavirus-19 (CoVID19) pandemic has inspired new innovations in diagnosing, treating and dispositioning patients during high census conditions with constrained resources During the first wave of the pandemic, we deployed an artificial intelligence (AI) algorithm for assisted interpretation of chest x-ray for use by radiologists and emergency department (ED) physicians We report first experiences of physician interaction with this novel AI algorithm designed to enhance physician abilities to identify ground glass and consolidation on chest radiographs Methods: Design: We created a fully-automated pipeline into the clinical environment to provide AI augmentation of chest x-rays, utilizing a previously developed deep learning-based AI algorithm Trained with 22,000 annotations by radiologists, the algorithm overlays X-rays with color-coded maps that indicate pneumonia probability This was provided alongside standard chest x-ray images for physicians to use in real-time at the point of care with existing imaging software For this prospective observational study, we developed a 3-point survey to characterize experiences with the tool regarding ease of use and impact on clinical decision-making Setting: Surveys were conducted during a one-month period surrounding the projected CoVID-19 surge locally (April 8-May 9) at two academic hospitals in Southern California A federal declaration of emergency occurred March 13, 2020 and the tool was urgently deployed on March 25 Types of Participants: Emergency medicine resident and attending physicians surveyed in real time by telephone Results: Of the 5,125 total visits and 1,960 chest radiographs obtained in the ED during the study period, 1,855 were analyzed by the algorithm Among these, emergency physicians were surveyed for their experiences on 202 Real-time computation and delivery of the tool took four minutes on average Overall, 86% either strongly agreed or somewhat agreed that the intervention was easy to use in the existing workflow 20% of all respondents reported that the algorithm impacted their clinical decision making In general, resident physicians found the AI implementation easier to use than attendings (Mann Whitney U, p=0 005) Descriptive statistics regarding further impact are summarized below (table 1) Conclusion: This AI technology was rapidly deployed in a large academic health system in the first wave of a global pandemic Surveyed ED physicians found this implementation easy to use within existing workflows Twenty percent of physicians reported that the tool changed clinical decision making, and approximately one third of those found that it impacted diagnostic testing decisions and treatment plans Several physicians reported ordering COVID-19 PCR testing as a direct result of the AI, resulting in positive tests and subsequent quarantining of patients who otherwise might not have been appropriately diagnosed To our knowledge, this is the first published study evaluating the impact of medical imaging AI on clinical decision making in the ED setting and may prove to be a powerful tool during the pandemic response [Formula presented]

As a result of the extensive integration of technology into the healthcare system, cybersecurity incidents have become an increasing challenge for the healthcare industry. Recent examples include WannaCry, a nontargeted ransomware attack on more than 150 countries worldwide that temporarily crippled parts of the National Health Service in the United Kingdom, and the 2016 ransomware attack on Los Angeles's Hollywood Presbyterian Medical Center. The attacks cost millions of dollars in lost revenue and fines, as well as significant reputational damage. Efforts are needed to devise tools that allow experts to more accurately quantify the actual impact of such events on both individual patients and healthcare systems as a whole. While the United States has robust disaster preparedness and response systems integrated throughout the healthcare and government sectors, the rapidly evolving cybersecurity threat against healthcare entities is outpacing existing countermeasures and challenges in the "all-hazards" disaster preparedness paradigm. Further epidemiologic research of clinical cybersecurity attacks and their effects on patient care and clinical outcomes is necessary to prevent and mitigate future attacks.

The future of connected health care will involve the collection of patient data or enhancement of clinician workflows through various biosensors and displays found on wearable electronic devices, many of which are marketed directly to consumers. The adoption of wearables in health care is being driven by efforts to reduce health care costs, improve care quality, and increase clinician efficiency. Wearables have significant potential to achieve these goals but are currently limited by lack of widespread integrations into electronic health records, biosensor data collection types, and a lack of scientifically rigorous literature showing benefit.

UNSTRUCTURED The practice of medicine has become increasingly dependent on connected medical technology and nearly every clinical environment requires physicians to be proficient in using computerized workflows for patient care. Cyberattacks affecting healthcare delivery organizations (HDOs) have evolved in frequency, sophistication, and scale. These attacks often involve the theft of protected health information (PHI) and disrupt the function and availability of critical resources such as diagnostic technologies, therapeutic devices, and electronic health records (EHRs). This review aims to describe cybersecurity threats as they impact the delivery of patient care by affecting the operational, financial, and clinical systems of HDOs and associated entities. It will highlight the challenges that remain in developing a database of evidence focused on classifying and comparing cybersecurity attacks by clinical relevance and effect on patient outcomes. Attackers have focused on health care as a key sector of critical infrastructure because the opportunity for financial gain has been reliably demonstrated. That cyberattacks have continued, and in many cases been precisely targeted to affect hospitals during the COVID-19 pandemic, demonstrates the persistent and malicious nature of cybersecurity challenges. The existing literature has discussed the theoretical clinical implications of vulnerabilities discovered in connected medical devices including insulin pumps and pacemakers, and media reports have prominently featured coverage of HDOs affected by cyberattacks. Yet there remains a paucity of data describing such events that can be used to quantitatively characterize effects on patient outcomes. The complex interplay of varying regulatory oversights, embryonic surveillance mechanisms, and organization-specific policies have made obtaining the data needed for formal epidemiological studies difficult. Despite these challenges, multiple United States government agencies, healthcare delivery organization industry groups, and healthcare advocacy groups have recognized cybersecurity threats as a key safety and operational concern worthy of expanded study and dedicated resources.

Purpose Cancer patients frequently utilize the emergency department (ED) for a variety of diagnoses both related to and unrelated to their cancer, yet ED outcomes for cancer patients are not well documented. This study sought to define risks and identify predictors for inpatient admission and hospital mortality among cancer patients presenting to the ED. Patients and Methods We utilized the National Emergency Department Sample to identify patients with and without a diagnosis of cancer presenting to the ED between January 2016 and December 2018. We used multivariable mixed‐effects logistic regression models to assess the influence of cancer on outcomes of hospital admission after the ED visit and hospital mortality for the whole patient cohort and individual presenting diagnoses. Results There were 340 million weighted ED visits, of which 8.3 million (2.3%) were associated with a cancer diagnosis. Compared to non‐cancer patients, patients with cancer had an increased risk of inpatient admission (64.7% vs. 14.8%; p, On average, 2.8 million (2.3%) patients visit the emergency department. For each of the top 15 presenting diagnoses cancer patients had increased risks of hospitalization and death compared to non‐cancer patients. Among cancer patients, cancer site was the most robust individual predictor associated with risk of hospitalization or death.

Background Cybersecurity risks in health care systems have traditionally been measured in data breaches of protected health information, but compromised medical devices and critical medical infrastructure present risks of disruptions to patient care. The ubiquitous prevalence of connected medical devices and systems may be associated with an increase in these risks. Objective This article details the development and execution of three novel high-fidelity clinical simulations designed to teach clinicians to recognize, treat, and prevent patient harm from vulnerable medical devices. Methods Clinical simulations were developed that incorporated patient-care scenarios featuring hacked medical devices based on previously researched security vulnerabilities. Results Clinicians did not recognize the etiology of simulated patient pathology as being the result of a compromised device. Conclusions Simulation can be a useful tool in educating clinicians in this new, critically important patient-safety space.

Background In March of 2020, the World Health Organization declared coronavirus disease 2019 (COVID-19)—a disease caused by a novel coronavirus—a pandemic, and it continued to spread rapidly in the community. Our institution implemented an emergency medicine telehealth system that sought to expedite care of stable patients, decrease provider exposure to COVID-19, decrease overall usage rate of personal protective equipment, and provide a platform so that infected or quarantined physicians could continue to work. This effort was among the first to use telehealth to practice emergency medicine in the setting of a pandemic in the United States. Discussion Outside the main emergency departments at each of 2 sites of our academic institution, disaster tents were erected with patient care equipment and medications, as well as technology to allow for telehealth visits. The triage system was modified to appropriately select low-risk patients with symptoms suggestive of COVID-19 who could be seen in these disaster tents. Despite some issues that needed to be addressed, such as provider discomfort, limited medication availability, and connectivity problems, the model was successful overall. Conclusions Other emergency departments might find this proof of concept article useful. Telehealth will likely be used more broadly in the future, including emergency care. It is imperative that the health care system continues to adapt to respond appropriately to challenges such as pandemics.

Author(s): Cronin, Alexandrea O.; Carlile, Morgan A.; Dameff, Christian J.; Coyne, Christopher J.; Castillo, Edward M. | Abstract: Introduction: The coronavirus disease 2019 (COVID-19) pandemic has seriously impacted clinical research operations in academic medical centers due to social distancing measures and stay-at-home orders. The purpose of this paper is to describe the implementation of a program to continue clinical research based out of an emergency department (ED) using remote research associates (RA).Methods: Remote RAs were trained and granted remote access to the electronic health record (EHR) by the health system’s core information technology team. Upon gaining access, remote RAs used a dual-authentication process to gain access to a host-based, firewall-protected virtual network where the EHR could be accessed to continue screening and enrollment for ongoing studies. Study training for screening and enrollment was also provided to ensure study continuity.Results: With constant support and guidance available to establish this EHR access pathway, the remote RAs were able to gain access relatively independently and without major technical troubleshooting. Each remote RA was granted access and trained on studies within one week and self-reported a high degree of program satisfaction, EHR access ease, and study protocol comfort through informal evaluation surveys.Conclusions: In response to the COVID-19 pandemic, we virtualized a clinical research program to continue important ED-based studies.

UNSTRUCTURED Health Level 7 (HL7) is a ubiquitous protocol in healthcare infrastructure, used to interface a variety of systems. HL7 lacks encryption at the level of the protocol, and is thus vulnerable to attacks that modify message contents. We describe a sophisticated man-in-the-middle attack that injects misinformation into clinical workflow that can cause patient harm.

Background Connected medical technology is increasingly prevalent and offers both a host of new therapeutic potentials and cybersecurity-related considerations. Current practice largely does not include discussions of cybersecurity issues when clinicians obtain informed consent. Objective This paper aims to raise awareness about cybersecurity considerations for connected medical technology as they relate to informed consent discussions between patients and clinicians. Methods Clinicians, health care cybersecurity researchers, and informed consent experts propose the concept of a cybersecurity informed consent for connected medical technology. Results This viewpoint discusses concepts designed to facilitate further discussion on the need, development, and execution of cybersecurity informed consent. Conclusions Cybersecurity informed consent may be a necessary component of informed consent practices, as connected medical technology proliferates in the health care environment.

BACKGROUND Connected medical technology is increasingly prevalent and offers both a host of new therapeutic potentials and cybersecurity-related considerations. Current practice largely does not include discussions of cybersecurity issues when clinicians obtain informed consent. OBJECTIVE This paper aims to raise awareness about cybersecurity considerations for connected medical technology as they relate to informed consent discussions between patients and clinicians. METHODS Clinicians, health care cybersecurity researchers, and informed consent experts propose the concept of a cybersecurity informed consent for connected medical technology. RESULTS This viewpoint discusses concepts designed to facilitate further discussion on the need, development, and execution of cybersecurity informed consent. CONCLUSIONS Cybersecurity informed consent may be a necessary component of informed consent practices, as connected medical technology proliferates in the health care environment.

e18618 Background: Cancer patients frequently utilize the Emergency Department (ED) for a variety of diagnoses, both related and unrelated to their cancer. Patients with cancer have unique risks related to their cancer and treatment which could influence ED-related outcomes. A better understanding of these risks could help improve risk-stratification for these patients and help inform future interventions. This study sought to define the increased risks cancer patients face for inpatient admission and hospital mortality among cancer patients presenting to the ED. Methods: From the National Emergency Department Sample (NEDS) we identified patients with and without a diagnosis of cancer presenting to the ED between 2016 and 2018. We used International Classification of Diseases, version 10 (ICD10-CM) codes to identify patients with cancer, and to identify patient’s presenting diagnosis. Multivariable mixed-effects logistic regression models assessed the influence of cancer diagnoses on two endpoints: hospital admission from the ED, and inpatient hospital mortality. Results: There were 340 million weighted ED visits, of which 8.3 million (2.3%) occurred in patients with a cancer diagnosis. Compared to non-cancer patients, patients with cancer had an increased risk of inpatient admission (64.7% vs. 14.8%; p < 0.0001) and hospital mortality (4.6% vs. 0.5%; p < 0.0001). Factors associated with both an increased risk of hospitalization and death included older age, male gender, lower income level, discharge quarter, and receipt of care in a teaching hospital. We identified the top 15 most common presenting diagnoses among cancer patients, and among each of these diagnoses, cancer patients had increased risks of hospitalization (odds ratio [OR] range 2.0-13.2; all p < 0.05) and death (OR range 2.1-14.4; all p < 0.05) compared to non-cancer patients with the same diagnosis. Within the cancer patient cohort, cancer site was the most robust individual predictor associated with risk of hospitalization or death, with highest risk among patients with metastatic cancer, liver and lung cancers compared to the reference group of prostate cancer patients. Conclusions: Cancer patients presenting to the ED have high risks for hospital admission and death when compared to patients without cancer. Cancer patients represent a distinct population and may benefit from cancer-specific risk stratification or focused interventions tailored to improve outcomes in the ED setting.

1512 Background: Elderly hospitalized cancer patients face high risks of inpatient hospital mortality. Identifying patients at high risk of hospital mortality could help with risk stratification, and potentially help inform future interventions aimed at improving outcomes. We evaluated the predictive capacity of a high-dimensional machine-learning prediction tool to predict inpatient mortality, and compared the performance of this new tool to existing prediction indices. Methods: We identified cancer patients 75 and over who presented to an emergency department (ED) and were subsequently hospitalized from the National Emergency Department Sample (NEDS) between 2016 and 2018. We used an extreme gradient boosting approach to predict the risk of death during hospitalization. Model covariates included patient demographics, hospital characteristics, and International Classification of Diseases, version 10 (ICD-10) diagnosis codes recorded during the ED visit. The data were split 75%/25% into training/testing datasets. We constructed the model with training data and evaluated performance within the test data using area under the curve (AUC), with an AUC of 1.0 indicating perfect prediction. We compared the performance of this risk prediction model to standard prediction indices including the Hospital Frailty Risk Score, modified 5-item frailty index, and Charlson comorbidity index. Results: We identified 1,892,690 weighted-hospitalizations among elderly cancer patients, of which 133,379 (7.0%) who died in the inpatient setting. Our final predictive model included 238 features, which contained 5 demographic variables, 3 hospital characteristics, and 230 ICD-10 diagnosis codes. The predictive model achieved an AUC of 0.92. Our comparator models including the Hospital Frailty Risk Score, modified 5-item frailty index, and Charlson comorbidity index achieved AUCs of 0.67, 0.56, and 0.60, respectively. Conclusions: Using a high-dimensional machine-learning model enabled a high level of precision in predicting hospital mortality among elderly cancer patients, substantially out-performing existing prediction indices. High-dimensional prediction models show promise in helping to identify patients at risk of severe adverse outcomes, though additional validation is needed as well as research studying how to implement these tools into practice.

e22019 Background: Pediatric cancer patients represent a vulnerable cohort at risk of adverse outcomes after presenting to the emergency department (ED). Given the severity of cancer-related complications and uniqueness of this population, approaches to better risk stratify this cohort could potentially help define future interventions geared towards improving outcomes. We used a high-dimensional machine learning approach to help define the risk of hospitalization after an ED visit among pediatric patients with cancer. Methods: A cohort of cancer patients under 18 was identified from the Nationwide Emergency Department Sample (NEDS) between 2016-2018. We used a lasso regression model to predict inpatient admission after an ED visit. Model covariates included patient demographics, hospital characteristics, and International Classification of Diseases, version 10 (ICD-10) diagnosis codes. The data were split 75%/25% into training/testing data. The model was constructed with training data, and performance assessed on the test data using the area under the curve (AUC), with an AUC of 1.0 indicating perfect prediction. Results: We identified 129,631 pediatric cancer patients who visited the ED, of which 54.5% were subsequently admitted. The final predictive model included 150 variables, including 9 demographic, 6 hospital, and 135 unique ICD-10 codes. The model demonstrated excellent ability to predict hospitalization with an AUC of 0.96. The top 5 most important variables associated with inpatient admission were diagnoses of paralytic ileus/intestinal obstruction, neutropenia, sepsis, aplastic anemia/bone marrow failure, and bacterial infection. Conclusions: Pediatric cancer patients frequently present to the ED with complications of their cancer or their treatment, and over half of these patients are admitted. This study demonstrates the capacity of high-dimensional prediction models to help identify pediatric patients at risk of hospitalization. Additional research is needed to determine how to implement these predictive models in clinical practice.

Emergency medical telecommunicators can play a key role in improving outcomes from out-of-hospital cardiac arrest (OHCA) by providing instructions for cardiopulmonary resuscitation (CPR) to callers. Telecommunicators, however, frequently encounter barriers that obstruct the Telephone CPR (TCPR) process. The nature and frequency of these barriers in public and residential locations have not been well investigated. The aim of this study is to identify the barriers to TCPR in public and residential locations.We conducted a retrospective study of audio recordings of EMS-confirmed OHCAs from eight regional 9-1-1 dispatch centers between January 2012 and December 2013.We reviewed 1850 eligible cases (public location OHCAs: N=223 and residential location OHCAs: N=1627). Telecommunicators less frequently encountered barriers such as inability to calm callers in public than in residential locations (2.1% vs 8.5%, p=0.002) or inability to place victims on a hard flat surface (13.9% vs 25.4%, p0.001). However, the barrier where callers were not with patients was more frequently observed in public than in residential locations (11.8% vs 2.7%, p0.001).This study revealed that barriers to TCPR are distributed differently across public and residential locations. Understanding these differences can aid in the development of strategies to enhance bystander CPR and improve overall patient outcomes.

Audiovisual streaming technologies allow detailed remote patient assessment and have been suggested to change management and enhance triage. The advent of wearable, head-mounted devices (HMDs) permits advanced teletoxicology at a relatively low cost. A previously published pilot study supports the feasibility of using the HMD Google Glass® (Google Inc.; Mountain View, CA) for teletoxicology consultation. This study examines the reliability, accuracy, and precision of the poisoned patient assessment when performed remotely via Google Glass®.A prospective observational cohort study was performed on 50 patients admitted to a tertiary care center inpatient toxicology service. Toxicology fellows wore Google Glass® and transmitted secure, real-time video and audio of the initial physical examination to a remote investigator not involved in the subject's care. High-resolution still photos of electrocardiograms (ECGs) were transmitted to the remote investigator. On-site and remote investigators recorded physical examination findings and ECG interpretation. Both investigators completed a brief survey about the acceptability and reliability of the streaming technology for each encounter. Kappa scores and simple agreement were calculated for each examination finding and electrocardiogram parameter. Reliability scores and reliability difference were calculated and compared for each encounter.Data were available for analysis of 17 categories of examination and ECG findings. Simple agreement between on-site and remote investigators ranged from 68 to 100 % (median = 94 %, IQR = 10.5). Kappa scores could be calculated for 11/17 parameters and demonstrated slight to fair agreement for two parameters and moderate to almost perfect agreement for nine parameters (median = 0.653; substantial agreement). The lowest Kappa scores were for pupil size and response to light. On a 100-mm visual analog scale (VAS), mean comfort level was 93 and mean reliability rating was 89 for on-site investigators. For remote users, the mean comfort and reliability ratings were 99 and 86, respectively. The average difference in reliability scores between on-site and remote investigators was 2.6, with the difference increasing as reliability scores decreased.Remote evaluation of poisoned patients via Google Glass® is possible with a high degree of agreement on examination findings and ECG interpretation. Evaluation of pupil size and response to light is limited, likely by the quality of streaming video. Users of Google Glass® for teletoxicology reported high levels of comfort with the technology and found it reliable, though as reported reliability decreased, remote users were most affected. Further study should compare patient-centered outcomes when using HMDs for consultation to those resulting from telephone consultation.

BACKGROUND Cybersecurity risks in healthcare systems have traditionally been measured in data breaches of protected health information but compromised medical devices and critical medical infrastructure raises questions about the risks of disrupted patient care. The increasing prevalence of these connected medical devices and systems implies that these risks are growing. OBJECTIVE This paper details the development and execution of three novel high fidelity clinical simulations designed to teach clinicians to recognize, treat, and prevent patient harm from vulnerable medical devices. METHODS Clinical simulations were developed which incorporated patient care scenarios with hacked medical devices based on previously researched security vulnerabilities. RESULTS Clinician participants universally failed to recognize the etiology of their patient’s pathology as being the result of a compromised device. CONCLUSIONS Simulation can be a useful tool in educating clinicians in this new, critically important patient safety space.

Background Emergency 9‐1‐1 callers use a wide range of terms to describe abnormal breathing in persons with out‐of‐hospital cardiac arrest ( OHCA ). These breathing descriptors can obstruct the telephone cardiopulmonary resuscitation ( CPR ) process. Methods and Results We conducted an observational study of emergency call audio recordings linked to confirmed OHCA s in a statewide Utstein‐style database. Breathing descriptors fell into 1 of 8 groups (eg, gasping, snoring). We divided the study population into groups with and without descriptors for abnormal breathing to investigate the impact of these descriptors on patient outcomes and telephone CPR process. Callers used descriptors in 459 of 2411 cases (19.0%) between October 1, 2010, and December 31, 2014. Survival outcome was better when the caller used a breathing descriptor (19.6% versus 8.8%, P OHCA recognition, CPR instruction, and telephone CPR were lower than when callers did not use a breathing descriptor (79.7% versus 93.0%, P P =0.0078; and 60.2% versus 66.9%, P =0.0123, respectively). The time interval between call receipt and OHCA recognition was longer when the caller used a breathing descriptor (118.5 versus 73.5 seconds, P Conclusions Descriptors of abnormal breathing are associated with improved outcomes but also with delays in the identification of OHCA . Familiarizing telecommunicators with these descriptors may improve the telephone CPR process including OHCA recognition for patients with increased probability of survival.

Background The use of ultrasound during invasive bedside procedures is quickly becoming the standard of care. Ultrasound machine placement during procedures often requires the practitioner to turn their head during the procedure to view the screen. Such turning has been implicated in unintentional hand movements in novices. Google Glass is a head-mounted computer with a specialized screen capable of projecting images and video into the view of the wearer. Such technology may help decrease unintentional hand movements. Objective Our aim was to evaluate whether or not medical practitioners at various levels of training could use Google Glass to perform an ultrasound-guided procedure, and to explore potential advantages of this technology. Methods Forty participants of varying training levels were randomized into two groups. One group used Google Glass to perform an ultrasound-guided central line. The other group used traditional ultrasound during the procedure. Video recordings of eye and hand movements were analyzed. Results All participants from both groups were able to complete the procedure without difficulty. Google Glass wearers took longer to perform the procedure at all training levels (medical student year 1 [MS1]: 193 s vs. 77 s, p > 0.5; MS4: 197s vs. 91s, p ≤ 0.05; postgraduate year 1 [PGY1]: 288s vs. 125 s, p > 0.5; PGY3: 151 s vs. 52 s, p ≤ 0.05), and required more needle redirections (MS1: 4.4 vs. 2.0, p > 0.5; MS4: 4.8 vs. 2.8, p > 0.5; PGY1: 4.4 vs. 2.8, p > 0.5; PGY3: 2.0 vs. 1.0, p > 0.5). Conclusions In this study, it was possible to perform ultrasound-guided procedures with Google Glass. Google Glass wearers, on average, took longer to gain access, and had more needle redirections, but less head movements were noted.

Glucagon-like peptide-1 (GLP-1) has protective effects in the heart. We hypothesized that GLP-1 would mitigate coronary microvascular and left ventricular (LV) dysfunction if administered after cardiac arrest and resuscitation (CAR). Eighteen swine were subjected to ventricular fibrillation followed by resuscitation. Swine surviving to return of spontaneous circulation (ROSC) were randomized to receive an intravenous infusion of either human rGLP-1 (10 pmol·kg−1·min−1; n = 8) or 0.9% saline ( n = 8) for 4 h, beginning 1 min after ROSC. CAR caused a decline in coronary flow reserve (CFR) in control animals (pre-arrest, 1.86 ± 0.20; 1 h post-ROSC, 1.3 ± 0.05; 4 h post-ROSC, 1.25 ± 0.06; P < 0.05). GLP-1 preserved CFR for up to 4 h after ROSC (pre-arrest, 1.31 ± 0.17; 1 h post-ROSC, 1.5 ± 0.01; 4 h post-ROSC, 1.55 ± 0.22). Although there was a trend toward improvement in LV relaxation in the GLP-1-treated animals, overall LV function was not consistently different between groups. 8 -iso-PGF2α, a measure of reactive oxygen species load, was decreased in post-ROSC GLP-1-treated animals [placebo, control (NS): 38.1 ± 1.54 pg/ml; GLP-1: 26.59 ± 1.56 pg/ml; P < 0.05]. Infusion of GLP-1 after CAR preserved coronary microvascular and LV diastolic function. These effects may be mediated through a reduction in oxidative stress.

Bystander cardiopulmonary resuscitation (CPR) significantly improves survival from out-of-hospital cardiac arrest but is provided in less than half of events on average. Telephone CPR (TCPR) can significantly increase bystander CPR rates and improve clinical outcomes.To investigate the effect of a TCPR bundle of care on TCPR process measures and outcomes.A prospective, before-after, observational study of adult patients with out-of-hospital cardiac arrest not receiving bystander CPR before the 9-1-1 call between October 1, 2010, and September 30, 2013.A TCPR program, including guideline-based protocols, telecommunicator training, data collection, and feedback, in 2 regional dispatch centers servicing metropolitan Phoenix, Arizona. Audio recordings of out-of-hospital cardiac arrest calls were audited and linked with emergency medical services and hospital outcome data.Survival to hospital discharge and functional outcome at hospital discharge.There were 2334 out-of-hospital cardiac arrests (798 phase 1 [P1] and 1536 phase 2 [P2]) in the study group; 64% (1499) were male, and the median age was 63 years (age range, 9-101 years; interquartile range, 51-75 years). Provision of TCPR increased from 43.5% in P1 to 52.8% in P2 (P .001), yielding an increase of 9.3% (95% CI, 4.9%-13.8%). The median time to first chest compression decreased from 256 seconds in P1 to 212 seconds in P2 (P .001). All rhythm survival was significantly higher in P2 (184 of 1536 [12.0%]) compared with P1 (73 of 798 [9.1%]), with an adjusted odds ratio (aOR) of 1.47 (95% CI, 1.08-2.02; P = .02) in a logistic regression model and an adjusted difference in absolute survival rates (adjusted rate difference) of 3.1% (95% CI, 1.5%-4.9%). Survival for patients with a shockable initial rhythm significantly improved in P2 (107 of 306 [35.0%]) compared with P1 (42 of 170 [24.7%]), with an aOR of 1.70 (95% CI, 1.09-2.65; P = .02) and an adjusted rate difference of 9.6% (95% CI, 4.8%-14.4%). The rate of favorable functional outcome was significantly higher in P2 (127 of 1536 [8.3%]; 95% CI, 6.9%-9.8%) than in P1 (45 of 798 [5.6%]; 95% CI, 4.1%-7.5%), with an aOR of 1.68 (95% CI, 1.13-2.48; P = .01) and an adjusted rate difference of 2.7% (95% CI, 1.3%-4.4%).Implementation of a guideline-based TCPR bundle of care was independently associated with significant improvements in the provision and timeliness of TCPR, survival to hospital discharge, and survival with favorable functional outcome.

Background— In cardiac arrest, the ventricular fibrillation (VF) waveform, particularly amplitude spectral area (AMSA) and slope, predicts the return of spontaneous circulation (ROSC), but it is unknown whether the predictive utility differs in an acute myocardial infarction (MI), prior MI, or normal myocardium and if the waveform can distinguish the underlying myocardial state. We hypothesized that in a swine model of VF cardiac arrest, AMSA and slope predict ROSC after a shock independent of substrate and distinguish an acute from nonacute MI state. Methods and Results— MI was induced by occlusion of the left anterior descending artery. Post MI swine recovered for a 2-week period before induction of VF. VF was untreated for 8 minutes in 10 acute MI, 10 post MI, and 10 control swine. AMSA and slope predicted ROSC after a shock independent of myocardial state. For AMSA >31 mV-Hz, the odds ratio was 62 ( P ≤0.001) compared with AMSA 3.1 mV/s, odds ratio was 52 ( P ≤0.001) compared with slope 33.5 mV-Hz, the sensitivity to identify an acute from nonacute (control or post MI) state was 83%. Conclusions— In a swine model of VF cardiac arrest, AMSA and slope predict ROSC independent of myocardial substrate. Furthermore, with chest compressions, the VF waveform evolves differently and may offer a means to distinguish an acute MI.

Background: Abnormal breathing, or “gasping,” frequently occurs during cardiac arrest and has been associated with increased survival. During 9-1-1 calls, early recognition of gasping may allow dispatchers to identify OHCA and to start CPR instructions promptly. Objective: To characterize lay rescuers’ descriptions of abnormal breathing during OHCA. Methods: 9-1-1 audio recordings of 4,279 confirmed OHCAs (10/10-7/14) in Arizona were reviewed by program personnel. Terms used by lay rescuers to describe victims’ breathing were recorded in a structured database. Results: Abnormal breathing (as opposed to “not breathing”) was described in 542/4279 (13%) calls. There were 623 verbal descriptions. A total of 50 different terms were used. However, the top 8 accounted for 83% and the remaining 42 descriptions (“Other”) comprised only 17% of all terms used. “Gasping” was the most commonly used term (30%; 187/623), followed by “snoring” (15%; 95/623; Figure). Conclusion: Lay rescuers used a wide variety of terms to describe abnormal breathing in documented OHCA. However, 8 terms accounted for 83% of descriptions. Familiarizing 9-1-1 telecommunicators with the most commonly used terms may improve the accuracy of recognition and hasten the identification of OHCA.

To evaluate CPR quality during cardiac resuscitation attempts in an urban emergency department (ED) and determine the influence of the combination of scenario-based training, real-time audiovisual feedback (RTAVF), and post-event debriefing on CPR quality.CPR quality was recorded using an R Series monitor-defibrillator (ZOLL Medical) during the treatment of adult cardiac arrest patients. Phase 1 (P1; 11/01/2010-11/15/2012) was an observation period of CPR quality. Phase 2 (P2; 11/15/2012-11/08/2013) was after a 60-min psychomotor skills CPR training and included RTAVF and post-event debriefing.A total of 52 cardiac arrest patients were treated in P1 (median age 56 yrs, 63.5% male) and 49 in P2 (age 60 yrs, 83.7% male). Chest compression (CC) depth increased from 46.7 ± 3.8mm in P1 to 61.6 ± 2.8mm in P2 (p0.001), with the percentage of CC ≥ 51 mm increasing from 30.6% in P1 to 87.4% in P2 (p0.001). CC release velocity increased from 314 ± 25 mm/s in P1 to 442 ± 20 mm/s in P2 (p0.001). No significant differences were identified in CC fraction (84.3% P1 vs. 88.4% P2, p = 0.1), CC rate (125 ± 3 cpm P1 vs. 125 ± 3 cpm P2, p = 0.7), or pre-shock pause (9.7s P1 vs. 5.9s P2, p = 0.5), though CC fraction and pre-shock pause were within guideline recommendations.Implementation of the bundle of scenario-based training, real-time audiovisual CPR feedback, and post-event debriefing was associated with improved CPR quality and compliance with CPR guidelines in this urban teaching emergency department.

PROBLEM Medical education today frequently includes standardized patient (SP) encounters to teach history-taking, physical exam, and communication skills. However, traditional wall-mounted cameras, used to record video for faculty and student feedback and evaluation, provide a limited view of key nonverbal communication behaviors during clinical encounters. APPROACH In 2013, 30 second-year medical students participated in an end-of-life module that included SP encounters in which the SPs used Google Glass to record their first-person perspective. Students reviewed the Google Glass video and traditional videos and then completed a postencounter, self-evaluation survey and a follow-up survey about the experience. OUTCOMES Google Glass was used successfully to record 30 student/SP encounters. One temporary Google Glass hardware failure was observed. Of the 30 students, 7 (23%) reported a "positive, nondistracting experience"; 11 (37%) a "positive, initially distracting experience"; 5 (17%) a "neutral experience"; and 3 (10%) a "negative experience." Four students (13%) opted to withhold judgment until they reviewed the videos but reported Google Glass as "distracting." According to follow-up survey responses, 16 students (of 23; 70%) found Google Glass "worth including in the [clinical skills program]," whereas 7 (30%) did not. NEXT STEPS Google Glass can be used to video record students during SP encounters and provides a novel perspective for the analysis and evaluation of their interpersonal communication skills and nonverbal behaviors. Next steps include a larger, more rigorous comparison of Google Glass versus traditional videos and expanded use of this technology in other aspects of the clinical skills training program.

Objectives: We explore and describe clinical experiences of the early adoption of smart glasses, for dermatology/ allergy decision support in two cases of suspected oral metal allergy. Materials and methods: Smart glasses were used to facilitate physician-dentist communication during dentalexamination for two patients with concerns for metal allergy and bio-incompatibility. Results: Clinical use of smart glasses has reduced the burden of multiple visits and facilitated coordinated care for complex patients with suspected oral metal allergy. Conclusions: Smart glasses technology may be used for hands-free videoconferencing during exams and procedures to coordinate care and consult experts in cutaneous allergy, increasing quality of care in complex cases and decreasing patient consults and visits. Clinical relevance: Smart glasses and other telecommunication devices will play larger roles in healthcare in the future, especially where their use will decrease visits and potentially decrease cost of care.

Background Bystander cardiopulmonary resuscitation (CPR) improves out-of-hospital cardiac arrest (OHCA) survival. Telephone CPR (TCPR) comprises CPR instruction given by emergency dispatchers to bystanders responding to OHCA and the CPR performed as a result. TCPR instructions improve bystander CPR rates, but the quality of the instructions varies widely. No standardized system exists to critically evaluate the TCPR intervention. Methods Investigators analyzed audio recordings of suspected OHCA calls from a large regional 9-1-1 dispatch center and applied descriptive terms, a data collection tool and a six metric reporting template to describe TCPR. Data were obtained from October 2010 to November 2011. Dispatcher recognition of CPR need, delivery of TCPR instructions, and bystander CPR performance were documented. Results A total of 590 calls were analyzed. Call evaluators achieved “near perfect agreement” with 5/6 reporting metrics and “strong agreement” on the 6th metric: percentage of calls where need for CPR was recognized by dispatch. CPR was indicated in 317 calls and already in progress in 94. Dispatchers recognized the need for TCPR in 176 of the 223 (79%) remaining calls. CPR instructions were started in 65/223 (29%) and bystander CPR resulting from TCPR instructions was started in 31/223 (14%). Conclusion We developed and demonstrated successful implementation of a simple data collection and reporting system for critical evaluation of the TCPR intervention. A standardized methodology for measuring TCPR is necessary to perform on-going quality improvement, to establish performance standards, and for future research on how to optimize bystander CPR rates and OHCA survival.