Smartphone Use in the Management of Neurological Emergencies: A Simulation-based Study

Smartphone Use in the Management of Neurological Emergencies: A Simulation-based Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Smartphone Use in the Management of Neurological Emergencies: A Simulation-based Study Melissa B. Pergakis, Afrah A. Ali, WanTsu Wendy Chang, Benjamin Neustein, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3914951/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 May, 2024 Read the published version in Neurocritical Care → Version 1 posted 4 You are reading this latest preprint version Abstract Background and Objectives Smartphone use in medicine is nearly universal despite a dearth of research assessing utility in clinical performance. We sought to identify and define smartphone use during simulated neuro-emergencies. Methods In this retrospective review of a prospective, observational, single-center simulation-based study, participants, ranging from sub-interns to attending physicians and stratified by training level (novice, intermediate, and advanced) managed a variety of neurological emergencies. The primary outcome was frequency and purpose of smartphone use. Secondary outcomes included success rate of smartphone use and performance (measured by completion of critical tasks) of participants who used smartphones vs. those that did not. In subgroup analyses we compared outcomes across participants by level of training using t-tests and Chi-square statistics. Results One hundred and three participants completed 245 simulation scenarios. Smartphones were used in 109 (45%) simulations. Of participants using smartphones, 102 participants looked up medication doses, 52 participants looked up management guidelines, 11 participants looked up hospital protocols, and 13 participants used smartphones for assistance with an exam scale. Participants found the correct answer 73% of the time using smartphones. There was an association between participant level and smartphone use with intermediate participants being more likely to use their smartphones than novice or advanced participants, 53% vs. 29% and 26%, respectively (p < .05). Of the intermediate participants, those who used smartphones did not perform better during the simulation scenario than participants who did not use smartphones (smartphone users’ mean score [standard deviation (SD)] = 12.3 (2.9) vs. non-smartphone users’ mean score (SD) = 12.9 (2.7), p = .85). Discussion Participants commonly used smartphones in simulated neuro-emergencies but use didn’t confer improved clinical performance. Less experienced participants were the most likely to use smartphones, were less likely to arrive at correct conclusions, and thus are the most likely to benefit from an evidence-based smartphone application for neuro-emergencies. Simulation medical education smartphone technology Figures Figure 1 Figure 2 Figure 3 Introduction Nearly all healthcare professionals use smartphone devices, and there is an increasing number of smartphone applications that are geared towards the medical field. Despite this, relatively little is known about how health professionals use smartphone applications in their daily practice, how it affects their performance of patient care, reliability of the medical information in these applications, and ultimately patient outcomes. Previous studies have assessed the use of smartphone applications in Accreditation Council for Graduate Medical Education (ACGME) training programs and found that up to 85% of participants (trainees through attending physicians) used a smartphone with their most common application usage being drug guidelines followed by medical calculators and coding and billing apps. 1 A study of smartphone applications in neurology found a variety of applications that could be grouped into the following categories: references, academic, communication, classroom, localization/examination, documentation/administrative, monitoring/analytics, and advising/teaching. 2 Recently, a study assessing the trend of medical application use in neurology found an increasing trend in the use of smartphone applications as medical instruments such as using a smartphone to identify seizure. 3,4 Other studies have shown that neurology trainees use their smartphones more frequently than attending physicians and often use them for patient-care related activities. 5 This suggests that providers and practitioners with less experience reach for their smartphone more often as a resource to aide in clinical decision-making. It is vital that medical applications used for reference must be evidence-based. However, to our knowledge there have not been studies validating the efficacy of smartphone applications in medical practice despite the nearly universal use. We sought to identify and define smartphone use during acute neurological emergencies, using simulated cases to standardize the clinical events. We hypothesized novice participants would use smartphones more often, and that smartphone use would be associated with better clinical performance as participants would be able to have a clinical reference guide. Methods Setting and Study Design This is a retrospective review of a prospective, single-center simulation study performed between February 2018 and October 2023. It was conducted by three neurointensivists (MBP, WWC, and NAM). Each simulation was recorded for video review by the raters. Participants ranged from neurology sub-interns to attending physicians. They voluntarily participated in the study as individuals and did not receive any information about the cases prior to participating. All participants completed a questionnaire prior to the simulation cases which included questions regarding demographics, level of training, primary work environment, prior experience, and self-rated proficiency regarding neurological emergencies. Each simulation lasted between 20–30 minutes. In a pre-briefing, participants were instructed to approach the cases just as they would in real life and to use any resources that they would normally use to assist them, including a smartphone. After each scenario was completed, participants partook in a debriefing session with the attending neurointensivist that ran the simulation. The debriefing session followed the Debriefing with Good Judgment approach to help participants reflect on their behavior during the simulation leading to behavioral change. 6 Simulation scenarios took place while trainees were on their neurocritical care rotation in which they were excused from clinical duties. They did not receive financial compensation for their participation. Their performance in the simulation scenarios had no bearing on clinical performance grading or evaluations. Clinical Simulation Case Scenarios Clinical simulation cases and critical action checklists to assess performance during the simulations were developed using a modified Delphi method as previously described. 7 Cases included hemorrhagic conversion of acute ischemic stroke complicated by hemorrhagic transformation, status epilepticus in the setting of viral encephalitis, traumatic brain injury followed by herniation syndrome, subarachnoid hemorrhage with early neuro-worsening, cardiac arrest followed by status epilepticus, and spinal cord compression. Critical action item checklists were based on the Emergency Neurological Life Support (ENLS) protocols and cross referenced with relevant guidelines from the American Heart Association, American Academy of Neurosurgeons / Congress of Neurological Surgeons, the Brain Trauma Foundation, the American Epilepsy Society, the Infectious Disease Society of America, and the Neurocritical Care Society as previously described. 7 Simulator and Simulation Environment We used the SimMan 3G manikin (Laerdal; Wappinger Falls, NY) that has both neurological and physiological signs including pupillary constriction, respiratory patterns, and tonic/clonic movements representing seizure. The manikin speaks through an internal speaker, and there is a nurse embedded at bedside who can offer other findings of the neurological examination including eye movements, motor, sensory, and cerebellar functioning. A monitor displays pertinent data related to the case including lab data, electrocardiogram (ECG), and neurodiagnostics including various intracranial imaging studies and electroencephalogram (EEG). There is also a bedside monitor which displays vital sign data including telemetry, oxygen saturation, arterial blood pressure, and temperature. The simulation room represented either an emergency department (ED) room or a room on a medical/surgical floor. All equipment and medications needed to perform the scenarios were available to participants including rapid sequence intubation medications, anti-seizure medications, hyperosmolar therapy, and intravenous fluids though not readily in view. Equipment for airway management included bag valve mask, nasal cannula, non-rebreather, endotracheal tubes, Bougie, laryngoscope, and a ventilator. An embedded participant was available to manage the airway for those participants not trained in airway management. Participants also had access to a lumbar puncture kit and continuous video EEG. In a pre-briefing, participants were oriented to the simulator and environment by the simulation operators (MBP, WWC, NAM) using a pre-briefing script. Participants were asked to verbalize any orders, components of the physical or neurological examinations, and any diagnostics. We asked participants to perform all expected skills that they would perform within their skillset and level of training. Consultations could be placed from a telephone in the simulation room with the simulation operator acting as the consultant. The embedded nurse, who had constant communication with the simulation operator through an earpiece, was in the room with the participant through the entirety of the simulation scenario. All smartphone use during the simulations were recorded either in realtime during the simulation or via video recording by the raters, MBP and AAA. Outcomes The primary outcome was the percent of participants using their smartphone during the simulations. Secondary outcomes included if participants arrived at the correct answer or critical action when using their smart phone based on use of their smartphone and then subsequent actions, smartphone use purpose divided into 4 categories including medication dosing, management guidelines, hospital/society protocols, and standardized exam scales including the National Institute of Health Stroke Scale (NIHSS) and Glasgow Coma Scale (GCS). Additionally, we assessed if participant level was correlated with use of smartphones during the simulation. To do this, we stratified participants into 3 levels of training: novice, intermediate, and advanced. Novice participants including neurology sub-interns and neurosurgery interns who have minimal experience with neurological emergencies at our institution. Intermediate participants included neurology residents and all critical care fellows besides neurocritical care fellows as they have rotated through the neurointensive care unit at our institution and have experience in initial work-up through consultation of a multitude of neuro-emergencies in our institution. Advanced participants included neurocritical care fellows, stroke fellows, and attending physicians in neurocritical care and vascular neurology who were all board-certified in neurology with all attendings having sub-specialty training in their respective fields. We also assessed performance during the simulations using a checklist of critical action items as previously described. 7 Statistical Analysis We reported descriptive statistics as mean (standard deviation [SD]) for continuous variables and counts and frequencies for categorical variables. We performed a Chi-square test to assess differences in smart phone use based on level of training. We then used a t-test to determine if there was a difference in performance between participants who used smartphones and those who did not. Standard Protocol Approvals, Registration, and Patient Consents This study was approved by the University of Maryland, Baltimore, Institutional Review Board (IRB), which waived the need for informed consent. Data Availability Statement Upon reasonable request, the data that support the findings of the present study are available from the corresponding author, MBP. Results One hundred and three participants took park in the 245 simulated cases including 96 trainees and 7 attending physicians (Table 1). Primary and Secondary Outcomes Smartphones were used in 109 simulated neurological emergencies (45%). Of the 109 simulation scenarios in which smartphones were used, we were able to determine whether smartphone use resulted in obtaining the correct answer in 88 discrete smartphone uses. Participants obtained the correct answer in 64/88 (73%) uses. When assessing level of training, advanced participants obtained the correct answer 6/6 (100%) times whereas intermediate and novice participants obtained the correct answer 53/72 (74%) and 6/10 (60%) times, respectively. When able, we identified for the purpose of smartphone use through direct observation or during debriefing. It was most frequently used for medication dosing (102 occurrences) followed by management guidelines (52 occurrences), standardized exam scales (13 occurrences), and hospital protocols (11 occurrences). Intermediate participants were more likely to use their smart phones than novice or advanced participants, 53% vs. 29% and 26%, respectively, p < .05. Intermediate participants who used smartphones performed similarly to participants who did not use smartphones (smartphone users’ mean score [standard deviation (SD)] = 12.3 (2.9) vs. non-smartphone users’ mean score = 12.9 (2.7), p = .85). In the cohort overall, participants who did not use their smartphones performed better than participants who used their smartphones (non-smartphone users’ mean score [standard deviation (SD)] = 13.0 (4.1) vs. smartphone users’ mean score = 12.3 (3.4), p = .045). Discussion We assessed trainee and attending smartphone use during neurological emergencies using high-fidelity simulation. We found that many participants used their smartphones, and those that were most likely to use smartphones were intermediate trainees as opposed to novice trainees or advanced participants. Participants were most likely to use their smartphones for medication dosing. Of the group that used their smartphones the most (the intermediate group), clinical performance was not superior to trainees that did not use smartphones. Participants most likely to use their smartphones during low frequency, high acuity events were mid-level trainees. This is congruous with previous studies that have shown a trend in which trainees as opposed to attending physicians across varying disciplines are more likely to use their smartphone in clinical and educational contexts. 8 We found that novice trainees often did not use their smartphones nearly as much as intermediate trainees. Perhaps they lacked the baseline knowledge to recognize what they needed to use their smartphones for during the clinical scenario. Expert participants used their smartphones the least. Thus, trainees who are most likely to see patients as the first patient interaction prior to discussing or seeing patients with more senior providers were the ones most likely to use their smartphones and in need of reputable and evidence-based clinical management tools. Consequently, these trainees have the potential to benefit most. However, despite greater use of smartphones in this group, our study showed that use of a smartphone during the simulation encounter did not confer any benefit in their simulated clinical performance. We hypothesize two potential explanations for this. First, the resources accessed may have provided incorrect information from unverified sources. Though specific smartphone resources that participants used in the simulation were not captured as part of data collection, we did query participants during the debriefing sessions. Participants reported a variety of resources ranging from a simple query of search engines such as Google to more targeted resources such as Micromedex or guidelines and management protocols on trusted sites (Emcrit.org was often sited particularly from critical care fellows). In our debriefing sessions, we also found that resources varied by participant level with more senior trainees accessing national guidelines that are evidence-based and peer-reviewed literature more than junior trainees who often utilized the first hit from a search engine without vetting. Second, even when they received factually correct information from the smartphone query, intermediate-level providers struggled with matching the correct information to the appropriate setting. For example, when searching for the appropriate dose of Tenecteplase for acute ischemic stroke, many trainees gave the dose for acute myocardial infarction, as that is the first dosing that comes up when using a search engine. This could lead to both overdosing or underdosing and resultant complications. This is a common problem found in hospitalized patients where medication errors occur at a rate of up to 90%. 9 Smartphone use may, in this context, yield a false sense of confidence to non-expert learners. Regardless of whether it was due to finding incorrect information or failing to correctly integrate the information received, our study demonstrated that smartphone queries resulted in an incorrect medication dose or treatment plan 27% of the time. Further studies should focus on development of smartphone applications that are evidence-based, regularly updated, and easily accessible to users. Additionally, given the reported use of smartphones by trainees in the literature 1 and in our study, further initiatives should be made to incorporate smartphone application training into medical education. Our study has several limitations. It is a single center study that may not represent trainees and attending physicians at large. We relied on simulated cases as opposed to real-life neurological emergencies; however, doing so allowed us to standardize the clinical scenarios among participants. Simulated cases have previously been used to study clinical behavior 10 and the cases used in this study have published validity evidence. 7,11–13 We did not gather a comprehensive list of all the resources used by participants nor did we have a way of digitally tracking smartphone use; however, we did explore smartphone use during debriefings. Future studies should delve into the reliability of resources available to the medical community. Conclusion Smartphone use was frequent during a variety of neurological emergencies, especially among intermediate participants. Our findings suggest a need for development of an evidence-based smartphone application for neurological emergencies, and future research should assess clinical performance with and without use of this evidence-based application. Declarations This manuscript complies with all instructions for authors. This manuscript has not been published elsewhere and is not under consideration by another journal. There was adherence to ethical guidelines during completion of this research, and this study was approved by the University of Maryland, Baltimore, Institutional Review Board (IRB), which waived the need for informed consent. The STROBE checklist was followed. Funding : This study was funded by the Faculty Innovation in Education Award from the American Board of Psychiatry and Neurology (Dr. Morris). Disclosures : The authors have no relevant disclosures to report. CRediT Author Statement and Contributions: MBP : Conceptualization, Methodology, Investigation, Data Collection, Formal Analysis, Writing - Original Draft, Writing Review and Editing AAA : Data Collection, Writing - Original Draft, Writing Review and Editing WWC : Data Collection, Writing - Original Draft, Writing Review and Editing BN : Data Collection, Writing - Original Draft, Writing Review and Editing CA: Writing - Original Draft, Writing Review and Editing AA : Writing - Original Draft, Writing Review and Editing ST : Writing - Original Draft, Writing Review and Editing NAM : Conceptualization, Methodology, Writing - Original Draft, Writing Review and Editing, Supervision, Project Administration All authors read and approved the final version. References Franko OI, Tirrell TF. Smartphone app use among medical providers in ACGME training programs. J Med Syst. 2012;36(5):3135–9. 10.1007/s10916-011-9798-7 . (In eng). Cohen AB, Nahed BV, Sheth KN. Mobile medical applications in neurology. Neurol Clin Pract. 2013;3(1):52–60. 10.1212/CPJ.0b013e318283ff4f . (In eng). Tatum WO, Hirsch LJ, Gelfand MA, et al. Assessment of the Predictive Value of Outpatient Smartphone Videos for Diagnosis of Epileptic Seizures. JAMA Neurol. 2020;77(5):593–600. 10.1001/jamaneurol.2019.4785 . (In eng). Tatum WOt, Acton EK, Freund B, de la Cruz Gutierrez M, Feyissa AM, Brigham T. Smartphone use in Neurology: a bibliometric analysis and visualization of things to come. Front Neurol. 2023;14:1237839. 10.3389/fneur.2023.1237839 . (In eng). Zeiger W, DeBoer S, Probasco J. Patterns and Perceptions of Smartphone Use Among Academic Neurologists in the United States: Questionnaire Survey. JMIR Mhealth Uhealth. 2020;8(12):e22792. 10.2196/22792 . (In eng). Rudolph JW, Simon R, Dufresne RL, Raemer DB. There's no such thing as nonjudgmental debriefing: a theory and method for debriefing with good judgment. Simul Healthc. 2006;1(1):49–55. 10.1097/01266021-200600110-00006 . (In eng). Morris NA, Chang W, Tabatabai A, et al. Development of Neurological Emergency Simulations for Assessment: Content Evidence and Response Process. Neurocrit Care. 2021;35(2):389–96. 10.1007/s12028-020-01176-y . (In eng). Smith S, Houghton A, Mockeridge B, van Zundert A. The Internet, Apps, and the Anesthesiologist. Healthc (Basel). 2023;11(22). 10.3390/healthcare11223000 . (In eng). Castro R, Aguiar LB, Volpe CRG, et al. Determining Medication Errors in an Adult Intensive Care Unit. Int J Environ Res Public Health. 2023;20(18). 10.3390/ijerph20186788 . (In eng). Gawande AA, Arriaga AF. A simulation-based trial of surgical-crisis checklists. N Engl J Med. 2013;368(15):1460. 10.1056/NEJMc1301994 . (In eng). Ali AA, Chang WW, Tabatabai A, et al. Simulation-based assessment of trainee's performance in post-cardiac arrest resuscitation. Resusc Plus. 2022;10:100233. 10.1016/j.resplu.2022.100233 . (In eng). Pergakis MB, Chang W-TW, Gutierrez CA et al. Education research: high-fidelity simulation to evaluate diagnostic reasoning reveals failure to detect viral encephalitis in medical trainees. Neurology: Educ 2022;1(2). Pergakis MB, Chang WW, Tabatabai A, et al. Simulation-Based Assessment of Graduate Neurology Trainees' Performance Managing Acute Ischemic Stroke. Neurology. 2021;97(24):e2414–22. 10.1212/wnl.0000000000012972 . (In eng). Tables Table 1. N = 103 Age, mean (SD) 31.8 (4.8) Female, n (%) 41 (40) Level of training, n (%) Neurology sub-intern Neurosurgery intern PGY-2 neurology resident PGY-3 neurology resident PGY-4 neurology resident Medical critical care fellow Emergency medicine critical care fellow Surgical critical care fellow Neurocritical care fellow Neurocritical care attending physician Stroke fellow Stroke attending physician 11 (11) 4 (4) 30 (29) 6 (6) 5 (5) 17 (17) 10 (10) 1 (1) 11 (10) 5 (5) 1 (1) 2 (2) Primary work location if available, n (%) Emergency department Neurocritical care unit Medical intensive care unit Surgical intensive care unit Neurology Ward Not applicable 1 (1) 20 (20) 25 (24) 2 (2) 44 (43) 11 (11) Total number of simulation cases, n (%) Hemorrhagic conversion of stroke HSV encephalitis and status epilepticus Epidural hematoma with spinal cord injury Subarachnoid hemorrhage with rebleed Cardiac arrest and status epilepticus Spinal cord compression 245 57 (23) 71 (29) 40 (16) 4 (2) 69 (28) 4 (2) ENLS certification, n (%) 41 (40) Prior participation in medical simulation, n (%) 90 (87) Cite Share Download PDF Status: Published Journal Publication published 20 May, 2024 Read the published version in Neurocritical Care → Version 1 posted Reviewers agreed at journal 02 Feb, 2024 Reviewers invited by journal 02 Feb, 2024 Editor invited by journal 01 Feb, 2024 First submitted to journal 31 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3914951","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270866502,"identity":"17ffeb66-c441-41ea-beec-a575197623dc","order_by":0,"name":"Melissa B. Pergakis","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-4526-4176","institution":"University of Maryland School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Melissa","middleName":"B.","lastName":"Pergakis","suffix":""},{"id":270866503,"identity":"2a759ca9-bd58-43de-b50b-374217185ddb","order_by":1,"name":"Afrah A. Ali","email":"","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Afrah","middleName":"A.","lastName":"Ali","suffix":""},{"id":270866504,"identity":"b2a4de71-b555-4467-a115-35f084369f5d","order_by":2,"name":"WanTsu Wendy Chang","email":"","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"WanTsu","middleName":"Wendy","lastName":"Chang","suffix":""},{"id":270866505,"identity":"532eae2e-0f7e-4b1e-b8cc-e6b73578e16b","order_by":3,"name":"Benjamin Neustein","email":"","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Neustein","suffix":""},{"id":270866506,"identity":"1314c6e9-7020-4ecb-acd1-3321a96b4c05","order_by":4,"name":"Casey Albin","email":"","orcid":"","institution":"Emory University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Casey","middleName":"","lastName":"Albin","suffix":""},{"id":270866507,"identity":"4fd946c7-0c61-41c5-b644-a063d0e38366","order_by":5,"name":"Aimee Aysenne","email":"","orcid":"","institution":"Tulane University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Aimee","middleName":"","lastName":"Aysenne","suffix":""},{"id":270866508,"identity":"5ad6b1a7-ecd3-416b-8a33-5f59a4619bd6","order_by":6,"name":"Samuel A. Tisherman","email":"","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"A.","lastName":"Tisherman","suffix":""},{"id":270866509,"identity":"8c1cb51c-294c-4a6c-8969-cf4ab7f078ae","order_by":7,"name":"Nicholas A. Morris","email":"","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Nicholas","middleName":"A.","lastName":"Morris","suffix":""}],"badges":[],"createdAt":"2024-01-31 19:50:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3914951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3914951/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12028-024-02000-7","type":"published","date":"2024-05-21T00:39:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50758046,"identity":"3949644d-8130-44be-a6a9-7d31d33a45f5","added_by":"auto","created_at":"2024-02-06 20:11:42","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":145240,"visible":true,"origin":"","legend":"\u003cp\u003eShows the total number of smartphone uses by identification of use if able to ascertained.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3914951/v1/1a5f8e1c3719da7d070c2d5c.jpeg"},{"id":50758045,"identity":"b888f1db-2908-461c-be68-0bf23206db17","added_by":"auto","created_at":"2024-02-06 20:11:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48896,"visible":true,"origin":"","legend":"\u003cp\u003ePercent of participants using smartphones by level of training.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3914951/v1/69efc35254cadc3610b1544f.png"},{"id":50758048,"identity":"7f2449f1-5bb3-4bdb-993b-530006dc509d","added_by":"auto","created_at":"2024-02-06 20:11:42","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141201,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance of intermediate participants comparing those who used smart phones to those that did not use smartphones.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3914951/v1/a77c83e38cfd54e87ea73c71.jpeg"},{"id":56897836,"identity":"0ae2b8db-5a31-4571-8afb-90096429dd35","added_by":"auto","created_at":"2024-05-22 00:39:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":701522,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3914951/v1/06d1419e-396d-4e35-9595-0304b791dcf6.pdf"}],"financialInterests":"","formattedTitle":"Smartphone Use in the Management of Neurological Emergencies: A Simulation-based Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNearly all healthcare professionals use smartphone devices, and there is an increasing number of smartphone applications that are geared towards the medical field. Despite this, relatively little is known about how health professionals use smartphone applications in their daily practice, how it affects their performance of patient care, reliability of the medical information in these applications, and ultimately patient outcomes. Previous studies have assessed the use of smartphone applications in Accreditation Council for Graduate Medical Education (ACGME) training programs and found that up to 85% of participants (trainees through attending physicians) used a smartphone with their most common application usage being drug guidelines followed by medical calculators and coding and billing apps.\u003csup\u003e1\u003c/sup\u003e A study of smartphone applications in neurology found a variety of applications that could be grouped into the following categories: references, academic, communication, classroom, localization/examination, documentation/administrative, monitoring/analytics, and advising/teaching.\u003csup\u003e2\u003c/sup\u003e Recently, a study assessing the trend of medical application use in neurology found an increasing trend in the use of smartphone applications as medical instruments such as using a smartphone to identify seizure.\u003csup\u003e3,4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOther studies have shown that neurology trainees use their smartphones more frequently than attending physicians and often use them for patient-care related activities.\u003csup\u003e5\u003c/sup\u003e This suggests that providers and practitioners with less experience reach for their smartphone more often as a resource to aide in clinical decision-making. It is vital that medical applications used for reference must be evidence-based. However, to our knowledge there have not been studies validating the efficacy of smartphone applications in medical practice despite the nearly universal use.\u003c/p\u003e \u003cp\u003eWe sought to identify and define smartphone use during acute neurological emergencies, using simulated cases to standardize the clinical events. We hypothesized novice participants would use smartphones more often, and that smartphone use would be associated with better clinical performance as participants would be able to have a clinical reference guide.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSetting and Study Design\u003c/h2\u003e \u003cp\u003e This is a retrospective review of a prospective, single-center simulation study performed between February 2018 and October 2023. It was conducted by three neurointensivists (MBP, WWC, and NAM). Each simulation was recorded for video review by the raters. Participants ranged from neurology sub-interns to attending physicians. They voluntarily participated in the study as individuals and did not receive any information about the cases prior to participating. All participants completed a questionnaire prior to the simulation cases which included questions regarding demographics, level of training, primary work environment, prior experience, and self-rated proficiency regarding neurological emergencies. Each simulation lasted between 20\u0026ndash;30 minutes. In a pre-briefing, participants were instructed to approach the cases just as they would in real life and to use any resources that they would normally use to assist them, including a smartphone. After each scenario was completed, participants partook in a debriefing session with the attending neurointensivist that ran the simulation. The debriefing session followed the Debriefing with Good Judgment approach to help participants reflect on their behavior during the simulation leading to behavioral change.\u003csup\u003e6\u003c/sup\u003e Simulation scenarios took place while trainees were on their neurocritical care rotation in which they were excused from clinical duties. They did not receive financial compensation for their participation. Their performance in the simulation scenarios had no bearing on clinical performance grading or evaluations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical Simulation Case Scenarios\u003c/h2\u003e \u003cp\u003eClinical simulation cases and critical action checklists to assess performance during the simulations were developed using a modified Delphi method as previously described.\u003csup\u003e7\u003c/sup\u003e Cases included hemorrhagic conversion of acute ischemic stroke complicated by hemorrhagic transformation, status epilepticus in the setting of viral encephalitis, traumatic brain injury followed by herniation syndrome, subarachnoid hemorrhage with early neuro-worsening, cardiac arrest followed by status epilepticus, and spinal cord compression. Critical action item checklists were based on the Emergency Neurological Life Support (ENLS) protocols and cross referenced with relevant guidelines from the American Heart Association, American Academy of Neurosurgeons / Congress of Neurological Surgeons, the Brain Trauma Foundation, the American Epilepsy Society, the Infectious Disease Society of America, and the Neurocritical Care Society as previously described.\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSimulator and Simulation Environment\u003c/h2\u003e \u003cp\u003eWe used the SimMan 3G manikin (Laerdal; Wappinger Falls, NY) that has both neurological and physiological signs including pupillary constriction, respiratory patterns, and tonic/clonic movements representing seizure. The manikin speaks through an internal speaker, and there is a nurse embedded at bedside who can offer other findings of the neurological examination including eye movements, motor, sensory, and cerebellar functioning. A monitor displays pertinent data related to the case including lab data, electrocardiogram (ECG), and neurodiagnostics including various intracranial imaging studies and electroencephalogram (EEG). There is also a bedside monitor which displays vital sign data including telemetry, oxygen saturation, arterial blood pressure, and temperature.\u003c/p\u003e \u003cp\u003eThe simulation room represented either an emergency department (ED) room or a room on a medical/surgical floor. All equipment and medications needed to perform the scenarios were available to participants including rapid sequence intubation medications, anti-seizure medications, hyperosmolar therapy, and intravenous fluids though not readily in view. Equipment for airway management included bag valve mask, nasal cannula, non-rebreather, endotracheal tubes, Bougie, laryngoscope, and a ventilator. An embedded participant was available to manage the airway for those participants not trained in airway management. Participants also had access to a lumbar puncture kit and continuous video EEG.\u003c/p\u003e \u003cp\u003eIn a pre-briefing, participants were oriented to the simulator and environment by the simulation operators (MBP, WWC, NAM) using a pre-briefing script. Participants were asked to verbalize any orders, components of the physical or neurological examinations, and any diagnostics. We asked participants to perform all expected skills that they would perform within their skillset and level of training. Consultations could be placed from a telephone in the simulation room with the simulation operator acting as the consultant. The embedded nurse, who had constant communication with the simulation operator through an earpiece, was in the room with the participant through the entirety of the simulation scenario.\u003c/p\u003e \u003cp\u003eAll smartphone use during the simulations were recorded either in realtime during the simulation or via video recording by the raters, MBP and AAA.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe primary outcome was the percent of participants using their smartphone during the simulations. Secondary outcomes included if participants arrived at the correct answer or critical action when using their smart phone based on use of their smartphone and then subsequent actions, smartphone use purpose divided into 4 categories including medication dosing, management guidelines, hospital/society protocols, and standardized exam scales including the National Institute of Health Stroke Scale (NIHSS) and Glasgow Coma Scale (GCS). Additionally, we assessed if participant level was correlated with use of smartphones during the simulation. To do this, we stratified participants into 3 levels of training: novice, intermediate, and advanced. Novice participants including neurology sub-interns and neurosurgery interns who have minimal experience with neurological emergencies at our institution. Intermediate participants included neurology residents and all critical care fellows besides neurocritical care fellows as they have rotated through the neurointensive care unit at our institution and have experience in initial work-up through consultation of a multitude of neuro-emergencies in our institution. Advanced participants included neurocritical care fellows, stroke fellows, and attending physicians in neurocritical care and vascular neurology who were all board-certified in neurology with all attendings having sub-specialty training in their respective fields. We also assessed performance during the simulations using a checklist of critical action items as previously described.\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe reported descriptive statistics as mean (standard deviation [SD]) for continuous variables and counts and frequencies for categorical variables. We performed a Chi-square test to assess differences in smart phone use based on level of training. We then used a t-test to determine if there was a difference in performance between participants who used smartphones and those who did not.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStandard Protocol Approvals, Registration, and Patient Consents\u003c/h2\u003e \u003cp\u003eThis study was approved by the University of Maryland, Baltimore, Institutional Review Board (IRB), which waived the need for informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eUpon reasonable request, the data that support the findings of the present study are available from the corresponding author, MBP.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOne hundred and three participants took park in the 245 simulated cases including 96 trainees and 7 attending physicians (Table\u0026nbsp;1).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrimary and Secondary Outcomes\u003c/h2\u003e \u003cp\u003eSmartphones were used in 109 simulated neurological emergencies (45%). Of the 109 simulation scenarios in which smartphones were used, we were able to determine whether smartphone use resulted in obtaining the correct answer in 88 discrete smartphone uses. Participants obtained the correct answer in 64/88 (73%) uses. When assessing level of training, advanced participants obtained the correct answer 6/6 (100%) times whereas intermediate and novice participants obtained the correct answer 53/72 (74%) and 6/10 (60%) times, respectively.\u003c/p\u003e \u003cp\u003eWhen able, we identified for the purpose of smartphone use through direct observation or during debriefing. It was most frequently used for medication dosing (102 occurrences) followed by management guidelines (52 occurrences), standardized exam scales (13 occurrences), and hospital protocols (11 occurrences). Intermediate participants were more likely to use their smart phones than novice or advanced participants, 53% vs. 29% and 26%, respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;.05. Intermediate participants who used smartphones performed similarly to participants who did not use smartphones (smartphone users\u0026rsquo; mean score [standard deviation (SD)]\u0026thinsp;=\u0026thinsp;12.3 (2.9) vs. non-smartphone users\u0026rsquo; mean score\u0026thinsp;=\u0026thinsp;12.9 (2.7), p\u0026thinsp;=\u0026thinsp;.85). In the cohort overall, participants who did not use their smartphones performed better than participants who used their smartphones (non-smartphone users\u0026rsquo; mean score [standard deviation (SD)]\u0026thinsp;=\u0026thinsp;13.0 (4.1) vs. smartphone users\u0026rsquo; mean score\u0026thinsp;=\u0026thinsp;12.3 (3.4), p\u0026thinsp;=\u0026thinsp;.045).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe assessed trainee and attending smartphone use during neurological emergencies using high-fidelity simulation. We found that many participants used their smartphones, and those that were most likely to use smartphones were intermediate trainees as opposed to novice trainees or advanced participants. Participants were most likely to use their smartphones for medication dosing. Of the group that used their smartphones the most (the intermediate group), clinical performance was not superior to trainees that did not use smartphones.\u003c/p\u003e \u003cp\u003eParticipants most likely to use their smartphones during low frequency, high acuity events were mid-level trainees. This is congruous with previous studies that have shown a trend in which trainees as opposed to attending physicians across varying disciplines are more likely to use their smartphone in clinical and educational contexts.\u003csup\u003e8\u003c/sup\u003e We found that novice trainees often did not use their smartphones nearly as much as intermediate trainees. Perhaps they lacked the baseline knowledge to recognize what they needed to use their smartphones for during the clinical scenario. Expert participants used their smartphones the least. Thus, trainees who are most likely to see patients as the first patient interaction prior to discussing or seeing patients with more senior providers were the ones most likely to use their smartphones and in need of reputable and evidence-based clinical management tools. Consequently, these trainees have the potential to benefit most.\u003c/p\u003e \u003cp\u003eHowever, despite greater use of smartphones in this group, our study showed that use of a smartphone during the simulation encounter did not confer any benefit in their simulated clinical performance. We hypothesize two potential explanations for this. First, the resources accessed may have provided incorrect information from unverified sources. Though specific smartphone resources that participants used in the simulation were not captured as part of data collection, we did query participants during the debriefing sessions. Participants reported a variety of resources ranging from a simple query of search engines such as Google to more targeted resources such as Micromedex or guidelines and management protocols on trusted sites (Emcrit.org was often sited particularly from critical care fellows). In our debriefing sessions, we also found that resources varied by participant level with more senior trainees accessing national guidelines that are evidence-based and peer-reviewed literature more than junior trainees who often utilized the first hit from a search engine without vetting.\u003c/p\u003e \u003cp\u003eSecond, even when they received factually correct information from the smartphone query, intermediate-level providers struggled with matching the correct information to the appropriate setting. For example, when searching for the appropriate dose of Tenecteplase for acute ischemic stroke, many trainees gave the dose for acute myocardial infarction, as that is the first dosing that comes up when using a search engine. This could lead to both overdosing or underdosing and resultant complications. This is a common problem found in hospitalized patients where medication errors occur at a rate of up to 90%. \u003csup\u003e9\u003c/sup\u003e Smartphone use may, in this context, yield a false sense of confidence to non-expert learners.\u003c/p\u003e \u003cp\u003eRegardless of whether it was due to finding incorrect information or failing to correctly integrate the information received, our study demonstrated that smartphone queries resulted in an incorrect medication dose or treatment plan 27% of the time. Further studies should focus on development of smartphone applications that are evidence-based, regularly updated, and easily accessible to users. Additionally, given the reported use of smartphones by trainees in the literature\u003csup\u003e1\u003c/sup\u003e and in our study, further initiatives should be made to incorporate smartphone application training into medical education.\u003c/p\u003e \u003cp\u003eOur study has several limitations. It is a single center study that may not represent trainees and attending physicians at large. We relied on simulated cases as opposed to real-life neurological emergencies; however, doing so allowed us to standardize the clinical scenarios among participants. Simulated cases have previously been used to study clinical behavior\u003csup\u003e10\u003c/sup\u003e and the cases used in this study have published validity evidence.\u003csup\u003e7,11\u0026ndash;13\u003c/sup\u003e We did not gather a comprehensive list of all the resources used by participants nor did we have a way of digitally tracking smartphone use; however, we did explore smartphone use during debriefings. Future studies should delve into the reliability of resources available to the medical community.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSmartphone use was frequent during a variety of neurological emergencies, especially among intermediate participants. Our findings suggest a need for development of an evidence-based smartphone application for neurological emergencies, and future research should assess clinical performance with and without use of this evidence-based application.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis manuscript complies with all instructions for authors.\u003c/p\u003e\n\u003cp\u003eThis manuscript has not been published elsewhere and is not under consideration by another journal.\u003c/p\u003e\n\u003cp\u003eThere was adherence to ethical guidelines during completion of this research, and this study was approved by the University of Maryland, Baltimore, Institutional Review Board (IRB), which waived the need for informed consent.\u003c/p\u003e\n\u003cp\u003eThe STROBE checklist was followed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study was funded by the Faculty Innovation in Education Award from the American Board of Psychiatry and Neurology (Dr. Morris).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e: The authors have no relevant disclosures to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT Author Statement and Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMBP\u003c/strong\u003e: Conceptualization, Methodology, Investigation, Data Collection, Formal Analysis, Writing - Original Draft, Writing Review and Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAAA\u003c/strong\u003e: Data Collection, Writing - Original Draft, Writing Review and Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWWC\u003c/strong\u003e: Data Collection, Writing - Original Draft, Writing Review and Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBN\u003c/strong\u003e: Data Collection, Writing - Original Draft, Writing Review and Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCA:\u003c/strong\u003e Writing - Original Draft, Writing Review and Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e: Writing - Original Draft, Writing Review and Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eST\u003c/strong\u003e: Writing - Original Draft, Writing Review and Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNAM\u003c/strong\u003e: Conceptualization, Methodology, Writing - Original Draft, Writing Review and Editing, Supervision, Project Administration\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAll authors read and approved the final version.\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFranko OI, Tirrell TF. Smartphone app use among medical providers in ACGME training programs. J Med Syst. 2012;36(5):3135\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10916-011-9798-7\u003c/span\u003e\u003cspan address=\"10.1007/s10916-011-9798-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (In eng).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen AB, Nahed BV, Sheth KN. Mobile medical applications in neurology. Neurol Clin Pract. 2013;3(1):52\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/CPJ.0b013e318283ff4f\u003c/span\u003e\u003cspan address=\"10.1212/CPJ.0b013e318283ff4f\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (In eng).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTatum WO, Hirsch LJ, Gelfand MA, et al. Assessment of the Predictive Value of Outpatient Smartphone Videos for Diagnosis of Epileptic Seizures. JAMA Neurol. 2020;77(5):593\u0026ndash;600. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamaneurol.2019.4785\u003c/span\u003e\u003cspan address=\"10.1001/jamaneurol.2019.4785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (In eng).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTatum WOt, Acton EK, Freund B, de la Cruz Gutierrez M, Feyissa AM, Brigham T. Smartphone use in Neurology: a bibliometric analysis and visualization of things to come. Front Neurol. 2023;14:1237839. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fneur.2023.1237839\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2023.1237839\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (In eng).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeiger W, DeBoer S, Probasco J. Patterns and Perceptions of Smartphone Use Among Academic Neurologists in the United States: Questionnaire Survey. JMIR Mhealth Uhealth. 2020;8(12):e22792. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/22792\u003c/span\u003e\u003cspan address=\"10.2196/22792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (In eng).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRudolph JW, Simon R, Dufresne RL, Raemer DB. There's no such thing as nonjudgmental debriefing: a theory and method for debriefing with good judgment. Simul Healthc. 2006;1(1):49\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/01266021-200600110-00006\u003c/span\u003e\u003cspan address=\"10.1097/01266021-200600110-00006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (In eng).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris NA, Chang W, Tabatabai A, et al. Development of Neurological Emergency Simulations for Assessment: Content Evidence and Response Process. Neurocrit Care. 2021;35(2):389\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12028-020-01176-y\u003c/span\u003e\u003cspan address=\"10.1007/s12028-020-01176-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (In eng).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith S, Houghton A, Mockeridge B, van Zundert A. The Internet, Apps, and the Anesthesiologist. Healthc (Basel). 2023;11(22). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/healthcare11223000\u003c/span\u003e\u003cspan address=\"10.3390/healthcare11223000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (In eng).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastro R, Aguiar LB, Volpe CRG, et al. Determining Medication Errors in an Adult Intensive Care Unit. Int J Environ Res Public Health. 2023;20(18). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph20186788\u003c/span\u003e\u003cspan address=\"10.3390/ijerph20186788\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (In eng).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGawande AA, Arriaga AF. A simulation-based trial of surgical-crisis checklists. N Engl J Med. 2013;368(15):1460. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMc1301994\u003c/span\u003e\u003cspan address=\"10.1056/NEJMc1301994\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (In eng).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli AA, Chang WW, Tabatabai A, et al. Simulation-based assessment of trainee's performance in post-cardiac arrest resuscitation. Resusc Plus. 2022;10:100233. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.resplu.2022.100233\u003c/span\u003e\u003cspan address=\"10.1016/j.resplu.2022.100233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (In eng).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePergakis MB, Chang W-TW, Gutierrez CA et al. Education research: high-fidelity simulation to evaluate diagnostic reasoning reveals failure to detect viral encephalitis in medical trainees. Neurology: Educ 2022;1(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePergakis MB, Chang WW, Tabatabai A, et al. Simulation-Based Assessment of Graduate Neurology Trainees' Performance Managing Acute Ischemic Stroke. Neurology. 2021;97(24):e2414\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/wnl.0000000000012972\u003c/span\u003e\u003cspan address=\"10.1212/wnl.0000000000012972\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (In eng).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":" \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;1.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;103\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.8 (4.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of training, n (%)\u003c/p\u003e \u003cp\u003eNeurology sub-intern\u003c/p\u003e \u003cp\u003eNeurosurgery intern\u003c/p\u003e \u003cp\u003ePGY-2 neurology resident\u003c/p\u003e \u003cp\u003ePGY-3 neurology resident\u003c/p\u003e \u003cp\u003ePGY-4 neurology resident\u003c/p\u003e \u003cp\u003eMedical critical care fellow\u003c/p\u003e \u003cp\u003eEmergency medicine critical care fellow\u003c/p\u003e \u003cp\u003eSurgical critical care fellow\u003c/p\u003e \u003cp\u003eNeurocritical care fellow\u003c/p\u003e \u003cp\u003eNeurocritical care attending physician\u003c/p\u003e \u003cp\u003eStroke fellow\u003c/p\u003e \u003cp\u003eStroke attending physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (11)\u003c/p\u003e \u003cp\u003e4 (4)\u003c/p\u003e \u003cp\u003e30 (29)\u003c/p\u003e \u003cp\u003e6 (6)\u003c/p\u003e \u003cp\u003e5 (5)\u003c/p\u003e \u003cp\u003e17 (17)\u003c/p\u003e \u003cp\u003e10 (10)\u003c/p\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003cp\u003e11 (10)\u003c/p\u003e \u003cp\u003e5 (5)\u003c/p\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003cp\u003e2 (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary work location if available, n (%)\u003c/p\u003e \u003cp\u003eEmergency department\u003c/p\u003e \u003cp\u003eNeurocritical care unit\u003c/p\u003e \u003cp\u003eMedical intensive care unit\u003c/p\u003e \u003cp\u003eSurgical intensive care unit\u003c/p\u003e \u003cp\u003eNeurology Ward\u003c/p\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003cp\u003e20 (20)\u003c/p\u003e \u003cp\u003e25 (24)\u003c/p\u003e \u003cp\u003e2 (2)\u003c/p\u003e \u003cp\u003e44 (43)\u003c/p\u003e \u003cp\u003e11 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of simulation cases, n (%)\u003c/p\u003e \u003cp\u003eHemorrhagic conversion of stroke\u003c/p\u003e \u003cp\u003eHSV encephalitis and status epilepticus\u003c/p\u003e \u003cp\u003eEpidural hematoma with spinal cord injury\u003c/p\u003e \u003cp\u003eSubarachnoid hemorrhage with rebleed\u003c/p\u003e \u003cp\u003eCardiac arrest and status epilepticus\u003c/p\u003e \u003cp\u003eSpinal cord compression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245\u003c/p\u003e \u003cp\u003e57 (23)\u003c/p\u003e \u003cp\u003e71 (29)\u003c/p\u003e \u003cp\u003e40 (16)\u003c/p\u003e \u003cp\u003e4 (2)\u003c/p\u003e \u003cp\u003e69 (28)\u003c/p\u003e \u003cp\u003e4 (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENLS certification, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior participation in medical simulation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"neurocritical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neca","sideBox":"Learn more about [Neurocritical Care](http://link.springer.com/journal/12028)","snPcode":"12028","submissionUrl":"https://www.editorialmanager.com/neca/default2.aspx","title":"Neurocritical Care","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Simulation, medical education, smartphone technology","lastPublishedDoi":"10.21203/rs.3.rs-3914951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3914951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Objectives\u003c/h2\u003e \u003cp\u003eSmartphone use in medicine is nearly universal despite a dearth of research assessing utility in clinical performance. We sought to identify and define smartphone use during simulated neuro-emergencies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective review of a prospective, observational, single-center simulation-based study, participants, ranging from sub-interns to attending physicians and stratified by training level (novice, intermediate, and advanced) managed a variety of neurological emergencies. The primary outcome was frequency and purpose of smartphone use. Secondary outcomes included success rate of smartphone use and performance (measured by completion of critical tasks) of participants who used smartphones vs. those that did not. In subgroup analyses we compared outcomes across participants by level of training using t-tests and Chi-square statistics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOne hundred and three participants completed 245 simulation scenarios. Smartphones were used in 109 (45%) simulations. Of participants using smartphones, 102 participants looked up medication doses, 52 participants looked up management guidelines, 11 participants looked up hospital protocols, and 13 participants used smartphones for assistance with an exam scale. Participants found the correct answer 73% of the time using smartphones. There was an association between participant level and smartphone use with intermediate participants being more likely to use their smartphones than novice or advanced participants, 53% vs. 29% and 26%, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;.05). Of the intermediate participants, those who used smartphones did not perform better during the simulation scenario than participants who did not use smartphones (smartphone users\u0026rsquo; mean score [standard deviation (SD)]\u0026thinsp;=\u0026thinsp;12.3 (2.9) vs. non-smartphone users\u0026rsquo; mean score (SD)\u0026thinsp;=\u0026thinsp;12.9 (2.7), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.85).\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eParticipants commonly used smartphones in simulated neuro-emergencies but use didn\u0026rsquo;t confer improved clinical performance. Less experienced participants were the most likely to use smartphones, were less likely to arrive at correct conclusions, and thus are the most likely to benefit from an evidence-based smartphone application for neuro-emergencies.\u003c/p\u003e","manuscriptTitle":"Smartphone Use in the Management of Neurological Emergencies: A Simulation-based Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 20:11:37","doi":"10.21203/rs.3.rs-3914951/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-02-02T20:35:21+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-02T20:10:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Neurocritical Care","date":"2024-02-01T20:04:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Neurocritical Care","date":"2024-01-31T15:16:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"neurocritical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neca","sideBox":"Learn more about [Neurocritical Care](http://link.springer.com/journal/12028)","snPcode":"12028","submissionUrl":"https://www.editorialmanager.com/neca/default2.aspx","title":"Neurocritical Care","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f32248c2-d5d2-4c59-8ac8-0c078420bbf5","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-22T00:39:26+00:00","versionOfRecord":{"articleIdentity":"rs-3914951","link":"https://doi.org/10.1007/s12028-024-02000-7","journal":{"identity":"neurocritical-care","isVorOnly":false,"title":"Neurocritical Care"},"publishedOn":"2024-05-21 00:39:26","publishedOnDateReadable":"May 21st, 2024"},"versionCreatedAt":"2024-02-06 20:11:37","video":"","vorDoi":"10.1007/s12028-024-02000-7","vorDoiUrl":"https://doi.org/10.1007/s12028-024-02000-7","workflowStages":[]},"version":"v1","identity":"rs-3914951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3914951","identity":"rs-3914951","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}