Secondary Logo

Institutional members access full text with Ovid®

Share this article on:

Comparative Analysis of Emergency Medical Service Provider Workload During Simulated Out-of-Hospital Cardiac Arrest Resuscitation Using Standard Versus Experimental Protocols and Equipment

Asselin, Nicholas, DO, MS; Choi, Bryan, MD, MPH; Pettit, Catherine C., MD; Dannecker, Max, NREMT-I; Machan, Jason T., PhD; Merck, Derek L., PhD; Merck, Lisa H., MD, MPH; Suner, Selim, MD, MS; Williams, Kenneth A., MD; Baird, Janette, PhD; Jay, Gregory D., MD, PhD; Kobayashi, Leo, MD

doi: 10.1097/SIH.0000000000000339
Empirical Investigations

Introduction Protocolized automation of critical, labor-intensive tasks for out-of-hospital cardiac arrest (OHCA) resuscitation may decrease Emergency Medical Services (EMS) provider workload. A simulation-based assessment method incorporating objective and self-reported metrics was developed and used to quantify workloads associated with standard and experimental approaches to OHCA resuscitation.

Methods Emergency Medical Services-Basic (EMT-B) and advanced life support (ALS) providers were randomized into two-provider mixed-level teams and fitted with heart rate (HR) monitors for continuous HR and energy expenditure (EE) monitoring. Subjects' resting salivary α-amylase (sAA) levels were measured along with Borg perceived exertion scores and multidimensional workload assessments (NASA-TLX). Each team engaged in the following three OHCA simulations: (1) baseline simulation in standard BLS/ALS roles; (2) repeat simulation in standard roles; and then (3) repeat simulation in reversed roles, ie, EMT-B provider performing ALS tasks. Control teams operated with standard state protocols and equipment; experimental teams used resuscitation-automating devices and accompanying goal-directed algorithmic protocol for simulations 2 and 3. Investigators video-recorded resuscitations and analyzed subjects' percent attained of maximal age-predicted HR (%mHR), EE, sAA, Borg, and NASA-TLX measurements.

Results Ten control and ten experimental teams completed the study (20 EMT-Basic; 1 EMT-Intermediate, 8 EMT-Cardiac, 11 EMT-Paramedic). Median %mHR, EE, sAA, Borg, and NASA-TLX scores did not differ between groups at rest. Overall multivariate analyses of variance did not detect significant differences; univariate analyses of variance for changes in %mHR, Borg, and NASA-TLX from resting state detected significant differences across simulations (workload reductions in experimental groups for simulations 2 and 3).

Conclusions A simulation-based OHCA resuscitation performance and workload assessment method compared protocolized automation-assisted resuscitation with standard response. During exploratory application of the assessment method, subjects using the experimental approach appeared to experience reduced levels of physical exertion and perceived workload than control subjects.

From the Department of Emergency Medicine (N.A., B.C., L.H.M., S.S., K.A.W., J.B., L.K., G.D.J.), Alpert Medical School of Brown University, Providence, RI; Emergency Department (C.C.P.), Tobey Hospital, Wareham, MA; Lifespan Medical Simulation Center (M.D.); Biostatistics Core (J.T.M.), Rhode Island Hospital; Departments of Diagnostic Imaging (D.L.M., L.H.M.) and Neurosurgery (L.H.M.), Alpert Medical School of Brown University; and School of Engineering (G.D.J.), Brown University, Providence, RI.

Reprints: Leo Kobayashi, MD, Lifespan Medical Simulation Center, Suite 106, Coro West Bldg, 1 Hoppin St, Providence, RI 02903 (e-mail: LKobayashi@lifespan.org).

The authors declare no conflict of interest.

This material is based on work supported by the institution's Department of Emergency Medicine and its medical simulation center. The opinions, findings, and conclusions or recommendations expressed in the article are those of the authors and do not necessarily reflect the views of the supporting department or center.

Research excerpts from the out-of-hospital cardiac resuscitation simulation study discussed within the article have been published in Simulation in Healthcare and were presented at the 2014 International Meeting on Simulation in Healthcare in San Francisco, California, and the 2015 International Meeting on Simulation in Healthcare in New Orleans, Louisiana.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.simulationinhealthcare.com).

© 2018 Society for Simulation in Healthcare