Chest pain is frequently encountered in the Emergency Department (ED) setting. Furthermore, ischemic heart disease is the leading cause of death among adults in the United States and the diagnosis of acute coronary syndrome (ACS) is made in approximately 10% of ED visits for chest pain. However, the evaluation of chest pain to rule out ACS requires laboratory or diagnostic tests spaced several hours apart at a minimum.[2,3] The longevity of chest pain evaluations often result in prolonged ED wait times and crowding.
Accordingly, multiple research studies have led to the creation of accelerated diagnostic protocols that use scoring criteria to expeditiously risk stratify patients for the presence of ACS. The Emergency Department Assessment of Chest Pain Score – Accelerated Diagnostic Protocol (EDACS-ADP) uses age, sex, coronary artery disease risk factors, pre-specified patient symptoms and signs, ECG, and two sets of 2-hour delta cardiac biomarkers to rule out major cardiac adverse events with a sensitivity greater than 99%. By engaging patients in the ED waiting room with a mobile health (mHealth) app to capture medical data, the efficiency of ED evaluations can be increased by harnessing otherwise lost time in the ED.
We propose a retrospective case-control study to evaluate the effect of an EDACS-ADP mHealth app which interfaces with the electronic health record (EHR) on ED length of stay (LOS). The intervention would be a custom app for the iOS operating system designed to elicit the pertinent medical history required for EDACS-ADP risk stratification from patients in the ED waiting room. The mHealth app would transmit the captured data into the EHR for scoring by clinical decision support systems. The resultant risk stratification score would be stored and displayed to clinicians in the EHR. All patients with a score indicating a high risk of ACS would trigger a best practice advisory to actively alert clinicians. Study participants would be randomly selected from a list of all ED patients presenting with chest pain from two time-intervals of interest: 6 months prior and 6 months after implementation of the EDACS-ADP mHealth app. Timestamps would be queried for each patient and extracted from the EHR into a spreadsheet to determine ED LOS. A sample size that provides the study with 90% power to discern a 10% difference in ED LOS pre-and post-implementation would be calculated. Patients presenting with an ECG concerning for an ST-segment myocardial infarction by EMS would be excluded. The primary outcome measure would be the total ED LOS of patients presenting with chest pain pre-and post-implementation of the EDACS-ADP mHealth app. We hypothesize that the post-implementation ED LOS would be less than the pre-implementation ED LOS in a statistically significant manner.
1. Marx JA, Hockberger RS, Walls RM, et al. Rosen’s emergency medicine: concepts and clinical practice. Philadelphia, PA Elsevier/Saunders; 2014.
2. Than M, Flaws D, Sanders S, et al. Development and validation of the Emergency Department Assessment of Chest pain Score and 2 h accelerated diagnostic protocol. Emerg Med Australas. 2014;26(1):34-44. doi:10.1111/1742-6723.12164.3.
3. Campbell, James R. The Five Rights of Clinical Decision Support: CDS Tools Helpful for Meeting Meaningful Use. J AHIMA. 2013;84(10):42-47 (web version updated February 2016).
DECISION SUPPORT & HOSPITAL MONITORING
Author: Elijah Bell
Coauthor(s): Elijah J. Bell III, MD (1); Christian Dameff, MD (2) 1) Department of Emergency Medicine, University of California Los Angeles, Los Angeles, CA 2) Department of Emergency Medicine, University of California San Diego, San Diego, CA
Status: Project Concept