Abstracts

Development of a Real Time Quality Indicator Assessment Program for Use in the Epilepsy Monitoring Unit

Abstract number : 1.092
Submission category : 2. Interprofessional Care / Professionals in Epilepsy Care
Year : 2016
Submission ID : 194064
Source : www.aesnet.org
Presentation date : 12/3/2016 12:00:00 AM
Published date : Nov 21, 2016, 18:00 PM

Authors :
Althea Wasson, Dell Children's Medical Center, Austin, Texas; Kevin Moon, Dell Children's Medical Center, Austin, Texas; Collin Hovinga, Dell Children's Medical Center; Christie Stanworth, Dell Children's Medical Center; Maximo Seda-Flores, Dell Children'

Rationale: In 2012, the Institute of Medicine (IOM) recommended Epilepsy Centers plan, develop, and sustain quality metrics and strategies to improve high-quality healthcare (1). Clinicians have come to recognize continual quality improvement (QI) as an essential tool to improve the treatment outcome of patients with epilepsy. However, there is limited information regarding patient-centric endpoints and efficient systems to perform QI in Epilepsy Medical Units (EMUs). Methods: Patient-centric quality measures were developed by a multidisciplinary team and used to evaluate EMU performance and patient outcomes at Dell Children's Medical Center in Austin, TX. The following guidelines were evaluated in real-time: 1) electrographic seizure identification by EEG technologist < 20 seconds, 2) clinical seizure identification by EEG technologist < 10 seconds, 3) RN response time < 30 seconds, 4) first medication treatment < 5 minutes from RN response time and 5) second medication treatment < 30 minutes of first medication treatment. Utilizing a secure web application called Research Electronic Data Capture (REDCap), the following indicators were identified: 1) electrographic and clinical onset to identification, 2) push button time to RN response time, 3) RN response time to 1st medication treatment, 4) time from 1st medication treatment to 2nd medication treatment, and 5) push button to end of seizure time. The data captured were limited to 10 seizures per patient, per seizure type, and per shift. Descriptive statistics were used to describe the data. Results: Over 6 months reviewed, there were 164 seizures captured for 64 patients who were monitored in the EMU. The mean time for an EEG technologist to identify the electrographic seizure onset was 38.6 54.7 seconds. Mean clinical onset to identification 2339 seconds. Push button time to RN response time had a mean of 13.416.6 seconds. The mean for RN response time to 1st medication treatment was 6.4 5.7 minutes. The time from 1st medication treatment to 2nd medication treatment had a mean of 8 5 minutes (limited to 3 events). The overall mean for push button to end of seizure time was 3 7 minutes. Conclusions: This is an ongoing study of response times to patient seizures with continuation of advancing and improving EMU staff knowledge, patient outcomes, and quality. At our initial review, only 2/5 (40%) of our goals were met; however, there was significant variability in our first period assessments. Advancements and obstacles to this quality indicator project were noted. An enhancement to the project was an inclusion of an audit of the video EEG recordings in March 2016. Barriers identified were inconsistent data entry due to increased admissions and limited time. As the project continues, we will identify opportunities for education for all EMU staff and implement process improvement. Funding: None
Interprofessional Care