BEGIN:VCALENDAR VERSION:2.0 PRODID:Linklings LLC BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260715T161119Z LOCATION:Murray Hill West DTSTART;TZID=America/New_York:20260324T153000 DTEND;TZID=America/New_York:20260324T155000 UID:HFESHCS_2026 International Symposium on Human Factors and Ergonomics i n Health Care_sess120_LEC300@linklings.com SUMMARY:Novel Approach to Clinical Event Debriefing Analysis: Utilizing SE IPS and FMEA Models DESCRIPTION:Carolina Rodriguez-Paras, Amber Peiffer, Lauren Heimall, Anne Ades, and Heidi Herrick (Children's Hospital of Philadelphia)\n\nBackgroun d:\n\nDebriefing is a tool commonly used after crisis intervention, psycho logical research, and experiential learning. It has also been incorporate d into the medical field after acute resuscitation events. Clinical event debriefing (CED) frequently uncovers issues and challenges that require a ttention. Categorizing CED findings allows prioritization of changes by r isk and pervasiveness. \n\nThe Special Delivery Unit (SDU) at a large urb an tertiary care children’s hospital delivers approximately 500 babies wit h congenital anomalies per year. In the SDU, CED with the entire resuscit ation team is recommended after each delivery. SDU CED collects data usin g a paper form on system concerns and improvement opportunities discussed by the newborn resuscitation team. The main goals of this study were two- fold: (1) perform a structured analysis of CED comments, categorizing them by content, risk stratification, and pervasiveness using established huma n factors and safety frameworks, and (2) develop common strategies for rem ediation of the issues based on similar content category, risk, and freque ncy.\n\nA total of 285 comments from 185 debrief forms collected between J une 2023 and August 2024 were reviewed by an interprofessional team. Team members included a human factors engineer, a clinical nurse specialist, a ttending neonatologists, and a quality improvement specialist. The forms were analyzed using the Systems Engineering Initiative for Patient Safety (SEIPS) model and the Failure Mode and Effects Analysis (FMEA) method. \n\ nThe SEIPS model analyzes the system and the interactions between tools an d technology, organization, people, tasks, and environment. The first par t of the analysis consisted of organizing the debrief form comments into c ategories according to the work system construct of SEIPS model (i.e. Tool s/Technology, Organization, People, Tasks, Environment) to provide context for understanding and improving patient safety by investigating complexit y within a healthcare system. Tools/Technology was the category with the m ost comments (45.6%, 130/285), followed by Tasks (44.2%, 126/285). This a nalysis helped the team to understand the areas that need improvement the most. \n\nBecause the SEIPS model analysis was a reactive approach, where the analysis happens after the forms were completed, the team decided to t ake a more proactive approach by applying a Failure Modes and Effects Anal ysis (FMEA) to determine areas of improvement. Each comment was analyzed according to the FMEA and the team assigned a severity score, occurrence s core, and a frequency score (1-10 for each score). This severity, occurre nce, and frequency score were then multiplied to determine the risk priori ty number (RPN). Each comment was analyzed to determine the probability o f recurrence and potential future failure modes. The median severity scor e was 5 (interquartile range (IQR) 3-6; range 1-9), and the median RPN was 16 (IQR 6-54; range 2-315). The team further explored median severity sc ore and RPN score by SEIPS category. Both severity score and RPN score va ried significantly by SEIPS category (p=0.008 and p=0.001 respectively). People and Environment had the highest median severity scores of 6 (IQR 4- 8) and 6 (IQR 2-7) respectively. Environment had the highest median RPN s core of 56 (IQR 4-84). \n\nNext, the team analyzed comments with RPNs ≥ 20 and severity scores ≥ 6 to develop improvement recommendations. Common t hemes for the recommendations included temperature maintenance, delivery n otification, inter-team communication, intravenous access, and timing of x -rays. The solutions were later reviewed and categorized according to SEI PS to ensure proactive safety. \n\nApplication\n\nThe CED analysis is part of a systematic initiative to improve the Special Delivery Unit (SDU). F uture work will oversee quality improvement projects identified by doing t he SEIPS and FMEA analyses, with the objective of taking a proactive appro ach to safety and preventing similar events from happening in the future. \n\nOverview of Presentation\n\nThis presentation will provide an overview of different human factors methods to analyze debriefing, including the S EIPS model, FMEA, and subject-matter expertise. The data analyzed consist ed of data from debrief forms collected after resuscitations of neonates w ith congenital anomalies. The debrief data was sorted into categories acc ording to the SEIPS model. Further work included doing an FMEA on the dat a to identify opportunities for improvement. Lastly, the FMEA items were ranked according to RPN score and severity, and improvement opportunities were proposed by an interdisciplinary group of subject matter experts.\n\n Track: Hospital Environments\n\nSession Chair: Gabriel Segarra (Medical Un iversity of South Carolina)\n\n END:VEVENT END:VCALENDAR