Presentation
Novel Approach to Clinical Event Debriefing Analysis: Utilizing SEIPS and FMEA Models
SessionHE7: Debriefs
DescriptionBackground:
Debriefing is a tool commonly used after crisis intervention, psychological research, and experiential learning. It has also been incorporated into the medical field after acute resuscitation events. Clinical event debriefing (CED) frequently uncovers issues and challenges that require attention. Categorizing CED findings allows prioritization of changes by risk and pervasiveness.
The Special Delivery Unit (SDU) at a large urban tertiary care children’s hospital delivers approximately 500 babies with congenital anomalies per year. In the SDU, CED with the entire resuscitation team is recommended after each delivery. SDU CED collects data using 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 human factors and safety frameworks, and (2) develop common strategies for remediation of the issues based on similar content category, risk, and frequency.
A total of 285 comments from 185 debrief forms collected between June 2023 and August 2024 were reviewed by an interprofessional team. Team members included a human factors engineer, a clinical nurse specialist, attending 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.
The SEIPS model analyzes the system and the interactions between tools and technology, organization, people, tasks, and environment. The first part of the analysis consisted of organizing the debrief form comments into categories according to the work system construct of SEIPS model (i.e. Tools/Technology, Organization, People, Tasks, Environment) to provide context for understanding and improving patient safety by investigating complexity within a healthcare system. Tools/Technology was the category with the most comments (45.6%, 130/285), followed by Tasks (44.2%, 126/285). This analysis helped the team to understand the areas that need improvement the most.
Because the SEIPS model analysis was a reactive approach, where the analysis happens after the forms were completed, the team decided to take a more proactive approach by applying a Failure Modes and Effects Analysis (FMEA) to determine areas of improvement. Each comment was analyzed according to the FMEA and the team assigned a severity score, occurrence score, and a frequency score (1-10 for each score). This severity, occurrence, and frequency score were then multiplied to determine the risk priority number (RPN). Each comment was analyzed to determine the probability of recurrence and potential future failure modes. The median severity score 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 score and RPN score by SEIPS category. Both severity score and RPN score varied 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 score of 56 (IQR 4-84).
Next, the team analyzed comments with RPNs ≥ 20 and severity scores ≥ 6 to develop improvement recommendations. Common themes for the recommendations included temperature maintenance, delivery notification, inter-team communication, intravenous access, and timing of x-rays. The solutions were later reviewed and categorized according to SEIPS to ensure proactive safety.
Application
The CED analysis is part of a systematic initiative to improve the Special Delivery Unit (SDU). Future work will oversee quality improvement projects identified by doing the SEIPS and FMEA analyses, with the objective of taking a proactive approach to safety and preventing similar events from happening in the future.
Overview of Presentation
This presentation will provide an overview of different human factors methods to analyze debriefing, including the SEIPS model, FMEA, and subject-matter expertise. The data analyzed consisted of data from debrief forms collected after resuscitations of neonates with congenital anomalies. The debrief data was sorted into categories according to the SEIPS model. Further work included doing an FMEA on the data 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.
Debriefing is a tool commonly used after crisis intervention, psychological research, and experiential learning. It has also been incorporated into the medical field after acute resuscitation events. Clinical event debriefing (CED) frequently uncovers issues and challenges that require attention. Categorizing CED findings allows prioritization of changes by risk and pervasiveness.
The Special Delivery Unit (SDU) at a large urban tertiary care children’s hospital delivers approximately 500 babies with congenital anomalies per year. In the SDU, CED with the entire resuscitation team is recommended after each delivery. SDU CED collects data using 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 human factors and safety frameworks, and (2) develop common strategies for remediation of the issues based on similar content category, risk, and frequency.
A total of 285 comments from 185 debrief forms collected between June 2023 and August 2024 were reviewed by an interprofessional team. Team members included a human factors engineer, a clinical nurse specialist, attending 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.
The SEIPS model analyzes the system and the interactions between tools and technology, organization, people, tasks, and environment. The first part of the analysis consisted of organizing the debrief form comments into categories according to the work system construct of SEIPS model (i.e. Tools/Technology, Organization, People, Tasks, Environment) to provide context for understanding and improving patient safety by investigating complexity within a healthcare system. Tools/Technology was the category with the most comments (45.6%, 130/285), followed by Tasks (44.2%, 126/285). This analysis helped the team to understand the areas that need improvement the most.
Because the SEIPS model analysis was a reactive approach, where the analysis happens after the forms were completed, the team decided to take a more proactive approach by applying a Failure Modes and Effects Analysis (FMEA) to determine areas of improvement. Each comment was analyzed according to the FMEA and the team assigned a severity score, occurrence score, and a frequency score (1-10 for each score). This severity, occurrence, and frequency score were then multiplied to determine the risk priority number (RPN). Each comment was analyzed to determine the probability of recurrence and potential future failure modes. The median severity score 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 score and RPN score by SEIPS category. Both severity score and RPN score varied 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 score of 56 (IQR 4-84).
Next, the team analyzed comments with RPNs ≥ 20 and severity scores ≥ 6 to develop improvement recommendations. Common themes for the recommendations included temperature maintenance, delivery notification, inter-team communication, intravenous access, and timing of x-rays. The solutions were later reviewed and categorized according to SEIPS to ensure proactive safety.
Application
The CED analysis is part of a systematic initiative to improve the Special Delivery Unit (SDU). Future work will oversee quality improvement projects identified by doing the SEIPS and FMEA analyses, with the objective of taking a proactive approach to safety and preventing similar events from happening in the future.
Overview of Presentation
This presentation will provide an overview of different human factors methods to analyze debriefing, including the SEIPS model, FMEA, and subject-matter expertise. The data analyzed consisted of data from debrief forms collected after resuscitations of neonates with congenital anomalies. The debrief data was sorted into categories according to the SEIPS model. Further work included doing an FMEA on the data 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.
Event Type
Oral Presentations
TimeTuesday, March 243:30pm - 3:50pm EDT
LocationMurray Hill West
Hospital Environments