Program Information
Strategies for Quality Improvement Based On RO-ILS Information
N Agazaryan
G Ezzell
A Dicker
S Weintraub
L Santanam
N Agazaryan1*, G Ezzell2*, A Dicker3*, S Weintraub4*, L Santanam5*, (1) UCLA School of Medicine, Los Angeles, CA, (2) Mayo Clinic Arizona, Phoenix, AZ, (3) Thomas Jefferson Univ Hospital, Philadelphia, PA, (4) Southcoast Health, Fairhaven, MA, (5) Washington University School of Medicine, St.louis, MO
Presentations
1:45 PM : The UCLA Experience with RO-ILS: Developing a Culture of Safety, Processes, and Metrics - N Agazaryan, Presenting Author2:09 PM : How RO-ILS works at the national level: methods and challenges in analyzing events - G Ezzell, Presenting Author
2:33 PM : What have we learned from RO-ILS: Part 1 - before the patient is on the table - A Dicker, Presenting Author
2:57 PM : What have we learned from RO-ILS: Part 2 - after the patient is on the table - S Weintraub, Presenting Author
3:21 PM : Strategies for Quality Improvement Based on RO-ILS Information - L Santanam, Presenting Author
WE-F-702-0 (Wednesday, August 2, 2017) 1:45 PM - 3:45 PM Room: 702
The Radiation Oncology Incident Learning System (RO-ILS) has been in operation since 2014, has over 200 facilities participating, and has received over 3000 event reports. It is a major initiative sponsored by AAPM and ASTRO. RO-ILS’ goal and purpose is to be a national incident learning system, and so it is important to share lessons learned with the community. The initial presentation will summarize UCLA’s experience implementing RO-ILS with an emphasis on developing collaborative processes, metrics, analytics and interventions. The system is designed to raise clinical quality and safety and improve the patient experience. The following speakers are current members of the committee that reviews the events submitted to the national system and develops reports to the community. The first speaker will describe how RO-ILS works at the national level, how events are prioritized for review, some of the challenges in analyzing events, and the processes used to identify common failure pathways and potential mitigation strategies. The next two speakers will describe in more detail what has been learned from RO-ILS reports, focusing on events triggered (1) before the patient is on the table (e.g. in prescription, simulation, or planning) and (2) after the patient is on the table (e.g. in setup or image guidance). Common error pathways will be described. For example, “wrong shift” is a frequent event, and RO-ILS data identifies a number of upstream errors leading to that outcome. This provides a fault tree based on experience and the safety barriers that either were or were not effective in identifying the issues. Other general error types that have been studied include: approved plans not conforming to the physician’s actual intent, problems associated with imaging for planning, problems associated with target definition, and problems associated with image guidance. The final speaker will discuss strategies for quality improvement based on information gathered from local and national incident learning systems, with particular emphasis on what has been found to work and how to customize quality improvement processes for clinics of different sizes.
Learning Objectives:
1. Understand how RO-ILS can be implemented in a facility and be used as a local incident learning system.
2. See how event reports can be structured to be of most use to external reviewers and therefore the entire community.
3. Understand how the reports are used at the national level to identify lessons of common interest.
4. See examples of error pathways that have been seen frequently and quality assurance steps that have succeeded or failed to prevent the errors from propagating.
5. Understand the tools provided by RO-ILS reports to allow clinics to learn from others’ experiences.
6. See examples of how local and national incident learning reports can be used to improve local practices.
Handouts
- 127-35499-418554-125999.pdf (N Agazaryan)
- 127-35500-418554-126265.pdf (G Ezzell)
- 127-35501-418554-127657.pdf (A Dicker)
- 127-35502-418554-127658.pdf (S Weintraub)
- 127-38246-418554-126910.pdf (L Santanam)
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