Enhancing treatment planning workflow in radiation oncology.

Authors

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Arpine Khudanyan

Washington University of St. Louis, St. Louis, MO

Arpine Khudanyan, Jerry Jeff Jaboin, Barb Agrimson, Simon Brown, Wolfram Laub, Jo Price, Stephanie Junell, Jeff Hanson, Dorothy Ryan, Jennifer Ruocco, Charles R. Thomas

Organizations

Washington University of St. Louis, St. Louis, MO, Oregon Health & Science University, Portland, OR, Oregon Health and Science University, Portland, OR

Research Funding

Other

Background: The treatment planning process is the most impactful and complex aspect of radiation oncology care. In order to provide short turn around times from patient CT simulation to treatment plan QA, requires a level of strain and haste for multiple members of the treatment team. We evaluated 18 months of data to determine the percentage of Quality Assurance (QA) approvals of nonemergent complex plans (including 3D/IMRT/Arc/SBRT/SRS) that are not completed by 8:00a the day prior to a patient's first treatment appointment, and found that this occcurred on time 62% of the time. We utilized the ASCO Quality Training Process (QTP) process to brainstorm methods to enhance workflow, and create an action plan that would allow for small Plan-Do-Study-Act cycles to reach our ideal state of > 90% On Time Treatment Plan Delivery. Methods: We utilized LEAN tools from the ASCO QTP progam (June 2016 cycle). We created an Ishikawa diagram to determine the areas of greatest potential. We subsequently developed a highly detailed flow chart of our work processes. Then we utilized Mosaiq scripts to establish baselines for our process measures. Results: From our Ishikawa diagram, the initial most impact was in generate target volume contours after the CT simulation. Our first measure was to visually manage the CT simulation process. We established a computer based quality control list (QCL) to enhance the communication process, and provided a "reminder" at the time of simulation of the target contour delivery date. After collection of data points, there was a significant improvement in on time delivery (now 89%, and approaching the ideal state), as illustrated by our Run Chart, and a coincident decrease in variability between providers and cases was noted in this cohort. Conclusions: Our preliminary change effort is promising, but further data will enhance our findings. Our next steps are to collect an additional two weeks of data, and initiate another PDSA cycle with a new measure of automated reminders from the QCL system. In achieving our project goals and making it sustainable, we believe that we will be providing high quality, high value patient care, while enhancing the healthiness of the work environment for our staff.

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Abstract Details

Meeting

2017 ASCO Quality Care Symposium

Session Type

Poster Session

Session Title

Poster Session A: Cost, Value, and Policy in Quality; Practice of Quality

Track

Cost, Value, and Policy in Quality,Practice of Quality

Sub Track

Learning from Projects Done in a Practice

Citation

J Clin Oncol 35, 2017 (suppl 8S; abstract 84)

DOI

10.1200/JCO.2017.35.8_suppl.84

Abstract #

84

Poster Bd #

D1

Abstract Disclosures

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