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Data Driven Model to Estimate the Cost Associated with the Correction of Deviations Found During the Physics Plan Check Process

B Vaishnav1*, K Kolsky2 , S Parker3 , C Deufel4 , L Fong de los Santos5 , (1) Mayo Clinic, Rochester, MN, (2) Mayo Clinic, Rochester, MN, (3) Wake Forest Baptist Health, High Point, NC, (4) Mayo Clinic, Rochester, MN, (5) Mayo Clinic, Rochester, MN

Presentations

(Monday, 7/15/2019) 7:30 AM - 9:30 AM

Room: Stars at Night Ballroom 1

Purpose: To develop a data driven model to estimate the cost associated with correction of deviations found during physics plan check process, using analysis of long term, high resolution plan check deviation data from a large academic institution.

Methods: Mistakes in the external beam treatment planning process were recorded in a physics plan check deviation database between 2014 and 2018(16,081 treatment plans). The plan check deviations were reported using an evolving checklist with nearly 60 core items and an additional 30 items for specialized procedures. For the purpose of the proposed model, each item was scored on a published five point risk scale, which was subsequently used to prioritize plans and estimate cost variation with the allowable threshold risk score. The checklist items were subsequently organized into thirteen core treatment planning steps according to the type of work that would be involved in correcting the mistake.This cost of correcting mistakes was considered for a subset of 594 pelvic site treatment plans (prostate and gynecologic sites) from 2018. Calculations of the correction expense were made:based on maximum risk involved in a plan based on the deviations involved, and on actual plan corrections determined from plan review comments. Finally, a model that estimated cost based on the cumulative frequency distribution of each risk was developed.

Results: The target and OAR contouring step consistently had the largest deviations.The estimated additional cost-per-plan varied approximately from 15% for correcting only high risk deviations to 25% for correcting all.The correction factor based model agreed well with the cost-estimate based on actual corrections.

Conclusion: A data driven, risk-informed method maybe able to help a clinic estimate the cost of quality improvement in the treatment planning process.Retrospective analysis of deviations suggests a redistribution of plan check items throughout the planning process could reduce overall costs.

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