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A Data-Driven Analytical Framework to Track and Improve Clinical Workflow in Radiation Oncology

R Munbodh1*, K Leonard1, T Roth2, M Schwer1, J Brindle1, E Klein1, (1) Alpert Medical School of Brown University/Rhode Island Hospital, Providence, RI, (2) Clark Cancer Center, Cape Cod Healthcare, Falmouth, MA,

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: Radiation treatment planning is a complex process with multiple, dependent steps involving an interdisciplinary team. We previously implemented an interactive, web-based dashboard to monitor and visualize clinical workflow in real-time. The dashboard also implements a standardized, integrated framework to acquire and analyze data for quantitative workflow evaluation. We present this framework and the results of data-driven analyses aimed at optimizing clinical efficiency, safety and workflow changes.

Methods: Process maps and flowcharts were created to model the treatment planning workflow. These described: (1) standardized carepath activities associated with 3D and IMRT treatments from CT simulation (CTSim) to treatment, (2) activity timeline and sequence, (3) activity ownership and (4) physician, medical physicist, dosimetrist and therapist interactions. An ideal timeline of 6 days from CTSim to physics chart review (PCR) was formulated. Patient carepath activity status was recorded in the departmental electronic medical record (EMR) system and automatically queried and analyzed. We evaluated various performance measures and the impact on workflow of a new activity, the pre-MD review (PMR), introduced to improve treatment plan quality.

Results: Staff were trained to use the EMR to record activity status as per the flowcharts. The dashboard displayed workflow progression. Data for 127 new patient treatments (72 3D, 55 IMRT) and 744 carepath activities were analyzed. The mean (standard deviation) from CTSim to PCR completion for 3D and IMRT, respectively, was 4.0(2.7) and 6.1(2.2) days without PMR and 5.7(2.6) and 6.2(2.1) with PMR. PMR did not significantly increase time. 3D activity times were significantly better (p < 0.05) than ideal. Mean activity on-time performance relative to the ideal timeline was 54% (11%-100%).

Conclusion: The framework developed allows for informed, data-driven decisions regarding clinical workflow management and the impact of changes as we seek to optimize clinical efficiency and safety, and incorporate new interventions into clinical practice.

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Keywords

Quality Control, Radiation Therapy, Computer Software

Taxonomy

IM/TH- Formal Quality Management Tools: General (most aspects)

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