Zhengzheng Xu1, Soyoung Lee2, David Albani2, Donald Dobbins2, Rodney J Ellis3, Tithi Biswas3, Mitchell Machtay3, Tarun K Podder3. 1. Department of Radiation Oncology, University Hospitals Seidman Cancer Center, United States. Electronic address: Zhengzheng.Xu@UHhospitals.org. 2. Department of Radiation Oncology, University Hospitals Seidman Cancer Center, United States. 3. Department of Radiation Oncology, University Hospitals Seidman Cancer Center, United States; School of Medicine, Case Western Reserve University, Cleveland, United States.
Abstract
PURPOSE: This study identified and evaluated the factors that are responsible for delay in the clinical workflow of radiation therapy, starting from the CT simulation (CT-Sim) to the first fraction of treatment delivery using the Failure Mode and Effects Analysis (FMEA) methodology. MATERIALS AND METHODS: A total of 1106 patient cases were retrospectively analyzed using FMEA methodology. For each failure mode (FM), the following factors were rated and discussed by the group: occurrence (O), severity (S), detectability (D), and methods of improvement or mitigation. In addition, two new factors, namely social effect (SE) and economic effect (EE), were introduced to evaluate the impact of FM on the department or hospital. Risk priority number (RPN) and the product of RPN, SE, and EE (i.e. RPNSE-EE) were calculated for each FM. RESULTS: Average delay caused by identified FM was 8 days while 76% of the FMs resulted in delay of less than 5 days. The RPN of all the FMs ranged from 4 to 60 with an average value of 18. "Tumor volume, prescription and objective" had the highest average RPN of 23. The majority of FMs with high RPN were identified in "CT-Sim" (RPN: 21.5 ± 11.1; RPNSE-EE: 97.0 ± 46.4) and "treatment planning" (RPN: 20.1 ± 8.1, RPNSE-EE: 152.9 ± 76.5) stages. CONCLUSION: The FMEA enabled identification of the factors that caused delay in the pre-treatment process of radiation therapy. "CT-Sim" and "treatment planning" stages had more FMs with high RPN values which have higher priority for future improvement. Two new factors, SE and EE, were introduced and appeared to be valuable in evaluating the impact of FMs on radiation oncology department or hospital in general.
PURPOSE: This study identified and evaluated the factors that are responsible for delay in the clinical workflow of radiation therapy, starting from the CT simulation (CT-Sim) to the first fraction of treatment delivery using the Failure Mode and Effects Analysis (FMEA) methodology. MATERIALS AND METHODS: A total of 1106 patient cases were retrospectively analyzed using FMEA methodology. For each failure mode (FM), the following factors were rated and discussed by the group: occurrence (O), severity (S), detectability (D), and methods of improvement or mitigation. In addition, two new factors, namely social effect (SE) and economic effect (EE), were introduced to evaluate the impact of FM on the department or hospital. Risk priority number (RPN) and the product of RPN, SE, and EE (i.e. RPNSE-EE) were calculated for each FM. RESULTS: Average delay caused by identified FM was 8 days while 76% of the FMs resulted in delay of less than 5 days. The RPN of all the FMs ranged from 4 to 60 with an average value of 18. "Tumor volume, prescription and objective" had the highest average RPN of 23. The majority of FMs with high RPN were identified in "CT-Sim" (RPN: 21.5 ± 11.1; RPNSE-EE: 97.0 ± 46.4) and "treatment planning" (RPN: 20.1 ± 8.1, RPNSE-EE: 152.9 ± 76.5) stages. CONCLUSION: The FMEA enabled identification of the factors that caused delay in the pre-treatment process of radiation therapy. "CT-Sim" and "treatment planning" stages had more FMs with high RPN values which have higher priority for future improvement. Two new factors, SE and EE, were introduced and appeared to be valuable in evaluating the impact of FMs on radiation oncology department or hospital in general.
Authors: Reshma Munbodh; Toni M Roth; Kara L Leonard; Robert C Court; Utkarsh Shukla; Sarah Andrea; Marissa Gray; Gregg Leichtman; Eric E Klein Journal: J Appl Clin Med Phys Date: 2022-08-03 Impact factor: 2.243