ESTRO 2020 Abstract book

S722 ESTRO 2020

The obtained results, lead to a more detailed understanding of PP patterns of care highlighting how, such continuative decision-making tool, can further optimize and improve the service. The results of CCA, partially validate and support the guideline of PP patters of care in use in our department. Further event logs and attributes will be implemented, in order to realize a web- based tool with an advanced reporting interface, able to optimize PP patters of care. PO-1279 More intelligent workflow in a radiation oncology center with the implementation of LEAN thinking E. López Ramirez 1 , L. Martin 2 , L.A. Glaría 3 , I. Castro 4 , R. Molina 4 , R. Lobo 3 , C. Fernandez 3 , E. Krumina 3 , D. Esteban 3 , M.I. Domenech 5 , N. Moratino 5 , C. Cordon 6 , L. Diaz 7 1 GenesisCare, Radiation Oncology, Madrid, Spain ; 2 GenesisCare, European Operations Improvement Team Lead. Oncology GB, Windsor, United Kingdom ; 3 GenesisCare, Radiation Oncology, Toledo, Spain ; 4 GenesisCare, Radiation Technician, Toledo, Spain ; 5 GenesisCare, Radiation Oncology nurse, Toledo, Spain ; 6 GenesisCare, Bussiness Development Analysis and SoF Leader, Madrid, Spain ; 7 GenesisCare, National Radiotherapy Coordinator, Madrid, Spain Purpose or Objective LEAN project was undertaken to implement Lean Management (LM) principles to improve workflow, identify and eliminate wastes in radiotherapy. This is important in centers with a huge patient volume and/or LINAC change. The value of the study was defined as achieving patient satisfaction and quality through the timely delivery of treatment, and specifically “patient treated on time”. Material and Methods LEAN project was conducted in the Center GenesisCare Radiation Oncology at Toledo, Spain (April-October) where 70 patients were treated per day and a new LINAC was installed in February 2019. The project has been developed in two phases: 1) Observation: (February-March): the workflow and sources of waste were recorded. 2) Continuous Improvement: (April-October) we worked in: Identification: Radiation technicians were trained in daily collected patients treated “on time” (green) and those who had suffered delay (red) on a blackboard and the reasons. Plan: The team analyzed the causes of delay to propose solutions. They realized that more than the 50% of patients were pelvic tumors and the major cause of the delays. Execute and Review : then one of the actions carried out was for pelvic tumors during August-October 2019 (8 weeks). Patients were instructed by nurses in how to drink the water daily before their session, dietary measures (empty rectum) and clothes recommendations (garments with elastics at the waist and comfortable shoes without laces). Results All patients comments and concerns were collected and they were related to feel ashamed to undress in front of the radiotherapy technicians (31.8%), difficulty with shoes (22.7%), no place in the LINAC to leave objects (13.6%), the necessity of using rectal catheter (40.9%), the use of crutches and obesity (9%). Usually problems appeared in patients over 70 years and they were solved with training during the treatment process. The real effectiveness of LEAN project implementation is that at the beginning of April 52% of the all the patients were treated “on time” and in mid-October became 92%. Regarding the pelvis tumors patients, in which specific actions were taken, the number of patients treated “on time” increase from 69% to 87% in 8 weeks (August- October).

different databases (radiotherapy department’s IOS and TPS), including pre-, intra- and post-treatment information. Event log generation has been sequentially realized based on in-house software PMiner, a R library born to deal with process mining in HealthCare. Then, a process discovery algorithm (PDA) has been applied to the acquired event logs database, in order to calculate which is the real model of the process that produced them. Finally, a conformance checking analysis (CCA) has been applied to measure how the acquired event logs database flows through a defined theoretical model based on the clinical practice. The model has three different patients’ groups defined on the dose prescription and the timespan between dose prescription and first day of the treatment event: 1-3 days for PP receiving 8 Gy, 3-10 day for PP receiving 20 Gy and 3-15 days for PP receiving 30 Gy. Results Based on the PDA 49, 238 and 154 plans (9.8%, 47,6% and 30,8%) with a prescription of 8 Gy, 20 Gy and 30 Gy respectively, were identified. Subsequentially, 59 plans (11.8%) were identified with a different dose prescription. Different PDA were investigated in order to describe the real model of the process, taking into account both databases. In Fig. 1 is depicted the results of a PDA computed on the TPS event logs. Fig. 2 shows the results of the CCA: 49/49 plans with 8 Gy prescription, 226/238 plans with 20 Gy prescription and 142/154 plans with 30 Gy prescription went through the defined model. The median time between the dose prescription and the first day of treatment is 4 days, 7 days and 10 days for plans with a prescription of 8 Gy, 20 Gy and 30 Gy respectively.

Conclusion