ESTRO 2023 - Abstract Book

S863

Digital Posters

ESTRO 2023

Further work is currently ongoing to improve reliability of results, collecting more samples over a range of machines and including additional treatment sites. The long-term objective is to optimise power consumption in radiotherapy to reduce costs. References: [1] Lancet Oncology, 22(10), 2021 [2] Carbon Trust, 2021 [3] Elekta site planning (1008404_02), 2009 [4] Clinical Oncology 34(3), 2022

[5] Ofgem, 2022 [6] Gov.uk, 2022

PO-1078 Factors that impact patients choosing the radiation oncologist in the era of social networks

J. Regis Neto 1 , T. Souza 2

1 Oncovida Radioterapia, Radiation Therapy, João Pessoa, Brazil; 2 Hospital Sírio-Libanês, Radiation Therapy Department, Brasilia, Brazil Purpose or Objective The choice of a medical facility and professionals to perform some type of treatment was always based on the indication of other doctors or patients, as well as information about professional training. In the last five years, with the boom of social media, many health professionals have been able to use these tools to create a new way of attracting patients, especially in areas related to aesthetic and well-being treatments. We aim to discover the current influence of digital networks on patients' choice of radiation oncologist and radiotherapy center. Materials and Methods A standardized interview was conducted with patients undergoing radiotherapy or with treatment completed in the last 2 years. In addition to demographic and marketing data, an importance score that ranged from not relevant (0) to very important (5) was applied to factors such as the physician's place of training, referral from other physicians, referral from relatives, popularity on social media and availability of state-of-the-art equipment. We also evaluated the importance of data such as online patient testimonials, health insurance coverage, content published on the internet and geographic distance, among others in the decision-making process. A Multivariate analysis was performed, completed with multivariate regression and Cox proportional hazard model. Results A total of 200 patients were interviewed, 52% of whom were men, mean age 53 years, distributed proportionally in the 5 Brazilian geographic regions. The vast majority (65%) sought information before scheduling the first appointment, with 71% using the internet in general for this purpose, of which 19.7% used social networks. Factors such as reputation of the place that the physician concluded radiation oncology training and others physician and patient recommendations had much more weight in the decision than factors such as popularity on social networks, publication of scientific articles and availability of state-of-the-art equipment for treatment in the facility, according to the applied score. Among patients who select their physicians and treatment centers based on information from the internet, the most relevant factor for the decision was the presence of others patients testimonials (61%) on the physician’s page/social networks, followed by the reputation of the physician’s main training site ( 19%) and distance from the treatment facility (5%). Conclusion A solid medical education in a center of excellence and a good relationship with the local medical community and with patients remains the main ways to attract new patients to radiation oncologists in Brazil, regardless of their popularity on social networks. Nonetheless, it is essential to remain present in these digital ecosystems as this is where the vast majority of patients seek to endorse information and references about health professionals in general. M. Huet Dastarac 1 , W. Zhao 2 , S. Deffet 1 , E. Longton 3 , S. Cvilic 4 , S. Michiels 1 , E. Sterpin 1 , B. Macq 2 , J. Lee 1 , A. Barragan Montero 1 1 Université Catholique de Louvain, MIRO, Brussels, Belgium; 2 Université Catholique de Louvain, ICTEAM, Louvain la Neuve, Belgium; 3 Cliniques Universitaires de Saint Luc, Radiation Oncology, Brussels, Belgium; 4 Clinique Saint Jean, Radiation Oncology, Brussels, Belgium Purpose or Objective Artificial Intelligence (AI) is gaining momentum in medical fields like radiation therapy. elineatig structures and predicting an optimal dose trade-off for patients can be achieved in a network of hospitals, which allow second advices, sharing of expertise and establishment of federated models Although some of these models are starting to be implemented clinically, an end-to-end workflow with a user-friendly graphical interface to visualize the result of successive AI models and to support decision making until final treatment indication is still prospective. To address this problem, we present PARROT, a free and open-source web platform that facilitates the curation of AI delineation models and the visualization of predicted dose distributions in a specific patient for different treatment modalities. The different treatments can be compared with clinical figures of merit and normal tissue complication PO-1079 PARROT: An end-to-end open source workflow of AI-assisted treatment planning and decision support