Management of the radiotherapy quality control using automated Big Data processing

Abstract

Introduction. In Moscow, the state-of-the-art information technologies for cancer care data processing are widely used in routine practice. Data Science approaches are increasingly applied in the field of radiation oncology. Novel arrays of radiotherapy performance indices can be introduced into real-time cancer care quality and safety monitoring. The purpose of the study. The short review of the critical structural elements of automated Big Data processing and its perspectives in the light of the internal quality and safety control organization in radiation oncology departments. Material and methods. The PubMed (Medline) and E-Library databases were used to search the articles published mainly in the last 2-3 years. In total, about 20 reports were selected. Results. This paper highlights the applicability of the next-generation Data Science approaches to quality and safety assurance in radiation oncological units. The structural pillars for automated Big Data processing are considered. Big Data processing technologies can facilitate improvements in quality management at any radiotherapy stage. Simultaneously, the high requirements for quality and integrity across indices in the databases are crucial. Detailed dose data may also be linked to outcomes and survival indices integrated into larger registries. Discussion. Radiotherapy quality control could be automated to some extent through further introduction of information technologies making comparisons of the real-time quality measures with digital targets in terms of minimum norms / standards. The implementation of automated systems generating early electronic notifications and rapid alerts in case of serious quality violation could drastically improve the internal medical processes in local clinics. Conclusion. The role of Big Data tools in internal quality and safety control will dramatically increase over time.