Facilitating the analysis and management of data for cancer care

Abstract

The Edinburgh Cancer Centre (ECC) is an institution containing the National Health Service (NHS) Lothian cancer patient data from multiple resources. These resources are scattered across different systems and platforms, making it difficult to use the information collected in a useful way. There is a lack of proxy between the different (sub)systems, and this thesis presents a series of applications/projects to promote data usage and interoperability. We develop both front-end and back-end applications to bring together several databases, such as ChemoCare, Trak, and Oncology database. We create the South East Scotland Oncology (SESO) Gateway to improve the quality and capability of reporting outcomes within South East Scotland Oncology databases in real-time using routinely captured and integrated electronic healthcare data. With SESO Gateway, we focus on cancer pathway data visualisation for both the personal timeline and the cohort summary for various treatments. We also carry out a database migration and evaluate several reporting services for the newly migrated database to accelerate data access. We then perform data analysis for the patient's treatment waiting time. By analysing the waiting time and comparing it to the intended pathway, we can simplify the auditing process of the first stage of patients' cancer care journey. Further, we use the patients' treatment data, recorded toxicity level, and various observations concerning breast cancer patients to create models to analyse the outcome of the treatments, mainly chemotherapy. We compare several different techniques applied to the same data set to predict the toxicity outcome of the treatment. Through analysis and evaluation of the performance of these techniques, we can determine which method is more suitable in different situations to assist the oncologists in real-time clinical practice. After training the models, we create a dashboard as a placeholder for the models. Lastly, we explore how to define rules for cancer data and use a constraint based approach to fabricate a large cancer dataset, which will allow us to explore more techniques and further improve our system capability in the future. With our proposed systems, healthcare professionals can directly access and analyse patient data to gain further insights regarding the treatment that is best suited for an individual patient.