WE-G-9A-01: Radiation Oncology Outcomes Informatics.
The construction of databases and support software to enable routine and systematic aggregation, analysis and reporting of patient outcomes data is emerging as an important area. "How have results for our patients been affected by the improvements we have made in our practice and in the technologies we use?" To answer this type of fundamental question about the overall pattern of efficacy observed, it is necessary to systematically gather and analyze data on all patients treated within a clinic. Clinical trials answer, in great depth and detail, questions about outcomes for the subsets of patients enrolled in a given trial. However, routine aggregation and analysis of key treatment parameter data and outcomes information for all patients is necessary to recognize emergent patterns that would be of interest from a public health or practice perspective and could better inform design of clinical trials or the evolution of best practice principals. To address these questions, Radiation Oncology outcomes databases need to be constructed to enable combination essential data from a broad group of data types including: diagnosis and staging, dose volume histogram metrics, patient reported outcomes, toxicity metrics, performance status, treatment plan parameters, demographics, DICOM data and demographics. Developing viable solutions to automate aggregation and analysis of this data requires multidisciplinary efforts to define nomenclatures, modify clinical processes and develop software and database tools requires detailed understanding of both clinical and technical issues. This session will cover the developing area of Radiation Oncology Outcomes Informatics.


LEARNING OBJECTIVES
1. Audience will be able to speak to the technical requirements (software, database, web services) which must be considered in designing an outcomes database. 2. Audience will be able to understand the content and the role of patient reported outcomes as compared to traditional toxicity measures. 3. Audience will be understand approaches, clinical process changes, consensus building efforts and standardizations which must be addressed to succeed in a multi-disciplinary effort to aggregate data for all patients. 4. Audience will be able to discuss technical and process issues related to pooling data among institutions in the context of collaborative studies among the presenting institutions.