Grants and Contracts Details
One of the most glaring gaps in our understanding of cancer progression is the issue of disease recurrence and how best to manage it. While primary treatment of localized tumors will often lead to long-term control or remission, many cancer survivors remain at risk for recurrences, which will require further intervention, with associated clinical resource use, adverse psychosocial effects, and costs. The objective of this proposal is to advance surveillance science by developing, validating and deploying a scalable, automated approach for identifying cancer recurrence. We will leverage the availability of medical claims from multiple payers and harness the power of mobile heath technology to augment the claims data with patient-reported outcome (PRO) information about disease status. In the UG3 phase we will combine claims and the PRO data in a novel recurrence-detection algorithm and we will validate it against a medical record gold standard in a subset of cases. In the UH3 phase we will deploy the claims-PRO algorithm and validate it in a second registry, the Kentucky Cancer registry, as demonstration of the scalability of the algorithm. We propose to focus on breast cancer due its prevalence and burden in US women and will build on the extensive expertise of our study team in breast cancer outcomes and recurrence ascertainment. By leveraging medical claims which can be made broadly available at a national level, and by incorporating mobile health technology which can be deployed on a large scale, we expect that our algorithm will be applicable at a population level across SEER registries. The addition of information on disease recurrence to cancer registry data will materially add to the portfolio of information about the burden of disease and open a whole new frontier in cancer research. Our proposed work will constitute a key step forward in making this vision a reality.
|Effective start/end date||9/25/17 → 8/31/18|
- Fred Hutchinson Cancer Research Center: $17,267.00
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.