[Using cancer case identification algorithms in medico-administrative databases: Literature review and first results from the REDSIAM Tumors group based on breast, colon, and lung cancer].

BACKGROUND: The development and use of healthcare databases accentuates the need for dedicated tools, including validated selection algorithms of cancer diseased patients. As part of the development of the French National Health Insurance System data network REDSIAM, the tumor taskforce established an inventory of national and internal published algorithms in the field of cancer. This work aims to facilitate the choice of a best-suited algorithm.

METHOD: A non-systematic literature search was conducted for various cancers. Results are presented for lung, breast, colon, and rectum. Medline, Scopus, the French Database in Public Health, Google Scholar, and the summaries of the main French journals in oncology and public health were searched for publications until August 2016. An extraction grid adapted to oncology was constructed and used for the extraction process.

RESULTS: A total of 18 publications were selected for lung cancer, 18 for breast cancer, and 12 for colorectal cancer. Validation studies of algorithms are scarce. When information is available, the performance and choice of an algorithm are dependent on the context, purpose, and location of the planned study. Accounting for cancer disease specificity, the proposed extraction chart is more detailed than the generic chart developed for other REDSIAM taskforces, but remains easily usable in practice.

CONCLUSIONS: This study illustrates the complexity of cancer detection through sole reliance on healthcare databases and the lack of validated algorithms specifically designed for this purpose. Studies that standardize and facilitate validation of these algorithms should be developed and promoted.