A text-mining approach to obtain detailed treatment information from free-text fields in population-based cancer registries: A study of non-small cell lung cancer in California.

BACKGROUND: Population-based cancer registries have treatment information for all patients making them an excellent resource for population-level monitoring. However, specific treatment details, such as drug names, are contained in a free-text format that is difficult to process and summarize. We assessed the accuracy and efficiency of a text-mining algorithm to identify systemic treatments for lung cancer from free-text fields in the California Cancer Registry. METHODS: The algorithm used Perl regular expressions in SAS 9.4 to search for treatments in 24,845 free-text records associated with 17,310 patients in California diagnosed with stage IV non-small cell lung cancer between 2012 and 2014. Our algorithm categorized treatments into six groups that align with National Comprehensive Cancer Network guidelines. We compared results to a manual review (gold standard) of the same records. RESULTS: Percent agreement ranged from 91.1% to 99.4%. Ranges for other measures were 0.71-0.92 (Kappa), 74.3%-97.3% (sensitivity), 92.4%-99.8% (specificity), 60.4%-96.4% (positive predictive value), and 92.9%-99.9% (negative predictive value). The text-mining algorithm used one-sixth of the time required for manual review. CONCLUSION: SAS-based text mining of free-text data can accurately detect systemic treatments administered to patients and save considerable time compared to manual review, maximizing the utility of the extant information in population-based cancer registries for comparative effectiveness research.

Extreme makeover: Transformation of the veterans health care system.

The veterans health care system administered by the U.S. Department of Veterans Affairs (VA) was established after World War I to provide health care for veterans who suffered from conditions related to their military service. It has grown to be the nation's largest integrated health care system. As the system grew, a number of factors contributed to its becoming increasingly dysfunctional. By the mid-1990s, VA health care was widely criticized for providing fragmented and disjointed care of unpredictable and irregular quality, which was expensive, difficult to access, and insensitive to individual needs. Between 1995 and 1999, the VA health care system was reengineered, focusing especially on management accountability, care coordination, quality improvement, resource allocation, and information management. Numerous systemic changes were implemented, producing dramatically improved quality, service, and operational efficiency. VA health care is now considered among the best in America, and the VA transformation is viewed as a model for health care reform.

Knowledge for improvement: who will lead the learning?

If we wish to improve the results of a system, what is needed to help health professionals learn about the redesign of that system? To create learning experiences that will attract good health professionals, a special blend of practical insight and critical thinking is required. To enjoy good standing in the academy, these leaders must be able to design, conduct, and disseminate knowledge-building activities subject to peer review. This is the story of the development of the United States Veteran's Health Administration Quality Scholars Program from those who designed and formed it to prepare health professional teachers and academics.