Key Points
• Conclusion: “The management of patients with NSCLC requires care from a multidisciplinary team, leading to a lack of a single aggregated data source in real-world settings. The availability of integrated clinical data from MRs, health plan claims, and other sources of clinical care may improve the ability to assess emerging treatments”
• In the retrospective cohort study, researchers integrated data from 4 disparate sources: administrative claims from the HealthCore Integrated Research Database, clinical data from a Cancer Care Quality Program (CCQP), clinical data from abstracted medical records (MRs), and mortality data from the US Social Security Administration. Selected patients received second-line (2L) therapy between November 01, 2015, and April 13, 2018.
• “The ability of our study to integrate data across 3 sources to create a cohort of NSCLC patients with rich clinical and economic data offers an important addition to the comparatively small body of data on the performance of data integration methods and the determination of health outcomes based on these data for patients with NSCLC,” the authors wrote.
• The design of the current study recapitulated a prospective observational study by identifying patients within large preexisting databases and then following them within the data set to assess outcomes. This strategy could lead to the creation of a future database that includes demographic, clinical, and health care resource utilization data that can better reflect health outcomes.
• The use of big data to determine NSCLC and other health outcomes represents a trend, and integrates health plan enrollment, disease registries, scanned image repositories, and more. Using real-world evidence drawn from big health care data has been spurred by technological advances including machine learning, natural language processing improvements in electronic medical systems, and the ability to link clinical and health claims data in private and public systems.
• Limitations of the current study include the underlying quality of the data mined for integration. For instance, CCQP data was obtained at the time of prior authorization and not at diagnosis. Another example involved tumor growth and progression information that was “collected in various formats and levels of detail outside of a clinical trial setting. As a result, some of our research questions of interest had underpopulated data.”