Leveraging Linkage of Cohort Studies With Administrative Claims Data to Identify Individuals With Cancer.

BACKGROUND: In an effort to overcome quality and cost constraints inherent in population-based research, diverse data sources are increasingly being combined. In this paper, we describe the performance of a Medicare claims-based incident cancer identification algorithm in comparison with observational cohort data from the Nurses' Health Study (NHS). METHODS: NHS-Medicare linked participants' claims data were analyzed using 4 versions of a cancer identification algorithm across 3 cancer sites (breast, colorectal, and lung). The algorithms evaluated included an update of the original Setoguchi algorithm, and 3 other versions that differed in the data used for prevalent cancer exclusions. RESULTS: The algorithm that yielded the highest positive predictive value (PPV) (0.52-0.82) and κ statistic (0.62-0.87) in identifying incident cancer cases utilized both Medicare claims and observational cohort data (NHS) to remove prevalent cases. The algorithm that only used NHS data to inform the removal of prevalent cancer cases performed nearly equivalently in statistical performance (PPV, 0.50-0.79; κ, 0.61-0.85), whereas the version that used only claims to inform the removal of prevalent cancer cases performed substantially worse (PPV, 0.42-0.60; κ, 0.54-0.70), in comparison with the dual data source-informed algorithm. CONCLUSIONS: Our findings suggest claims-based algorithms identify incident cancer with variable reliability when measured against an observational cohort study reference standard. Self-reported baseline information available in cohort studies is more effective in removing prevalent cancer cases than are claims data algorithms. Use of claims-based algorithms should be tailored to the research question at hand and the nature of available observational cohort data.

The 5 T's: Applying Cognitive Science to Improve Prehospital Medical Education.

Although research on effective teaching methods exists, the application of this information in prehospital medical education is limited. Applying lessons from the realms of cognitive psychology and neuroscience, prehospital educators can enhance their ability to teach. One such concept is the theory of cognitive load. Understanding this theory can reduce the mental strain placed on learners and allow educators to best accomplish long-term learning success, defined as "far transfer" of material to novel contexts. Thus, we propose 5 concise strategies gleaned from cognitive science literature: Tell a story, Time, Technical elements, Think novelly, and Testing and recall (referred to as the "5 T's"). Each strategy is grounded in research and applicable to medical education. Increased educator awareness and use of these strategies garners the potential to transform prehospital medical education.