Reader strategies: Variability and error- methodology, findings and health policy implications from a study of the US population of mammographers

Each year, approximately 60% of all US women over the age of 40 utilize mammography. Through the matrix of an imaging technology, this "Population of Patients (POP)" interacts with a population of approximately 20,000 physicians who interpret mammograms in the US. This latter "Population of Diagnosticians (POD)" operationally serves as the interface between an image-centric healthcare technology system and patient METHODS: Using data collected from a large POD and POP based study, I evaluate the distribution of several ROC curve-related parameters in the POD and explore the health policy implications of a population ROC curve for mammography. RESULTS AND CONCLUSIONS: Principal Components Analysis suggests that two Binomial parameters are sufficient to explain variation in the POD and implies that the Binomial model is foundational to Health Policy Research in Mammography. A population ROC curve based on percentiles of the POD can be used to set targets to achieve national health policy goals. Medical Image Perception science provides the framework. Alternatively, a "restrictive" policy can be envisioned using performance criteria based on area. However, the data suggests this sort of policy would be too costly in terms of reduced healthcare service capacity in the US in the face of burgeoning demands.