Personalized Breast Cancer Screening

Abundant literature and clinical trials indicate that routine cancer screenings decrease patient mortality for several common cancers. However, current national cancer screening guidelines heavily rely on patient age as the predominant factor in deciding cancer screening timing, neglecting other important medical characteristics of individual patients. This approach either delays screening or prescribes excessive screenings. Another disadvantage of the current approach is its inability to combine information across hospital systems because of the lack of a coherent records system.

We propose to use claims data and medical insurance transactions that use consistent and pre-established sets of codes for diagnosis, procedures, and medications to develop a clinical support tool to supply supplemental insights and precautions for physicians to make more informed decisions. Furthermore, we propose a novel machine learning framework to recommend personalized, data-driven, and dynamic screening decisions.

We apply this new method to the study of breast cancer mammograms using claims data from 378,840 female patients to demonstrate that across different risk populations, personalized screening reduces the average delay in a cancer diagnosis by 2-3 months with statistical significance, with even stronger benefits for individual patients up to 10 months.

Incorporating personal medical characteristics using claims data and novel machine learning methodologies into breast cancer screening improves screening delay by more dynamically considering changing patient risks. Future incorporation of the proposed methodology in health care settings could be provided as a potential support tool for clinicians.