PEPRVision: a visualization framework for Pharmacoepidemiology research

Medical chart review is commonly used in epidemiologic research to evaluate health outcomes, refine measurement algorithms, and identify potential study biases. In pharmacoepidemiologic research, administrative claims and other structured healthcare databases provide longitudinal detail suitable for similar purposes but are typically presented in tabular formats that are cumbersome to interpret. This study introduces PEPRVision, a visualization framework that transforms structured longitudinal healthcare data into intuitive graphics to support patient profile review in pharmacoepidemiology research. PEPRVision was developed based on principles of preattentive visual processing, leveraging visual attributes such as color, spatial position, shape, and length to facilitate rapid pattern recognition. We demonstrated PEPRVision using US administrative claims data through four case studies: (1) evaluating a claims-based algorithm for measurement of congenital hearing loss, (2) enhancing study design decisions by identifying reverse causation in a drug safety study, (3) assessing the face validity of signals of potential teratogenicity detected through data mining for triage for advancement to causal inference studies, and (4) identifying mechanisms of inappropriate prenatal exposure to teratogenic medications to inform improvement of risk mitigation strategies. As a complement to population-based causal inference studies, PEPRVision facilitates review of patient-level variation, enhancing the validity and clinical relevance of pharmacoepidemiologic research.