Estimating Data Sharing in Patient Portal Ecosystems from Privacy Consent Interfaces

Abstract

The proliferation of patient portal ecosystems has fundamentally transformed healthcare delivery, offering unprecedented opportunities for secondary data use in medical research and public health surveillance. However, the success of these data-driven initiatives relies heavily on patient willingness to share their personal health information. This paper investigates the predictive relationship between privacy consent interface design and patient data sharing behaviors within patient portal ecosystems. Utilizing a longitudinal cohort study design, we analyze how varying levels of interface granularity, transparency, and cognitive load influence the consent decisions of a diverse patient population. Over a twenty-four-month period, patient interactions with three distinct consent interface typologies were monitored to capture behavioral data, which was subsequently analyzed using multivariate logistic regression and machine learning classification techniques. The findings indicate that interface design serves as a critical determinant of data sharing, with granular, highly transparent interfaces yielding significantly higher sustained sharing rates compared to traditional, broad-consent models. Furthermore, predictive modeling reveals that interface engagement metrics, such as time spent on the consent page and the frequency of policy review clicks, are robust predictors of eventual sharing behavior. This research contributes to the broader discourse on health information technology by providing empirical evidence that privacy interfaces are not merely administrative hurdles, but active modulators of patient trust and data philanthropy. The insights derived from this study offer actionable guidelines for health informatics professionals aiming to optimize patient portal architectures for ethical and effective health data mobilization.