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Patients, policy makers, and the public have access to many types of health and scientific data relevant to various individual and societal decisions. Yet, these audiences often struggle with the meaning and the potential usefulness of those data, so they may choose not to engage with the data at all. Scientific and health data are generally difficult to interpret, yet presentations often implicitly assume that the recipient has the necessary contextual knowledge to identify the data’s meaning. To address this problem, designers of data communications should go beyond considering audience characteristics (e.g., numeracy) and focus more on increasing information evaluability (a concept from the judgment and decision-making literatures). The challenge is understanding which data characteristics guide people’s ability to extract meaning from data in a given situation. Prioritizing use-relevant contextual information (e.g., by defining action thresholds, comparison standards, meaningful categories, and/or significant differences) is the single best thing experts can do to improve data communication effectiveness. Doing so increases the chances that the patient, public, or policy maker audience does not just know what their numbers are but also what they mean.

Helping People Know Whether Measurements Have Good or Bad Implications: Increasing the Evaluability of Health and Science Data Communications