OBJECTIVES: The growth of health data digitization and precision medicine has encouraged reform in how we collect, store, manage, and provide access to health data. An important consideration is the ways that patients trust and subsequently contribute their data. To better understand data sharing considerations, researchers have studied how individuals make data sharing decisions using heuristic processing. Our objective was to synthesize this research into patterns of behaviour that can guide the restructuring of data systems and policies to represent actual patient decision making.

METHODS: This scoping review collected primary data studies that examine the heuristics used by the public when making personal data sharing decisions, including – but not limited to – health data sharing. Web of Science (Clarivate), PsycInfo, CINAHL (EBSCO), Medline and Embase (OVID) were sourced following a consistent search strategy across databases. Themes were identified by capturing the type and frequency of heuristic studied and how they impacted decision-making.

RESULTS: The search produced 399 publications, 43 of which were included for data extraction. The majority of studies found a significant relationship between at least one heuristic and their participants’ decisions. Affect (emotional responses), social norms (following societal trends) and framing (the ordering of options) were the heuristics that were most studied. Only two studies examined health data sharing. The major themes identified were the impact of emotions, the knowledge and influence of peer behaviour, the effect of the decision environment, and the use of cues or framing.

CONCLUSIONS: There are heuristic patterns to personal data sharing behaviour that have significant impacts on individual decision-making. Deliberate cues and framing can further trigger heuristics in ways that suit data collection and privacy efforts. These patterns can be used to design infrastructure that supports precision medicine data needs, as well as privacy and collection policies that integrate real patient behaviour and preferences.