Survey Researchers Assess Bot Detection Effectiveness
In a 2026 study, UCSF researchers and partners analyzed responses to a cross-sectional, web-based statewide California survey of caregivers to evaluate methods for detecting bot-driven fraudulent records. They received 646 valid and 905 fraudulent responses, finding that most recommended detection methods had low sensitivity; combinations of fraud markers and blocking repeated-response blocks improved detection. Removing fraud materially altered key study outcomes, such as wait times.
Scoring Rationale
Provides actionable, empirically evaluated detection methods; limited generalizability from a single survey study reduces broader certainty.
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