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Dryad

Do privacy assurances work? A study of truthfulness in healthcare history data collection

Cite this dataset

Masters, Tamara M.; Keith, Mark; Hess, Rachel; Jenkins, Jeffrey (2023). Do privacy assurances work? A study of truthfulness in healthcare history data collection [Dataset]. Dryad. https://doi.org/10.5061/dryad.qrfj6q5k8

Abstract

Patients often provide untruthful information about their health to avoid embarrassment, evade treatment, or prevent financial loss. Privacy disclosures (e.g. HIPAA) intended to dissuade privacy concerns may actually increase patient lying. We used new mouse tracking-based technology to detect lies through mouse movement (distance and time to response) and patient answer adjustment in an online controlled study of 611 potential patients, randomly assigned to one of six treatments. Treatments differed in the notices patients received before health information was requested, including notices about privacy, benefits of truthful disclosure, and risks of inaccurate disclosure. Increased time or distance of device mouse movement and greater adjustment of answers indicate less truthfulness. Mouse tracking revealed a significant overall effect (p < 0.001) by treatment on the time to reach their final choice. The control took the least time indicating greater truthfulness and the privacy + risk group took the longest indicating the least truthfulness. Privacy, risk, and benefit disclosure statements led to greater lying. These differences were moderated by gender. Mouse tracking results largely confirmed the answer adjustment lie detection method with an overall treatment effect (p < .0001) and gender differences (p < .0001) on truthfulness. Privacy notices led to decreased patient honesty. Privacy notices should perhaps be administered well before personal health disclosure is requested to minimize patient untruthfulness. Mouse tracking and answer adjustment appear to be healthcare lie-detection methods to enhance optimal diagnosis and treatment.

Methods

The data were collected as part of a controlled experiment using Amazon Mechanical Turk, Qualtrics, and JavaScript-based mouse tracking technology.