Human-Precision Medicine Interaction: Public Perceptions of Polygenic Risk Score for Genetic Health Prediction

TL;DR

This study explores public perceptions of Polygenic Risk Scores in precision medicine, identifying barriers and proposing design guidelines to improve trust, inclusivity, and responsible use in human health contexts.

Contribution

It introduces the Human-Precision Medicine Interaction framework, extending HCI methods to address challenges in PRS adoption and responsible precision medicine practices.

Findings

Identified ten key barriers to PRS adoption.

Proposed five themes for improving PRS acceptance.

Developed design implications for responsible PRS frameworks.

Abstract

Precision Medicine (PM) transforms the traditional "one-drug-fits-all" paradigm by customising treatments based on individual characteristics, and is an emerging topic for HCI research on digital health. A key element of PM, the Polygenic Risk Score (PRS), uses genetic data to predict an individual's disease risk. Despite its potential, PRS faces barriers to adoption, such as data inclusivity, psychological impact, and public trust. We conducted a mixed-methods study to explore how people perceive PRS, formed of surveys (n=254) and interviews (n=11) with UK-based participants. The interviews were supplemented by interactive storyboards with the ContraVision technique to provoke deeper reflection and discussion. We identified ten key barriers and five themes to PRS adoption and proposed design implications for a responsible PRS framework. To address the complexities of PRS and enhance broader PM practices, we introduce the term Human-Precision Medicine Interaction (HPMI), which integrates, adapts, and extends HCI approaches to better meet these challenges.