Designing Explainable AI for Healthcare Reviews: Guidance on Adoption and Trust

TL;DR

This study evaluates an explainable AI system for healthcare reviews, showing that transparency, simplicity, and perceived usefulness significantly influence user trust and adoption, with design guidance provided for effective explanations.

Contribution

It offers novel insights into user preferences and technical considerations for explainable AI in healthcare reviews, along with actionable design guidance.

Findings

High perceived usefulness promotes adoption.

Transparency and simplicity increase trust.

Technical challenges affect explanation effectiveness.

Abstract

Patients increasingly rely on online reviews when choosing healthcare providers, yet the sheer volume of these reviews can hinder effective decision-making. This paper summarises a mixed-methods study aimed at evaluating a proposed explainable AI system that analyses patient reviews and provides transparent explanations for its outputs. The survey (N=60) indicated broad optimism regarding usefulness (82% agreed it saves time; 78% that it highlights essentials), alongside strong demand for explainability (84% considered it important to understand why a review is classified; 82% said explanations would increase trust). Around 45% preferred combined text-and-visual explanations. Thematic analysis of open-ended survey responses revealed core requirements such as accuracy, clarity and simplicity, responsiveness, data credibility, and unbiased processing. In addition, interviews with AI experts provided deeper qualitative insights, highlighting technical considerations and potential challenges for different explanation methods. Drawing on TAM and trust in automation, the findings suggest that high perceived usefulness and transparent explanations promote adoption, whereas complexity and inaccuracy hinder it. This paper contributes actionable design guidance for layered, audience-aware explanations in healthcare review systems.