# A GPT-reinforced social robot for patient communication: a pilot study

## TL;DR

This paper explores using a GPT-powered social robot to improve patient communication in healthcare, showing promising results in a pilot study.

## Contribution

The novel contribution is a GPT-reinforced social robot designed to deliver health information and support patient communication in clinical settings.

## Key findings

- The robot showed above-average attractiveness and novelty in lab tests but scored lower in dependability and efficiency.
- OA patients and healthcare professionals reported positive trends in efficiency and acceptability during clinical implementation.
- Semi-structured interviews provided deeper insights into user experience and acceptance of the robot.

## Abstract

Quality healthcare requires effective patient communication. However, lack of personnel and increasing demands on healthcare professionals (HCPs) create a need for innovative solutions that enhance accessibility and delivery of information to patients.

We propose an innovative method to convey treatment and disease information using an Artificial Intelligence (AI)-driven social robotic physical interface. The aim of this study is to develop and test the feasibility of using a social robot that can convincingly provide health information in patient dialogues within clinical practice, to support patient communication and information exchange.

This paper sets out the architectural approach of an AI-reinforced social robot connected to whitelisted validated clinical sources using a Generative Pre-training Transformer (GPT)-based Large Language Model (LLM). We describe experimental results in a lab-based pilot feasibility study, and then highlight related results for user experience in clinical practice implementation for an osteoarthritis (OA) use case, in which the robot answers osteoarthritis-related questions. Results were obtained after end-user engagement using the User Experience Questionnaire (UEQ) and semi-structured interviews.

UEQ results were obtained in a lab-based pilot test (n = 20) and with OA patients (n = 21) and healthcare professionals (n = 7). Above average/good attractiveness, perspicuity and stimulation were reported in the pilot test; novelty was excellent, yet dependability and efficiency were reported below average. In the clinical setting, Patient UEQ score resulted in mean 2.13 with values ranging from 1.7 to 2.5, indicating a positive trend in efficiency, inventiveness and acceptability. HCPs UEQ scores reached mean 1.89, with all values above 1 except for excitement of usage, which scored 0.8 (SD 1.3). Semi-structured interviews added in-depth enrichment of the data.

In summary, this paper demonstrates the feasibility of implementing a GPT-reinforced social robot for patient communication in clinical practice.