ESTRO 2024 - Abstract Book

S5664

RTT - Patient experience and quality of life

ESTRO 2024

(3,4) and have relied on deployment of online surveys. There has only been 1 patient-facing study(5) which deployed a simple survey to patients while on treatment and concluded that more in-depth research was needed. This paper presents the results of a qualitative study that aimed to explore the patient perception concerning the involvement of AI in their radiotherapy.

Material/Methods:

A phenomenological approach was adopted utilising a focus group interview(6) to explore Service User perceptions of AI use in radiotherapy. Participants were recruited via email invitations distributed by regional radiotherapy support groups. The focus group was conducted online and was planned for a 1-hour duration. Recorded audio was transcribed professionally and passed to 2 independent researchers for thematic analysis. Analysis adopted the constant comparison method(7) using an iterative 3 phase process of open, axial and selective coding. All analysis and themes from the focus group were shared with participants to confirm accuracy.

Results:

There were 5 participants in the focus group and the session lasted for 70 minutes until the participants were satisfied that they had reached data saturation. No participants dropped out. Four main themes emerged from the data, along with associated subthemes.

The first theme related to the speed of the care pathway. Participants felt that AI could speed up the diagnosis and referral process, reduce waiting times and thus enable wider access to radiotherapy machines.

The second theme expressed their concern to maintain human input. They felt that humans should be making final decisions and retaining oversight. They expressed a need for face-to-face consultations and suggested that AI could give healthcare professionals’ more time to help patients directly. The third theme regarded AI’s role as a tool; participants wanted transparency regarding its use but acknowledged that it would be a valuable tool to help clinicians and hopefully improve accuracy and reduce frequency of human error. The final theme related to the patient experience and identified how AI could help to improve the patient experience. They stressed that patients need human interactions but acknowledged that AI will be a large part of normal life from now on. The focus group participants demonstrated useful insight into the emerging role of AI within the radiotherapy pathway. They strongly supported the use of AI to help increased accuracy and speed of diagnosis but emphasised the importance of maintaining human interaction with their clinicians. This aligns well with other studies(3-6). They identified that the time saved would be used to treat more patients, but hoped that human interaction would not be compromised. As proliferation of such systems increases throughout the radiotherapy pathway, more time will be needed to perform checks. This could result in an inverse relationship developing between time efficiency savings, and the number of AI systems within a given radiotherapy pathway. The group agreed that AI should be used as a tool to supplement the role of the oncologist and not to replace it. Thus, they expressed the need for AI implementation to be cautious and for patients to be informed on how it is being used. They were concerned with oversight, testing, and monitoring of AI systems. This correlates closely with patient feedback regarding wider use of AI in healthcare(6) and also with Radiographer concerns about safety and oversight(4). There are still no agreed national frameworks for the independent assessment, testing, safe implementation and monitoring of AI systems.