Background: The growth of data science and artificial intelligence offers novel healthcare applications and research possibilities. Patients should be able to make informed choices about using healthcare. Therefore, they must be provided with lay information about new technology. A team consisting of academic researchers, health professionals, and public contributors collaboratively co-designed and co-developed the new resource offering that information. In this paper, we evaluate this novel approach to co-production.
Methods: We used participatory evaluation to understand the co-production process. This consisted of creative approaches and reflexivity over three stages. Firstly, everyone had an opportunity to participate in three online training sessions. The first one focused on the aims of evaluation, the second on photovoice (that included practical training on using photos as metaphors), and the third on being reflective (recognising one's biases and perspectives during analysis). During the second stage, using photovoice, everyone took photos that symbolised their experiences of being involved in the project. This included a session with a professional photographer. At the last stage, we met in person and, using data collected from photovoice, built the mandala as a representation of a joint experience of the project. This stage was supported by professional artists who summarised the mandala in the illustration.
Results: The mandala is the artistic presentation of the findings from the evaluation. It is a shared journey between everyone involved. We divided it into six related layers. Starting from inside layers present the following experiences (1) public contributors had space to build confidence in a new topic, (2) relationships between individuals and within the project, (3) working remotely during the COVID-19 pandemic, (4) motivation that influenced people to become involved in this particular piece of work, (5) requirements that co-production needs to be inclusive and accessible to everyone, (6) expectations towards data science and artificial intelligence that researchers should follow to establish public support.
Conclusions: The participatory evaluation suggests that co-production around data science and artificial intelligence can be a meaningful process that is co-owned by everyone involved.
Keywords: AI; Background; Co-production; Data science; Evaluation; Photovoice.
Modern technology offers new treatment options for patients and novel avenues of research. However, there is limited available information in easily understandable language for the public explaining how technology relates to them and could influence their healthcare. The researchers, healthcare professionals and public members worked together collaboratively to address this problem by creating new materials for the public. Our paper explores that project through creative methods. Firstly, everyone involved was offered an opportunity to attend training sessions. Then, people took photos and described them to illustrate to others what is their experience of working together. Finally, we all met to use included photos as building blocks to present a shared experience in the project. Afterwards, the professional artist included it as one circular illustration with six interlinked layers. These layers present everyone’s experiences (from inside) (1) is about the opportunity to build confidence in a new topic, (2) relationships with others, (3) working remotely during the pandemic, (4) motivation that influenced people to become involved in this particular piece of work, (5) expectation that the project needs be inclusive and accessible, (6) ethical principles that researchers using new technology should follow. We showed that it is possible for researchers, healthcare professionals and members of the public to feel joint ownership of the project and that working together can be meaningful to everyone.
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