Participatory evaluation of the process of co-producing resources for the public on data science and artificial intelligence

Abstract 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

‘A good decision is the one that feels right for me’: codesign with patients to inform theoretical underpinning of a decision aid website

Introduction: patient decision aids (PtDA) complement shared decision-making with healthcare professionals and improve decision quality. However, PtDA often lack theoretical underpinning. We are codesigning a PtDA to help people with increased genetic cancer risks manage choices. The aim of an innovative workshop described here was to engage with the people who will use the PtDA regarding the theoretical underpinning and logic model outlining our hypothesis of how the PtDA would lead to more informed decision-making. Methods: short presentations about psychological and behavioural theories by an expert were interspersed with facilitated, small-group discussions led by patients. Patients were asked what is important to them when they make health decisions, what theoretical constructs are most meaningful and how this should be applied to codesign of a PtDA. An artist created a visual summary. Notes from patient discussions and the artwork were analysed using reflexive thematic analysis. Results: the overarching theme was: It's personal. Contextual factors important for decision-making were varied and changed over time. There was no one ‘best fit’ theory to target support needs in a PtDA, suggesting an inductive, flexible framework approach to programme theory would be most effective. The PtDA logic model was revised based on patient feedback. Conclusion: meaningful codesign of PtDA including discussions about the theoretical mechanisms through which they support decision-making has the potential to lead to improved patient care through understanding the intricately personal nature of health decisions, and tailoring content and format for holistic care. Patient Contribution: Patients with lived experience were involved in codesign and coproduction of this workshop and analysis as partners and coauthors. Patient discussions were the primary data source. Facilitators provided a semi-structured guide, but they did not influence the patient discussions or provide clinical advice. The premise of this workshop was to prioritise the importance of patient lived experience: to listen, learn, then reflect together to understand and propose ideas to improve patient care through codesign of a PtDA.</p