Background This paper considers remote working in patient public involvement and engagement (PPIE) in health and social care research. With the advent of the Covid-19 pandemic and associated lock-down measures in the UK (from March 2020), PPIE activities switched to using remote methods (e.g., online meetings), to undertake involvement. Our study sought to understand the barriers to and facilitators for remote working in PPIE by exploring public contributors’ and PPIE professionals’ (people employed by organisations to facilitate and organise PPIE), experiences of working remotely, using online and digital technologies. A particular focus of our project was to consider how the ‘digital divide’ might negatively impact on diversity and inclusion in PPIE in health and social care research. Methods We used a mixed method approach: online surveys with public contributors involved in health and social care research, online surveys with public involvement professionals, and qualitative interviews with public contributors. We co-produced the study with public contributors from its inception, design, subsequent data analysis and writing outputs, to embed public involvement throughout the study. Results We had 244 respondents to the public contributor survey and 65 for the public involvement professionals (PIPs) survey and conducted 22 qualitative interviews. Our results suggest public contributors adapted well to working remotely and they were very positive about the experience. For many, their PPIE activities increased in amount and variety, and they had learnt new skills. There were both benefits and drawbacks to working remotely. Due to ongoing Covid restrictions during the research project, we were unable to include people who did not have access to digital tools and our findings have to be interpreted in this light. Conclusion Participants generally favoured a mixture of face-to-face and remote working. We suggest the following good practice recommendations for remote working in PPIE: the importance of a good moderator and/or chair to ensure everyone can participate fully; account for individual needs of public contributors when planning meetings; provide a small expenses payment alongside public contributor fees to cover phone/electricity or WiFi charges; and continue the individual support that was often offered to public contributors during the pandemic.

ObjectiveArtificial intelligence (AI) will have a significant impact on healthcare over the coming decade. At the same time, health inequity remains one of the biggest challenges. Primary care is both a driver and a mitigator of health inequities and with AI gaining traction in primary care, there is a need for a holistic understanding of how AI affect health inequities, through the act of providing care and through potential system effects. This paper presents a systematic scoping review of the ways AI implementation in primary care may impact health inequity.DesignFollowing a systematic scoping review approach, we searched for literature related to AI, health inequity, and implementation challenges of AI in primary care. In addition, articles from primary exploratory searches were added, and through reference screening.The results were thematically summarised and used to produce both a narrative and conceptual model for the mechanisms by which social determinants of health and AI in primary care could interact to either improve or worsen health inequities.Two public advisors were involved in the review process.Eligibility criteriaPeer-reviewed publications and grey literature in English and Scandinavian languages.Information sourcesPubMed, SCOPUS and JSTOR.ResultsA total of 1529 publications were identified, of which 86 met the inclusion criteria. The findings were summarised under six different domains, covering both positive and negative effects: (1) access, (2) trust, (3) dehumanisation, (4) agency for self-care, (5) algorithmic bias and (6) external effects. The five first domains cover aspects of the interface between the patient and the primary care system, while the last domain covers care system-wide and societal effects of AI in primary care. A graphical model has been produced to illustrate this. Community involvement throughout the whole process of designing and implementing of AI in primary care was a common suggestion to mitigate the potential negative effects of AI.ConclusionAI has the potential to affect health inequities through a multitude of ways, both directly in the patient consultation and through transformative system effects. This review summarises these effects from a system tive and provides a base for future research into responsible implementation.

IntroductionBig data research has grown considerably over the last two decades. This presents new ethical challenges around consent, data storage and anonymisation. Big data research projects require public support to succeed and it has been argued that one way to achieve this is through public involvement and engagement. To better understand the role public involvement and engagement can play in big data research, we will review the current literature. This protocol describes the planned review methods.Methods and analysisOur review will be conducted in two stages. In the first stage, we will conduct a scoping review using Arksey and O’Malley methodology to comprehensively map current evidence on public involvement and engagement in big data research. Databases (CINAHL, Health Research Premium Collection, PubMed, Scopus, Web of Science) and grey literature will be searched for eligible papers. We provide a narrative description of the results based on a thematic analysis. In the second stage, out of papers found in the scoping review which discuss involvement and engagement strategies, we will conduct a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, exploring the delivery and effectiveness of these strategies. We will conduct a qualitative synthesis. Relevant results from the quantitative studies will be extracted and placed under qualitative themes. Individual studies will be appraised through Mixed Methods Appraisal Tool (MMAT), we will then assess the overall confidence in each finding through Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual). Results will be reported in a thematic and narrative way.Ethics and disseminationThis protocol sets out how the review will be conducted to ensure rigour and transparency. Public advisors were involved in its development. Ethics approval is not required. Review findings will be presented at conferences and published in peer-reviewed journals.

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ObjectivesPublic involvement and engagement (PIE)) is playing an increasingly important role in big data initiatives and projects. It is therefore important to gain a deeper understanding of the different approaches used. ApproachThis study explores PIE using ethnographically-informed qualitative case studies. The case studies include: three citizen juries, each one carried out over eight days and that asked jurors to consider different real-world health data initiatives; and a public panel set up by a regional databank that carries out data linking. Data collection is ongoing and I will be continuing to carry out close observations of activities, and conducting semi-structured 1:1 interviews with those that organise and have taken part in the activities. ResultsData collection so far comprises completed observations at the citizen juries (~96 hours), ongoing observations of the public panel meetings (~15 hours), and thirty semi-structured 1:1 interviews with public contributors and other stakeholders about their experiences of the activities they were involved in. Early data analysis indicates key themes of: jurors feeling heard, but unsure whether anybody was listening; stakeholders being impressed by informed jurors, but raising concerns over contributors becoming too ‘expert’; how who is at the table and what information is presented impacts what is discussed; differences between online and in-person participation; and public involvement not being a substitute for informing the public about how their data is used. Conclusion‘Who’ is involved, and ‘how’ PPIE activities are designed and run can facilitate or constrain discussion, enhancing or limiting public contributions. If public involvement is to achieve its aims, including increasing trustworthiness, deeper consideration of these factors by those who seek the public’s views in their data projects is recommended.