In the past five years of the Applied Research Collaboration Northwest London (ARC NWL) Information and Intelligence theme, we have sought to involve patients throughout our research and improvement work. This blog summarises our conversations with three public partners: Amandeep Chopra, Ana Urbina, and Angela Quilley, who volunteered their time with us to discuss methods for patient engagement in quantitative research from their own experiences, our current approach, and best practice guidelines. This blog post will be helpful to researchers, patients, and members of the public interested in exploring what their role could be in quantitative studies.
Drawing on the 4PI principles, we developed standards to ensure that we are following best practice for involving patients and members of the public in our work.
One of our overarching principles is incorporating PPIE in all stages of our work, from planning to dissemination. We do not assume that everyone understands statistical research language and methodology. Therefore, we seek to bridge that gap as best as possible. The ways we will do this include providing training materials, consulting with public partners to write lay summaries of our work, developing glossaries to define specialised language, and consistently communicating to improve our ways of working.
Our primary purpose for involving patients and public partners in our research is to make our quantitative analysis accessible and to sense-check findings and interpretations against their lived experience, but we engage public partners throughout all stages of research. Public partners are present at our project steering meetings, serve as co-authors on publications, and attend NIHR Applied Research Collaboration collaborative learning events.
We also follow a consistent process of presenting our ideas for studies to SDRAG (the committee which includes members of the public who approve our usage of the WSIC data for our projects), holding project-specific steering group meetings, attending CL events, and compensating our public partners for their involvement.
The main impacts we have made are the dissemination of findings via publications and sharing our work with multidisciplinary audiences at conferences and collaborative learning events.
We identified three phases in our work: planning and prioritising, analysis and research, and impact and dissemination. We have compiled examples of our work involving patients and members of the public in each of these phases of our research, which are summarised below.
We currently have PhD students in our team, their work is a big part of what we do. In an application for PhD funding, one team member received a grant to conduct PPIE activities, which played a crucial role in refining their research questions. We also involved individuals with lived experience in writing the lay summary of the PhD application, ensuring that findings are accessible and meaningful to a broader audience.
Our team regularly presents work at the NWL ARC Collaborative Learning Events, held twice a year. The events provide an opportunity for the wider NWL ARC community to come together and share learning, focused on key issues facing the health and care system, and past, current and future work of the ARC. At these events, the involvement of patients and members of the public is crucial in forming ideas for new studies, providing feedback on current projects, and interpreting early findings presented. You can register for upcoming collaborative learning events on our website.
Many of our projects rely on the data we use from the Discover-NOW database, a deidentified database of patient healthcare engagement in North West London. All projects using this data are reviewed and approved by a committee comprising a mix of stakeholders, where patient representatives are an equal voice and hold one of the two co-chair positions. The committee is not an ethics approval board, but it helps ensure that data usage is appropriate and beneficial for the local population. Public partners review our research questions, study design, and intended outputs before taking the opportunity to ask us directly about our planned work.
In this data analysis study on patients receiving multiple medications, we involved a patient advisor and clinical colleagues to develop a method for identifying polypharmacy and to use this method to describe patterns of polypharmacy in NWL. This work was published as a peer-reviewed journal article, on which our patient advisor, Angela Quilley, was a co-author. We held steering group meetings throughout the development and writing to discuss our methods, share early findings and review interpretation of the results. We also explored the implications of the results and ensured that our findings could be understood by a wide variety of people.
For this project, we met with community and maternity champions in the NWL area to understand barriers and attitudes towards receiving vaccines during pregnancy. We asked them what they wanted to know about the research on the flu vaccine, aiming to fill in knowledge gaps. Additionally, we inquired about what was missing and how we might build on the findings to best support improvements in health and care for women and children. We shared preliminary findings with the group and published two papers: one in the British Journal of General Practice on influenza and another in Vaccine on the COVID-19 vaccine in pregnancy.
Through the scope of these projects, our teams and collaborators have overcome many barriers to achieve their aims. The challenges we face range from working within the limitations of available data to ensuring that we reach the right audience as we make our research translatable and sustainable. The success of these projects relies upon the networks of people who challenge our research plans and outputs to improve the quality of work, ensuring that any learning is beneficial and accessible to the populations being studied. By considering both the local impact and academic outputs from the beginning stages of every project, we can fill knowledge gaps and publish learning across both communities of practice.
To build upon these years of practice and experience, we set up sessions with our public partners, specifically devoting time to discussing the strengths and limitations of our current approach to their involvement and their recommendations for improving our ways of working together. Improving public involvement in quantitative research requires a multifaceted approach spanning the interpersonal and technical aspects of this kind of involvement. The group made the following recommendations:
Communication: Build relationships through regular meetings and carve out time to enact the principles of collaboration can foster a supportive environment for public partners.
Feedback: Establish feedback mechanisms to continuously collect and incorporate ideas and suggestions to inform improvements in how we conduct research involving patients and public partners. It's essential to communicate the benefits of the research to patients or members of the public, helping them understand the value of their involvement. Effective dissemination of results can further highlight these benefits.
Terminology: Create a glossary of both generic and specific terms to help bridge the gap between researchers and participants, ensuring that everyone understands the terminology used. Some existing resources we hope to draw from can be found here and here.
Training: Provide training to equip researchers with necessary skills to engage with public partners, as well as to equip public partners with skills they need to contribute to the work freely and equitably.
Overall, the recommendations were illuminating, and we would like to act on all of them at some point. A great first step towards this will be to work on a project-specific mini-glossary with public partners. We asked public partners to read through a published study in an ongoing work package of ours and pick out 5-10 terms that they believe many would benefit from having explained further. These terms could be project-specific (e.g. polypharmacy or disposition) or statistical (e.g. regression or interaction term), if they thought further explanation would be helpful. Next steps will be to consider what resources would be of benefit as a training programme for members of the public who would like to get involved in quantitative research using routinely collected electronic health record data.
This blog was written by our Information and Intelligence Theme and our Public Partners.