Adapting Patient and Public Involvement processes in response to the Covid‐19 pandemic

Snowdon, Claire; Silver, Elizabeth; Charlton, Paul; Devlin, Brian; Greenwood, Emma; Hutchings, Andrew; Moug, Susan; Vohra, Ravinder; Grieve, Richard

doi:10.1111/hex.13771

Health Expectations

2023

DOI: 10.1111/hex.13771

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Adapting Patient and Public Involvement processes in response to the Covid‐19 pandemic

Claire Snowdon

Elizabeth Silver²,

Paul Charlton³

et al.

Abstract: Background: The COVID-19 pandemic brought rapid and major changes to research, and those wishing to carry out Patient and Public Involvement (PPI) activities faced challenges, such as restrictions on movement and contact, illness, bereavement and risks to potential participants. Some researchers moved PPI to online settings during this time but remote consultations raise, as well as address, a number of challenges.It is important to learn from PPI undertaken in this period as face-to-face consultation may no l… Show more

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Cited by 4 publications

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Adapting Patient and Public Involvement processes in response to the Covid‐19 pandemic

Snowdon

Silver²,

Charlton³

et al. 2023

Health Expectations

Self Cite

View full text Add to dashboard Cite

Background: The COVID-19 pandemic brought rapid and major changes to research, and those wishing to carry out Patient and Public Involvement (PPI) activities faced challenges, such as restrictions on movement and contact, illness, bereavement and risks to potential participants. Some researchers moved PPI to online settings during this time but remote consultations raise, as well as address, a number of challenges.It is important to learn from PPI undertaken in this period as face-to-face consultation may no longer be the dominant method for PPI.Methods: UK stay-at-home measures announced in March 2020 necessitated immediate revisions to the intended face-to-face methods of PPI consultation for the ESORT Study, which evaluated emergency surgery for patients with common acute conditions. PPI plans and methods were modified to all components being online. We describe and reflect on: initial plans and adaptation; recruitment; training and preparation; implementation, contextualisation and interpretation. Through first-hand accounts we show how the PPI processes were developed, experienced and viewed by different partners in the process.Discussion and Conclusions: While concerns have been expressed about the possible limiting effects of forgoing face-to-face contact with PPI partners, we found important benefits from the altered dynamic of the online PPI environment. There were increased opportunities for participation which might encourage the involvement of a broader demographic, and unexpected benefits in that the online platform seemed to have a 'democratising' effect on the meetings, to the benefit of the PPI processes and outcomes. Other studies may however find that their particular research context raises particular challenges for the use of online methods, especially in relation to representation and inclusion, as new barriers to participation may be raised. It is important that methodological challenges are addressed, and researchers provide detailed examples of novel methods for discussion and empirical study.

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Adapting Patient and Public Involvement processes in response to the Covid‐19 pandemic

Snowdon

Silver²,

Charlton³

et al. 2023

Health Expectations

Self Cite

View full text Add to dashboard Cite

show abstract

Involving Knowledge Users in Health Services Research: Collective Reflections and Learning From a National Evaluation of Recurrent Miscarriage Services

Hennessy,

Dennehy,

O'Leary

et al. 2024

Health Expectations

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IntroductionInvolving knowledge users in research can facilitate the translation of evidence into policy and practice. How to best involve and support various types of knowledge users, including patient and public involvement contributors, in research is an identified knowledge gap. We conducted a national evaluation of recurrent miscarriage care supported by a Research Advisory Group (convened in March 2020) comprising a range of knowledge users, including parent advocates and people involved in the management/provision of services. The Group met virtually nine times, and actively collaborated beyond this on various research activities across the project. In this paper, we share insights from our collective evaluation of these involvement efforts.MethodsWe drew on records kept over the timespan of the project to describe involvement activities and experiences. Advisory Group members participated in an electronic survey to assess their involvement experiences at two time points (February 2021 and May 2022); we analysed the results descriptively. In May 2022, we hosted a virtual World Café, comprising the Research Team and Advisory Group, to explore what worked well and what could have been improved regarding involvement activities within the project; we analysed this data thematically.ResultsResponses to both rounds of the survey were positive, with people reporting: their ability to discuss research issues, contribute to the research, express their own views; feeling valued as a partner; that they could bring their own ideas and values to the research; perceived potential to gain status, expertise, or credibility because of their involvement. Themes constructed from the Word Café discussions highlighted that structural and relational spaces shaped the accessibility and experience of involvement.ConclusionMembers reported a positive and rewarding experience with a visible impact on the research process but highlighted issues with the feasibility and scope of the research protocol and challenges to autonomous involvement in aspects reliant on clinical expertise. Our analysis reinforces that the relational nature of involvement takes precedence over instrumental aspects or techniques. Realistic study protocols that allow time and space for the evolving nature of research with knowledge users, and institutional and financial support to facilitate meaningful involvement, are needed.Patient or Public ContributionPeople with lived experience of recurrent miscarriage/pregnancy loss were involved in this evaluation—as members of the RE:CURRENT Research Advisory Group, contributing to the methodology, evaluation activities, interpretation and reporting of findings and insights.

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An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions

Moler-Zapata,

Hutchings,

Grieve

et al. 2024

Med Decis Making

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Background Machine learning (ML) methods can identify complex patterns of treatment effect heterogeneity. However, before ML can help to personalize decision making, transparent approaches must be developed that draw on clinical judgment. We develop an approach that combines clinical judgment with ML to generate appropriate comparative effectiveness evidence for informing decision making. Methods We motivate this approach in evaluating the effectiveness of nonemergency surgery (NES) strategies, such as antibiotic therapy, for people with acute appendicitis who have multiple long-term conditions (MLTCs) compared with emergency surgery (ES). Our 4-stage approach 1) draws on clinical judgment about which patient characteristics and morbidities modify the relative effectiveness of NES; 2) selects additional covariates from a high-dimensional covariate space ( P > 500) by applying an ML approach, least absolute shrinkage and selection operator (LASSO), to large-scale administrative data ( N = 24,312); 3) generates estimates of comparative effectiveness for relevant subgroups; and 4) presents evidence in a suitable form for decision making. Results This approach provides useful evidence for clinically relevant subgroups. We found that overall NES strategies led to increases in the mean number of days alive and out-of-hospital compared with ES, but estimates differed across subgroups, ranging from 21.2 (95% confidence interval: 1.8 to 40.5) for patients with chronic heart failure and chronic kidney disease to −10.4 (−29.8 to 9.1) for patients with cancer and hypertension. Our interactive tool for visualizing ML output allows for findings to be customized according to the specific needs of the clinical decision maker. Conclusions This principled approach of combining clinical judgment with an ML approach can improve trust, relevance, and usefulness of the evidence generated for clinical decision making. Highlights Machine learning (ML) methods have many potential applications in medical decision making, but the lack of model interpretability and usability constitutes an important barrier for the wider adoption of ML evidence in practice. We develop a 4-stage approach for integrating clinical judgment into the way an ML approach is used to estimate and report comparative effectiveness. We illustrate the approach in undertaking an evaluation of nonemergency surgery (NES) strategies for acute appendicitis in patients with multiple long-term conditions and find that NES strategies lead to better outcomes compared with emergency surgery and that the effects differ across subgroups. We develop an interactive tool for visualizing the results of this study that allows findings to be customized according to the user’s preferences.

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Adapting Patient and Public Involvement processes in response to the Covid‐19 pandemic

Cited by 4 publications

References 18 publications

Adapting Patient and Public Involvement processes in response to the Covid‐19 pandemic

Adapting Patient and Public Involvement processes in response to the Covid‐19 pandemic

Involving Knowledge Users in Health Services Research: Collective Reflections and Learning From a National Evaluation of Recurrent Miscarriage Services

An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions

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