Partnering with patients in the production of evidence

Defining patient's experiential knowledge: Who, what and how patients know. A narrative critical review

Experiential knowledge is today increasingly valued in health-care practices, public health policies and health research and education programs. However, despite popular and institutional success, the concept remains loosely defined with the result of weakening its heuristic scope and paving the way for its commodification. In this article, we seek to provide a finer characterisation of patients' experiential knowledge's features and specificities through a critical narrative review of humanities and social science (HSS) literature published in English and French (1976-2021). Inspired by Jovchelovitch's analysis of social knowledge, we seek to highlight the diversity and plurality of forms and articulations of knowledge that characterise experiential knowledge, as well as the gradual, dynamic and entangled process that leads from experience to knowledge and expertise. Our analysis points to the need for future research to adopt a resolutely pragmatic and situated orientation in the study of experiential knowledge and the new figures of the contemporary patient that they help to create.

Identifying the top research priorities in medically not yet explained symptoms (MNYES): a James Lind Alliance priority setting partnership

OBJECTIVES

This study establishes research priorities for medically not yet explained symptoms (MNYES), also known as persistent physical symptoms or medically unexplained symptoms, from the perspective of patients, caregivers and clinicians, in a priority setting partnership (PSP) following the James Lind Alliance (JLA) approach. Research into such symptoms in general has been poorly funded over the years and so far has been primarily researcher-led with minimal input from patients, caregivers and clinicians; and sometimes has been controversial.

DESIGN

JLA PSP method. The PSP termed these symptoms MNYES.

METHODS

The study was conducted according to the JLA's detailed methodology for conducting priority setting exercises. It involved five key stages: defining the appropriate term for the conditions under study by the PSP Steering Group; gathering questions on MNYES from patients, caregivers and clinicians in a publicly accessible survey; checking these research questions against existing evidence; interim prioritisation in a second survey; and a final multi-stakeholder consensus meeting to determine the top 10 unanswered research questions using the modified nominal group methodology.

RESULTS

Over 700 responses from UK patients, caregivers and clinicians were identified in the two surveys and charities contributed from a broad range of medical specialties and primary care. The final top 10 unanswered research questions cover, among others: treatment strategies, personalisation of treatment, collaborative care pathways, training for clinicians and outcomes that matter to patients.

INTERPRETATION

The top 10 unanswered research questions are expected to generate much needed, relevant and impactful research into MNYES.

Patient and public involvement to build trust in artificial intelligence: A framework, tools, and case studies

Artificial intelligence (AI) is increasingly taking on a greater role in healthcare. However, hype and negative news reports about AI abound. Integrating patient and public involvement (PPI) in healthcare AI projects may help in adoption and acceptance of these technologies.

We argue that AI algorithms should also be co-designed with patients and healthcare workers.

We specifically suggest (1) including patients with lived experience of the disease, and (2) creating a research advisory group (RAG) and using these group meetings to walk patients through the process of AI model building, starting with simple (e.g., linear) models.

We present a framework, case studies, best practices, and tools for applying participative data science to healthcare, enabling data scientists, clinicians, and patients to work together. The strategy of co-designing with patients can help set more realistic expectations for all stakeholders, since conventional narratives of AI revolve around dystopia or limitless optimism.

Hype and negative news reports about artificial intelligence (AI) abound. Involving patients in healthcare AI projects may help in adoption and acceptance of these technologies. We argue that AI algorithms should be co-designed with patients and healthcare workers.

We show examples of how to involve patients in AI research and how patients can build trust in algorithms. We share some best practices, case studies, a framework, and computational tools.

Avenues for future work include guidelines for patient and public involvement in AI healthcare research for funding bodies and regulatory agencies.

An understanding of what AI can and cannot do, and a realistic appraisal of risks and benefits, may help in adoption and democratize access to AI for healthcare.