AI Framework for Co-Production of Patient Engagement in Healthcare Research

Panelyze is an AI-powered co-production system designed to augment Patient and Public Involvement and Engagement (PPIE) in healthcare research. The framework generates synthetic personas and panels, analyzes research proposals, and produces engagement artifacts like Plain English Summaries. Andrew Steele authored this 1.8 MB PDF resource, last updated in April 2026.

Use Cases

• Generate synthetic personas and panel discussions based on census data and lived experiences to simulate diverse public feedback.
• Perform semantic analysis of research proposals to identify terminology issues, such as drug naming conventions.
• Produce PPIE co-production artifacts like Plain English Summaries and Infographics from proposal content.
• Calculate the Panelyze Score, an algorithmic metric evaluating proposals against UK Standards for Public Involvement.
• Identify critical socio-economic barriers within proposals, such as potential wage loss from clinic visits.

Strengths

• Framework validated using a specific cardiovascular research proposal (CAPRIE-2).
• Defines a novel, five-step workflow for AI-augmented co-production.
• Released under a permissive CC-BY-4.0 license for broad reuse.

Limitations

• Dataset is a 1.8 MB PDF document describing a framework, not a traditional dataset of records or features.
• No quantitative data, sample size, or columnar data is provided for direct analysis.
• The synthetic personas and outputs are conceptual demonstrations, not empirically collected data.

Provenance

Source

Andrew Steele on figshare.

Collection Method

Describes a developed AI system (Panelyze) and its validation case.

Time Range

null

Freshness

Last updated in April 2026, indicating a forward-looking, conceptual publication.

Geography

Framework references UK Standards for Public Involvement; validation used a specific proposal.