# Public and Patient Involvement in Artificial Intelligence and Big Data Healthcare Research: An Exploration of Issues and Challenges Within the AI‐Multiply Project

## TL;DR

This paper explores challenges in involving the public and patients in AI and big data healthcare research using the AI-Multiply project as a case study.

## Contribution

The study provides insights into applying PPIE in complex AI and data-driven healthcare research.

## Key findings

- Public contributors focused on person-centered outcomes, while researchers prioritized data-driven metrics.
- Mutual trust and integration between researchers and contributors developed over time.
- Existing PPIE guidance on clarity and facilitation remains relevant in AI and big data contexts.

## Abstract

Public and patient involvement and engagement (PPIE) is intended to shape research priorities and improve relevance and impact. However, implementing PPIE in complex fields such as artificial intelligence (AI) and big data health research presents specific challenges. This study explores the issues and barriers to meaningful PPIE using the AI‐Multiply project as a case example.

AI‐Multiply is a large, interdisciplinary UK‐based research project using AI and routine health data to investigate trajectories of multiple long‐term conditions and polypharmacy. PPIE was embedded across all five work packages. We used a mixed‐methods approach, drawing on CUBE framework surveys, PPIE feedback forms and impact logs to evaluate involvement. Data were analysed thematically using a ‘follow‐a‐thread’ approach to identify key issues across sources.

Three themes were identified: (1) differing priorities—public contributors prioritised person‐centred outcomes, while researchers focused on data‐driven healthcare metrics, often constrained by data availability; (2) movement on both sides—both researchers and contributors expressed early apprehension, but mutual trust and integration developed over time; and (3) the importance of established guidance—many issues raised echoed longstanding PPIE guidance on clarity, feedback and facilitation.

While AI and data‐specific challenges exist, many PPIE issues in this context relate to applying existing good practice in complex projects. Strong PPIE leadership, early expectation‐setting and consistent facilitation are critical for success. Findings will inform the development of practical tools to support involvement in data‐driven research.

Public contributors with lived experience of multiple long‐term conditions contributed to the interpretation of data and co‐authored this manuscript.