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The increasing financial burden and complexity of health-care services, exacerbated by factors such as an ageing population and the rise of chronic conditions, necessitate comprehensive and integrated care approaches. While co-created service design has proven valuable in transforming some service industries, its application to the health-care industry is not well understood. This study aims to examine how health consumers are involved in health-care service co-creation.

The study searched 11 electronic databases for peer-reviewed articles published between 2010 and 2019. Additionally, hand searches of reference lists from included studies, Google^© citation searches and searches for grey literature were conducted. The Whittemore and Knafl integrative framework guided the systematic review, and Callahan’s 6 Ws framework was used to extract data from the included articles, facilitating comparisons.

The authors identified 21 articles, mainly from the UK, North America and Australia. Despite the need for more research, findings reveal limited and geographically narrow empirical studies with restricted theory and method applications. From these findings, the authors constructed a conceptual model to enhance nuanced understanding.

This study offers four contributions. First, it introduces the Health Service Design Transformation Model for Comprehensive Consumer Co-Creation, illustrating health consumers’ multifaceted roles in shaping services. Second, consumer vulnerabilities in co-creating services are identified, linked to diverse consumer groups, power dynamics and decision complexity. Third, this study suggests broadening participant inclusion may enhance consumer-centricity, inclusivity and innovation in service design. Finally, the research agenda explores consumer experiences, organizational dynamics, value outcomes and co-creation theory for health-care service advancement.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.