Establishing a Health CASCADE-Curated Open-Access Database to Consolidate Knowledge About Co-Creation: Novel Artificial Intelligence-Assisted Methodology Based on Systematic Reviews

Abstract

Background: Co-creation is an approach that aims to democratize research and bridge the gap between research and practice, but the potential fragmentation of knowledge about co-creation has hindered progress. A comprehensive database of published literature from multidisciplinary sources can address this fragmentation through the integration of diverse perspectives, identification and dissemination of best practices, and increase clarity about co-creation. However, two considerable challenges exist. First, there is uncertainty about co-creation terminology, making it difficult to identify relevant literature. Second, the exponential growth of scientific publications has led to an overwhelming amount of literature that surpasses the human capacity for a comprehensive review. These challenges hinder progress in co-creation research and underscore the need for a novel methodology to consolidate and investigate the literature.

Objective: This study aimed to synthesize knowledge about co-creation across various fields through the development and application of an artificial intelligence (AI)-assisted selection process. The ultimate goal of this database was to provide stakeholders interested in co-creation with relevant literature.

Methods: We created a novel methodology for establishing a curated database. To accommodate the variation in terminology, we used a broad definition of co-creation that encompassed the essence of existing definitions. To filter out irrelevant information, an AI-assisted selection process was used. In addition, we conducted bibliometric analyses and quality control procedures to assess content and accuracy. Overall, this approach allowed us to develop a robust and reliable database that serves as a valuable resource for stakeholders interested in co-creation.

Results: The final version of the database included 13,501 papers, which are indexed in Zenodo and accessible in an open-access downloadable format. The quality assessment revealed that 20.3% (140/688) of the database likely contained irrelevant material, whereas the methodology captured 91% (58/64) of the relevant literature. Participatory and variations of the term co-creation were the most frequent terms in the title and abstracts of included literature. The predominant source journals included health sciences, sustainability, environmental sciences, medical research, and health services research.

Conclusions: This study produced a high-quality, open-access database about co-creation. The study demonstrates that it is possible to perform a systematic review selection process on a fragmented concept using human-AI collaboration. Our unified concept of co-creation includes the co-approaches (co-creation, co-design, and co-production), forms of participatory research, and user involvement. Our analysis of authorship, citations, and source landscape highlights the potential lack of collaboration among co-creation researchers and underscores the need for future investigation into the different research methodologies. The database provides a resource for relevant literature and can support rapid literature reviews about co-creation. It also offers clarity about the current co-creation landscape and helps to address barriers that researchers may face when seeking evidence about co-creation.

Keywords: artificial intelligence; co-creation; co-design; co-production; database; methodology; participatory.

Figure 1

Screening with a unified concept of co-creation.

Figure 2

Decision support tree for double screening of papers.

Figure 3

An adapted PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart of the full methodology. The start indicates the citations that were not shown by ASReview to the researchers.

Figure 4

Keyword co-occurrence across the title and abstracts of the papers from January 2010 to November 2022. Each link between 2 keywords represents a co-occurrence. The size of the keyword bubble represents its importance in the number of co-occurrences. Five colors represent 5 clusters. cbpr: community-based participatory research.

Figure 5

Keyword co-occurrence across the title and abstracts of the papers in the database from January 2010 to November 2022. Each link between 2 keywords represents a co-occurrence. The size of the keyword bubble represents its importance in the number of co-occurrences. The color of the link represents the period where it occurs, and the color of the bubble represents the average of the co-occurrence’s periods. (date range on the displayed scale is automatically adapted by the software based on relevancy). cbpr: community-based participatory research.

Figure 6

Mapping and clustering of coauthorship of the papers in the database from January 2010 to November 2022. The size of the author’s bubble represents its importance in the number of coauthorships. The color represents the average period where the coauthorships occur.

Figure 7

Mapping and clustering of author citations from January 2010 to November 2022. The author’s bubble’s size represents its importance in the number of citations. Each link between 2 authors represents a citation. The color of the link represents the period where the citation occur, and the bubble 1 represents the average periods of the author’s citations.

Figure 8

Source landscape mapping from January 1970 to November 2022. Each link between 2 journals represents a citation between 2 journals’ documents. The journal bubble size represents its importance in the number of citations. On 8 clusters in the analysis, 5 clusters are visible through 5 colors.