Collaboration and data quality management in the development of Artificial Intelligence based Cinical Decision Support Systems (AI-CDSS)

This research aims to delve deeper into stakeholder relationships, roles, expectations and interdependencies when collaborating in data quality management when developing AI-based Clinical Decision Support Systems.

This will involve getting an understanding of the status, processes in place by various stakeholders, identifying bottlenecks, recommendations on what needs to happen for the various stakeholders to ensure data quality.

Research questions

The overall question this study aims to answer is how stakeholders manage data quality during the development of AI-based Clinical Decision Support Systems to promote innovation and ensure regulatory compliance.

This will be broken down into these sub questions.

How are the data quality, collaboration, and regulatory compliance perceived in the development of AI-based Clinical Decision Support Systems
What are the stakeholders’ perceived roles, responsibilities, and contributions in the development of AI-CDSS tools?
How are stakeholders collaborating or plan to collaborate in ensuring data quality during the development of AI-CDSS tools?

Importance of the study

The study was necessitated by the increased requirements and regulations for high quality healthcare data as well as the increased complexity and interdependence of stakeholders in the ecosystem and teams in the development of AI-based Clinical Decision Support Systems and will contribute to the empirical literature on collaboration in healthcare projects as well as to guide new developers in the ecosystem and to inform policy.