Paper Type
Complete
Abstract
Healthcare organizations increasingly seek to empower clinicians with data, AI and analytical tools. Yet, existing approaches often struggle to align with clinical reasoning and cognitive constraints, limiting their adoption and effectiveness. Self-Service Business Intelligence (SSBI) offers a promising but underexplored solution in this context. This study applies a Design Science Research approach to derive design principles for SSBI systems in cardiology, a domain characterized by complex decision-making. We conducted structured focus groups with 60 participants, including cardiologists and BI professionals, to identify meta-requirements for clinical environments. These insights were then developed into eight design principles grounded in Cognitive Load Theory and Affordances Theory, addressing workflow alignment, transparency, collaboration, and traceability. The principles were evaluated using a light usability framework through expert interviews, demonstrating strong relevance and effectiveness. This research contributes prescriptive design knowledge for developing clinical SSBI systems that support domain expertise while reducing cognitive burden.
Paper Number
1177
Recommended Citation
Giunta, Benito; Bono Rossello, Nicolas; and Faulkner, Alexandre, "Toward Effective Self-Service Business Intelligence in Healthcare: Design Principles from a Cardiology Context" (2026). AMCIS 2026 Proceedings. 2.
https://aisel.aisnet.org/amcis2026/sig_health/sig_health/2
Toward Effective Self-Service Business Intelligence in Healthcare: Design Principles from a Cardiology Context
Healthcare organizations increasingly seek to empower clinicians with data, AI and analytical tools. Yet, existing approaches often struggle to align with clinical reasoning and cognitive constraints, limiting their adoption and effectiveness. Self-Service Business Intelligence (SSBI) offers a promising but underexplored solution in this context. This study applies a Design Science Research approach to derive design principles for SSBI systems in cardiology, a domain characterized by complex decision-making. We conducted structured focus groups with 60 participants, including cardiologists and BI professionals, to identify meta-requirements for clinical environments. These insights were then developed into eight design principles grounded in Cognitive Load Theory and Affordances Theory, addressing workflow alignment, transparency, collaboration, and traceability. The principles were evaluated using a light usability framework through expert interviews, demonstrating strong relevance and effectiveness. This research contributes prescriptive design knowledge for developing clinical SSBI systems that support domain expertise while reducing cognitive burden.
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