TriGenDT: A hybrid TRIZ, design thinking, and generative artificial intelligence framework for systematic innovation in healthcare management information systems
Innovation teams in regulated digital-service environments routinely encounter hard architectural contradictions, situations in which two non-negotiable requirements are in direct technical conflict. Despite significant scholarly interest in Theory of Inventive Problem Solving (TRIZ)-based contradiction resolution and the institutionalization of Design Thinking in software engineering and management information systems, no existing framework has formally coupled these two paradigms with the generative potential of large language models into a unified, human-centered innovation process. The current study presents TriGenDT, a five-stage Design Science Research artifact that integrates TRIZ contradiction resolution, Design Thinking, and generative artificial intelligence to enable systematic innovation in healthcare management information systems. The framework is illustrated through a constructed clinical decision-support scenario that highlights the tension between real-time alert latency and privacy/compliance constraints. The demonstration illustrates process coherence, stage-to-tool traceability, and design-level feasibility; it does not constitute field validation. Projected values, such as alert latency (~30 ms) and alert-path call-depth reduction (~50%, from six to three hops), are design-level estimates that require empirical confirmation. The study contributes a formally specified TRIZ-to-software-engineering parameter mapping, an integrated innovation methodology grounded in a reproducible demonstration case, a preliminary expert appraisal, representative prompt templates with model parameters, and an openly available artifact repository. Future research should pursue empirical validation through live industry case studies and comparative evaluation against TRIZ-only, Design Thinking-only, and large language model-native TRIZ baselines.
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