TriGenDT: A hybrid TRIZ, design thinking, and generative artificial intelligence framework for systematic innovation in healthcare management information systems

Abstract

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.

Keywords

TriGenDT / Theory of Inventive Problem Solving / Design Thinking / Generative artificial intelligence / Large language models / Management information systems / Design Science Research / Healthcare innovation

References

[1] Aguilar—Calderón, J. A., Tripp—Barba, C., Aguilar—Calderón, P. A., Aguilar—Calderón, P. A., & Zaldívar—Colado, A. (2025). Design thinking in practice: Innovation in the conceptualization of a mobile app. In Innovative design thinking approaches in software engineering (pp. 205-236). IGI Global Scientific Publishing. https://doi.org/10.4018/979—8—3693—9531—8.ch008

[2] Ahmed, B., Dannhauser, T., & Philip, N. (2018). A lean design thinking methodology (LDTM) for machine learning and modern data projects. In 2018 10th Computer Science and Electronic Engineering Conference (CEEC) (pp. 11-14). IEEE. https://doi.org/10.1109/CEEC.2018.8674234

[3] Ala—Kitula, A., Talvitie—Lamberg, K., Tyrväinen, P., & Silvennoinen, M. (2017). Developing solutions for healthcare—Deploying artificial intelligence to an evolving target. In 2017 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1637-1642). IEEE. https://doi.org/10.1109/CSCI.2017.285

[4] Al’tshuller, G. (1996). And suddenly the inventor appeared: TRIZ, the theory of inventive problem solving . Technical Innovation Center, Inc.

[5] Altun, K. (2025a). Contradiction—oriented exploration: A dual—track methodology combining OTSM—TRIZ and the Six—Box Scheme. International Journal of Systematic Innovation, 9(6), 1-16. https://doi.org/10.6977/IJoSI.202512_9(6).0001

[6] Altun, K. (2025b). A Mayan calendar—inspired cyclical TRIZ approach: Enhancing systematic innovation and long—term problem—solving. International Journal of Systematic Innovation, 9(3). https://doi.org/10.6977/IJoSI.202506_9(3).0002

[7] Amazon Web Services. (2022). Amazon EC2 instance network bandwidth . Amazon Web Services . Accessed March 16, 2026. https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2—instance—network—bandwidth.html

[8] Babu, C. S., Sekhar, M. R., Sachin, A., & Brindha, B. (2025). Leveraging artificial intelligence techniques in design thinking tools for software engineering. In Innovative design thinking approaches in software engineering (pp. 27-52). IGI Global Scientific Publishing. https://doi.org/10.4018/979—8—3693—9531—8.ch002

[9] Brown, T. (2008). Design thinking. Harvard Business Review, 86(6), 84—92 . Accessed March 16, 2026. https://hbr.org/2008/06/design—thinking

[10] Chen, L., Song, Y., Ding, S., Sun, L., Childs, P., & Zuo, H. (2024). TRIZ—GPT: An LLM—augmented method for problem—solving. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 6, p. V006T06A010). American Society of Mechanical Engineers. https://doi.org/10.1115/DETC2024—143163

[11] Chongwatpol, J . (2024). A technological, data—driven design journey for artificial intelligence (AI) initiatives. Education and Information Technologies, 29(12), 15933-15963. https://doi.org/10.1007/s10639—024—12459—8

[12] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008

[13] Dennehy, D., Schmarzo, B., & Sidaoui, M. (2022). Organising for AI—powered innovation through design: The case of Hitachi Vantara. International Journal of Technology Management, 88(2—4), 312-334. https://doi.org/10.1504/IJTM.2022.121507

[14] Fischer, F., & Gardoni, M. (2024). Comparing different ChatGPT4.0 generated oriented word suggestions as design fixation interferences for the ideation phase in design thinking. In IFIP International Conference on Product Lifecycle Management (pp. 207-219). Springer Nature Switzerland. https://doi.org/10.1007/978—3—031—93323—3_16

[15] Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75-106. https://doi.org/10.2307/25148625

[16] Indriasari, E., Prabowo, H., Gaol, F. L., & Purwandari, B. (2021). The adoption of design thinking, agile software development and co—creation concepts: A case study of digital banking innovation. In 2021 International Conference on Platform Technology and Service (PlatCon) (pp. 1-6). IEEE. https://doi.org/10.1109/PlatCon53246.2021.9680763

[17] Ishfaq, F., Marwat, S. N. K., Khan, W. U., Shahzad, S., Khan, S., & Abbasi, Q. H. (2025). A machine learning based empathy mapping framework for enhancing user experience through app review analysis. Scientific Reports, 16(1), Article 30729. https://doi.org/10.1038/s41598—025—30729—4

[18] Livotov, P., Huber, N., Hentschel, C., Mayer, O., Träger, J., Bohn, G., Höcht, L., Kläb, M., Kuhlmann, L., & Scherb, B. (2026). Comparative study on AI—augmented inventive problem solving with TRIZ, TRIZ—AI hybrid, and autonomous AI: An inline coating measurement case in lithium—ion cell production. In International TRIZ and Artificial Intelligence Conference (pp. 35-54). Springer Nature Switzerland. https://doi.org/10.1007/978—3—032—08847—5_3

[19] Macedo, I., Meireles, M. A. C., Barreto, H., Steinmacher, I., Maldonado, J. C., Gadelha, B., & Conte, T. (2025). AI in the design thinking toolbox: What to recommend, when, and why. In Simpósio Brasileiro de Engenharia de Software (SBES) (pp. 755-761). SBC/ACM. https://doi.org/10.5753/sbes.2025.11538

[20] Mysior, M., Iniotakis, C., & Zyga, Ł. (2025). Large language model—based functional modeling of technical systems. In International TRIZ and Artificial Intelligence Conference (pp. 227-239). Springer Nature Switzerland. https://doi.org/10.1007/978—3—032—08847—5_16

[21] Nada, M., Ayaou, I., Chibane, H., Liverneaux, P., & Cavallucci, D. (2026). A methodological framework integrating TRIZ and AI to address complex design contradictions: Application to an innovative prosthetic thumb. In International TRIZ and Artificial Intelligence Conference (pp. 294-307). Springer Nature Switzerland. https://doi.org/10.1007/978—3—032—08851—2_19

[22] Obojski, J. (2026). Breaking industry barriers: Using AI to contextualize TRIZ principles across diverse sectors. In International TRIZ and Artificial Intelligence Conference (pp. 323-334). Springer Nature Switzerland. https://doi.org/10.1007/978—3—032—08851—2_21

[23] OpenSSL Project . (2023). openssl—speed command documentation . Accessed March 16, 2026. https://www.openssl.org/docs/man3.0/man1/openssl—speed.html

[24] Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S . (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45-77. https://doi.org/10.2753/MIS0742—1222240302

[25] Qiao, J., Liu, Y., An, Z., & Lee, C.—H. (2026). Requirement and service quality—driven service design for cross—border e—commerce export service optimization incorporated with TRIZ and generative AI. In International TRIZ and Artificial Intelligence Conference (pp. 335-352). Springer Nature Switzerland. https://doi.org/10.1007/978—3—032—08851—2_22

[26] Redis Labs . (2023). Redis benchmark results . Accessed March 16, 2026. https://redis.io/docs/management/optimization/benchmarks/

[27] Savransky, S. D. (2000). Engineering of creativity: Introduction to TRIZ methodology of inventive problem solving . CRC Press. https://doi.org/10.1201/9781420038958

[28] Shin, W.—S., Choi, Y., & Hyun, J. S. (2024). Examining the structural attributes of TRIZ contradiction matrix using exploratory data analysis. International Journal of Systematic Innovation, 8(4), 1-17. https://doi.org/10.6977/IJoSI.202412_8(4).0001

[29] Shneiderman, B . (2020). Human—centered artificial intelligence: Three fresh ideas. AIS Transactions on Human—Computer Interaction, 12(3), 109-124. https://doi.org/10.17705/1thci.00131

[30] Sun, W., Zhao, Y., Xi, C., & Lyu, L. (2026). Technical contradiction extraction based on two—stage knowledge fusion mechanism. In International TRIZ and Artificial Intelligence Conference (pp. 308-322). Springer Nature Switzerland. https://doi.org/10.1007/978—3—032—08851—2_20

[31] Venable, J., Pries—Heje, J., & Baskerville, R. (2016). FEDS: A framework for evaluation in design science research. European Journal of Information Systems, 25(1), 77-89. https://doi.org/10.1057/ejis.2014.36

[32] Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage Publications, Thousand Oaks, CA.