{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:05:34Z","timestamp":1780635934275,"version":"3.54.1"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032285690","type":"print"},{"value":"9783032285706","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-28570-6_14","type":"book-chapter","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:57:53Z","timestamp":1780635473000},"page":"169-180","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Theory-Driven LLM Agent Design for Generating Synthetic Data in Tele-Triage"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2623-7569","authenticated-orcid":false,"given":"Hetiao Slim","family":"Xie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1988-2192","authenticated-orcid":false,"given":"Morteza","family":"Namvar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5305-8677","authenticated-orcid":false,"given":"Saeed","family":"Akhlaghpour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2260-4142","authenticated-orcid":false,"given":"Andrew","family":"Staib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,6]]},"reference":[{"issue":"6","key":"14_CR1","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1080\/0960085X.2023.2252390","volume":"33","author":"L Nguyen","year":"2024","unstructured":"Nguyen, L., Ngwenyama, O., Bandyopadhyay, A., Nallaperuma, K.: Realising the potential of digital health communities: a study of the role of social factors in community engagement. Eur. J. Inf. Syst. 33(6), 1033\u20131068 (2024)","journal-title":"Eur. J. Inf. Syst."},{"issue":"9","key":"14_CR2","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1177\/1357633X221150278","volume":"30","author":"M Farzandipour","year":"2024","unstructured":"Farzandipour, M., Nabovati, E., Sharif, R.: The effectiveness of tele-triage during the COVID-19 pandemic: a systematic review and narrative synthesis. J. Telemed. Telecare 30(9), 1367\u20131375 (2024)","journal-title":"J. Telemed. Telecare"},{"issue":"1","key":"14_CR3","doi-asserted-by":"publisher","DOI":"10.2196\/40983","volume":"25","author":"C Ziebart","year":"2023","unstructured":"Ziebart, C., Kfrerer, M.L., Stanley, M., Austin, L.C.: A digital-first health care approach to managing pandemics: scoping review of pandemic self-triage tools. J. Med. Internet Res. 25(1), e40983 (2023)","journal-title":"J. Med. Internet Res."},{"issue":"3","key":"14_CR4","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1287\/isre.2020.0926","volume":"31","author":"S Sun","year":"2020","unstructured":"Sun, S., Lu, S.F., Rui, H.: Does telemedicine reduce emergency room congestion? Evidence from New York State. Inf. Syst. Res. 31(3), 972\u2013986 (2020)","journal-title":"Inf. Syst. Res."},{"issue":"1","key":"14_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-024-01367-3","volume":"7","author":"BA Naved","year":"2024","unstructured":"Naved, B.A., Luo, Y.: Contrasting rule and machine learning based digital self triage systems in the USA. Npj Digital Med. 7(1), 1\u201310 (2024)","journal-title":"Npj Digital Med."},{"issue":"8","key":"14_CR6","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","volume":"29","author":"AJ Thirunavukarasu","year":"2023","unstructured":"Thirunavukarasu, A.J., Ting, D.S.J., Elangovan, K., Gutierrez, L., Tan, T.F., Ting, D.S.W.: Large language models in medicine. Nat. Med. 29(8), 1930\u20131940 (2023)","journal-title":"Nat. Med."},{"issue":"10s","key":"14_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3502287","volume":"54","author":"MA Bansal","year":"2022","unstructured":"Bansal, M.A., Sharma, D.R., Kathuria, D.M.: A systematic review on data scarcity problem in deep learning: solution and applications. ACM Comput. Surv. 54(10s), 1\u201329 (2022)","journal-title":"ACM Comput. Surv."},{"issue":"1","key":"14_CR8","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1186\/s40537-023-00727-2","volume":"10","author":"L Alzubaidi","year":"2023","unstructured":"Alzubaidi, L., et al.: A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications. J. Big Data 10(1), 46 (2023)","journal-title":"J. Big Data"},{"issue":"1","key":"14_CR9","doi-asserted-by":"publisher","first-page":"2989","DOI":"10.1080\/07853890.2022.2136402","volume":"54","author":"R Inokuchi","year":"2022","unstructured":"Inokuchi, R., Iwagami, M., Sun, Y., Sakamoto, A., Tamiya, N.: Machine learning models predicting undertriage in telephone triage. Ann. Med. 54(1), 2989\u20132996 (2022)","journal-title":"Ann. Med."},{"issue":"7","key":"14_CR10","doi-asserted-by":"publisher","first-page":"4957","DOI":"10.1109\/TPAMI.2024.3362821","volume":"46","author":"I Joshi","year":"2024","unstructured":"Joshi, I., Grimmer, M., Rathgeb, C., Busch, C., Bremond, F., Dantcheva, A.: Synthetic data in human analysis: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 46(7), 4957\u20134976 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2025.114452","volume":"193","author":"L Wei","year":"2025","unstructured":"Wei, L., Chen, S., Lin, J., Shi, L.: Enhancing return forecasting using LSTM with agent-based synthetic data. Decis. Support Syst. 193, 114452 (2025)","journal-title":"Decis. Support Syst."},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Park, J.S., O'Brien, J., Cai, C.J., Morris, M. R., Liang, P., Bernstein, M.S.: Generative agents: Interactive simulacra of human behavior. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, (pp. 1\u201322) (2023)","DOI":"10.1145\/3586183.3606763"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Zhu, L., Huang, X., Sang, J.: How reliable is your simulator? analysis on the limitations of current llm-based user simulators for conversational recommendation. Companion Proceedings of the ACM Web Conference 2024 (pp. 1726\u20131732) (2024)","DOI":"10.1145\/3589335.3651955"},{"issue":"3","key":"14_CR14","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1287\/isre.2019.0911","volume":"31","author":"X Liu","year":"2020","unstructured":"Liu, X., Wang, G.A., Fan, W., Zhang, Z.: Finding useful solutions in online knowledge communities: a theory-driven design and multilevel analysis. Inf. Syst. Res. 31(3), 731\u2013752 (2020)","journal-title":"Inf. Syst. Res."},{"issue":"1","key":"14_CR15","doi-asserted-by":"publisher","first-page":"63","DOI":"10.25300\/MISQ\/2022\/17062","volume":"47","author":"Y Yang","year":"2023","unstructured":"Yang, Y., Qin, Y., Fan, Y., Zhang, Z.: Unlocking the power of voice for financial risk prediction: a theory-driven deep learning design approach. MIS Q. 47(1), 63\u201396 (2023)","journal-title":"MIS Q."},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Fang, X., Hu, P. J., Chau, M., Chen, H.: Computational Design Science: A Critical Information Systems Research Area Contributing to Artificial Intelligence and Data Science (2025). Available at SSRN 5455094","DOI":"10.2139\/ssrn.5455094"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Rai, A., et al.: Editor\u2019s comments: Diversity of design science research. MIS Q. 41(1), iii-xviii (2017)","DOI":"10.25300\/MISQ\/2017\/41.1.E0"},{"issue":"1","key":"14_CR18","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1186\/s12911-023-02307-z","volume":"23","author":"JN Hall","year":"2023","unstructured":"Hall, J.N., Galaev, R., Gavrilov, M., Mondoux, S.: Development of a machine learning-based acuity score prediction model for virtual care settings. BMC Med. Inform. Decis. Mak. 23(1), 200 (2023)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"14_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejon.2021.102036","volume":"56","author":"J Hawkins","year":"2022","unstructured":"Hawkins, J., Jones, P., McShane, B., Morris, H., Ollett, L., Sanderson, L.: Telephone triage toolkit for children\u2019s cancer services: a quality initiative. Eur. J. Oncol. Nurs. 56, 102036 (2022)","journal-title":"Eur. J. Oncol. Nurs."},{"issue":"1","key":"14_CR20","doi-asserted-by":"publisher","DOI":"10.2196\/43803","volume":"25","author":"E Riboli-Sasco","year":"2023","unstructured":"Riboli-Sasco, E., et al.: Triage and diagnostic accuracy of online symptom checkers: systematic review. J. Med. Internet Res. 25(1), e43803 (2023)","journal-title":"J. Med. Internet Res."},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Horton, J.J.: Large language models as simulated economic agents: what can we learn from homo silicus?. Natl. Bureau Econ. Res. (2023)","DOI":"10.3386\/w31122"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Yu, H., et al.: Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education. Commun. Med. 6(27) (2025)","DOI":"10.1038\/s43856-025-01283-x"},{"key":"14_CR23","unstructured":"Yao, S., et al.: React: Synergizing reasoning and acting in language models. The 18th International Conference on Learning Representations (2022)"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Guo, N., Cong, W., Su, Y., Zhang, S., Man, Q.: The influence of social interaction and emotional states on residents\u2019 decision-making on energy-efficiency retrofit: an improved multi-agent simulation approach. Eng. Constr. Architectural Manage. 1\u201319 (2025)","DOI":"10.1108\/ECAM-04-2025-0640"},{"key":"14_CR25","doi-asserted-by":"publisher","first-page":"1669896","DOI":"10.3389\/frai.2025.1669896","volume":"8","author":"A Zolnour","year":"2025","unstructured":"Zolnour, A., et al.: LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data. Front. Artif. Intell. 8, 1669896 (2025)","journal-title":"Front. Artif. Intell."},{"key":"14_CR26","unstructured":"Wagner, S.S., Behrendt, M., Ziegele, M., Harmeling, S.: The power of llm-generated synthetic data for stance detection in online political discussions. The 13th International Conference on Learning Representations (ICLR) (2025)"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Mohammadi, M., Li, Y., Lo, J., Yip, W.: Evaluation and benchmarking of llm agents: a survey. Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2 (pp. 6129\u20136139) (2025)","DOI":"10.1145\/3711896.3736570"},{"issue":"4","key":"14_CR28","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1177\/09637214221096485","volume":"31","author":"LF Bringmann","year":"2022","unstructured":"Bringmann, L.F., Elmer, T., Eronen, M.I.: Back to basics: the importance of conceptual clarification in psychological science. Curr. Dir. Psychol. Sci. 31(4), 340\u2013346 (2022)","journal-title":"Curr. Dir. Psychol. Sci."},{"issue":"2","key":"14_CR29","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/BF01173486","volume":"16","author":"H Leventhal","year":"1992","unstructured":"Leventhal, H., Diefenbach, M., Leventhal, E.A.: Illness cognition: using common sense to understand treatment adherence and affect cognition interactions. Cogn. Ther. Res. 16(2), 143\u2013163 (1992)","journal-title":"Cogn. Ther. Res."},{"issue":"3","key":"14_CR30","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1080\/17437199.2021.1878050","volume":"16","author":"MS Hagger","year":"2022","unstructured":"Hagger, M.S., Orbell, S.: The common sense model of illness selfregulation: a conceptual review and proposed extended model. Health Psychol. Rev. 16(3), 347\u2013377 (2022)","journal-title":"Health Psychol. Rev."},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Strong, D., et al.: A theory of organization-ehr affordance actualization. J. Assoc. Inf. Syst. 15(2) (2014)","DOI":"10.17705\/1jais.00353"},{"issue":"3","key":"14_CR32","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1080\/0960085X.2024.2363322","volume":"34","author":"Z Shao","year":"2025","unstructured":"Shao, Z., Zhang, J., Zhang, L., Benitez, J.: Uncovering post-adoption usage of AI-based voice assistants: a technology affordance lens using a mixed-methods approach. Eur. J. Inf. Syst. 34(3), 475\u2013501 (2025)","journal-title":"Eur. J. Inf. Syst."},{"issue":"3","key":"14_CR33","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.jik.2020.09.002","volume":"6","author":"D-H Huang","year":"2021","unstructured":"Huang, D.-H., Chueh, H.-E.: Chatbot usage intention analysis: veterinary consultation. J. Innov. Knowl. 6(3), 135\u2013144 (2021)","journal-title":"J. Innov. Knowl."},{"issue":"5","key":"14_CR34","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.annemergmed.2021.06.012","volume":"78","author":"DP Ly","year":"2021","unstructured":"Ly, D.P.: The influence of the availability heuristic on physicians in the emergency department. Ann. Emerg. Med. 78(5), 650\u2013657 (2021)","journal-title":"Ann. Emerg. Med."},{"issue":"4","key":"14_CR35","first-page":"1","volume":"41","author":"L Li","year":"2023","unstructured":"Li, L., Zhang, Y., Chen, L.: Personalized prompt learning for explainable recommendation. ACM Trans. Inf. Syst. 41(4), 1\u201326 (2023)","journal-title":"ACM Trans. Inf. Syst."}],"container-title":["Lecture Notes in Computer Science","Design for Better Futures: Beyond the Science of the Artificial. Prototypes and Research-in-Progress"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-28570-6_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:58:05Z","timestamp":1780635485000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-28570-6_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032285690","9783032285706"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-28570-6_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"6 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"DESRIST","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Design Science Research in Information Systems and Technology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"M\u00fcnster","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 June 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"desrist2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/desrist2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}