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Nevertheless, the pathways to improving such effectiveness remain insufficiently understood.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to conduct an in-depth exploration of the multiple factors influencing the effectiveness of human-AI collaborative consultation in OHCs and to assess the causal relationships among these factors, thereby providing a theoretical foundation and practical guidance for advancing the clinical application of human-AI collaboration.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Grounded in the information ecology theory, we constructed an analytical framework encompassing four dimensions: information human, information, environment, and technology. We collected 296 valid questionnaire responses and used fuzzy set qualitative comparative analysis to systematically investigate the configurational mechanisms through which these four types of factors jointly influence consultation effectiveness.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The findings are as follows: (1) no single factor constitutes a sufficient condition for high consultation effectiveness (consistency <0.9); rather, such effectiveness emerges from the synergistic interplay of multiple configurations involving technology, information, human information, and environment; (2) five distinct configurational pathways lead to high consultation effectiveness, demonstrating clear equifinality; (3) system responsiveness, information usefulness, perceived service empathy, perceived service accuracy, and perceived service effectiveness are all core or important conditions across these pathways; and (4) substitutability exists among antecedent conditions\u2014specifically, perceived uncertainty and social norms, as well as operational convenience and platform ethical norms, can substitute for one another in different configurations to enhance patient satisfaction jointly.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This study reveals multiple pathways to achieving high effectiveness in human-AI collaborative consultation within OHCs. It not only offers a novel theoretical perspective for understanding the complex mechanisms of human-AI collaboration in medical contexts, but also provides significant practical implications for the design optimization of digital health platforms and related policy formulation.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/90611","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T19:15:27Z","timestamp":1784142927000},"page":"e90611-e90611","source":"Crossref","is-referenced-by-count":0,"title":["Research on the Improvement Path of Human-AI Collaborative Consultation Effectiveness From the Perspective of Information Ecology: Configurational Analysis"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9214-4906","authenticated-orcid":false,"given":"Dandan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9833-4976","authenticated-orcid":false,"given":"Mengyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8198-9436","authenticated-orcid":false,"given":"Bingjun","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"key":"R1","unstructured":"The CPC Central Committee and the State Council issued the \u201cOutline of the \u2018Healthy China 2030\u2019 plan\u201d [Article in Chinese]. 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