【公开学术报告-第146期】Developing Generative AI for Value Co-Creation: An Intervention-Based Randomized Field Experiment in a Healthcare Context

Developing Generative AI for Value Co-Creation: An Intervention-Based Randomized Field Experiment in a Healthcare Context

【面向价值共创的生成式人工智能开发：一项医疗场景下的干预性随机实地实验】

Speaker: Yanwu SONG (Ph.D. Candidate in USTC)

Date & Time: Wed.5, November 2025, from 10:00 to 11:30 (Beijing Time)

Place: Tongji Building A2101 

ABSTRACT

Owing to limited healthcare resources, there has been increased demand for artificial intelligence (AI) interventions to treat mental health problems of chronic disease patients in developing countries. However, it is challenging to overcome the AI trust crisis in the healthcare context and develop AI that improves the patient's personalized experience and the quality of care. Elaborating on the value co-creation theory using an actor–network theory (ANT) approach, this study examines how generative artificial intelligence (GenAI) can improve post-discharge care for patients with cardiovascular disease in resource-limited settings. Using an intervention-based research approach in collaboration with a major hospital in China, researchers co-designed a GenAI intervention with potential users and various stakeholders. Through a randomized controlled trial, we further evaluated the impact of a co-created GenAI intervention on the post-discharge self-confidence and quality of care of 114 patients. Compared with the standard post-discharge care process, chronically ill patients who received the GenAI intervention experienced a 6.049-point (out of a total of 80 points) decrease in state anxiety and an 87.8% decrease in the 30-day readmission risk. The insights gained from the intervention process, as interpreted using the ANT approach, expand the generic framework of value co-creation to include a more GenAI-mediated network of human and non-human objects. Results reveal GenAI's boundary-spanning and integrating roles as a critical node in the emerging, dynamic, value-creating actor network. The inclusion of “a nonparticipant observe” allows us to offer cognitive explanations for why GenAI works. Overall, this study contributes to healthcare operations management by designing a process for developing and implementing GenAI to improve healthcare operations.