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Paper Number
1767
Paper Type
Complete
Abstract
The integration of AI is reshaping decision-making, especially in healthcare, where it supports diagnostic procedures. However, a significant knowledge gap exists regarding the impact of physician-AI collaboration on patient care. Leveraging proprietary data from a hospital that implemented an AI-based risk evaluation system in 2022, we examine the effects of physician-AI collaboration on patient care outcomes, considering patient risk levels and physician experience. Results indicate that physician-AI collaboration reduces hospital stays and readmissions, particularly benefiting high-risk patients. Notably, high levels of physician experience exhibit complex moderating effects. Our findings underscore the role of AI as an assistant in healthcare, revealing variations based on patient risk and physician diverse experience. They are critical for optimizing AI integration, minimizing decision biases, and emphasizing how AI strengthens medical diagnosis with nuanced implementation.
Recommended Citation
Qin, Yaxue and Kwon, Juhee, "The Impact of Physician-AI Collaboration on Care Quality: Empirical Evidence in Acute Diseases" (2024). ICIS 2024 Proceedings. 11.
https://aisel.aisnet.org/icis2024/ishealthcare/ishealthcare/11
The Impact of Physician-AI Collaboration on Care Quality: Empirical Evidence in Acute Diseases
The integration of AI is reshaping decision-making, especially in healthcare, where it supports diagnostic procedures. However, a significant knowledge gap exists regarding the impact of physician-AI collaboration on patient care. Leveraging proprietary data from a hospital that implemented an AI-based risk evaluation system in 2022, we examine the effects of physician-AI collaboration on patient care outcomes, considering patient risk levels and physician experience. Results indicate that physician-AI collaboration reduces hospital stays and readmissions, particularly benefiting high-risk patients. Notably, high levels of physician experience exhibit complex moderating effects. Our findings underscore the role of AI as an assistant in healthcare, revealing variations based on patient risk and physician diverse experience. They are critical for optimizing AI integration, minimizing decision biases, and emphasizing how AI strengthens medical diagnosis with nuanced implementation.
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16-HealthCare