Paper Number
ICIS2025-1407
Paper Type
Short
Abstract
Health information exchange (HIE) plays a critical role in the digital transformation of healthcare, particularly in advancing accessible and patient-centered health systems. This study examines how HIE technologies influence patient mobility across healthcare providers. We construct patient transfer networks using data from 76 million outpatient visits across 171 hospitals in New York State. Drawing on the relational view, our results reveal that broader types of information sharing between hospitals significantly increase patient flows between them. Employing explainable machine learning techniques, we further identify that sharing unstructured information, such as clinical records and radiology reports, plays a more critical role in facilitating patient flows. Additionally, PageRank analysis shows that high-value information exchange enhances hospitals’ ultimate patient inflow within patient networks, providing competitive advantages. Our findings contribute to the relational view by empirically comparing the importance of different types of information in establishing knowledge-sharing routines and shaping patient mobility.
Recommended Citation
Zhang, Mingshan; Ke, Weiling; and Li, Yao, "Shaping Patient Flows Through Health Information Exchange: Evidence from New York State Hospitals" (2025). ICIS 2025 Proceedings. 7.
https://aisel.aisnet.org/icis2025/is_health/ishealthcare/7
Shaping Patient Flows Through Health Information Exchange: Evidence from New York State Hospitals
Health information exchange (HIE) plays a critical role in the digital transformation of healthcare, particularly in advancing accessible and patient-centered health systems. This study examines how HIE technologies influence patient mobility across healthcare providers. We construct patient transfer networks using data from 76 million outpatient visits across 171 hospitals in New York State. Drawing on the relational view, our results reveal that broader types of information sharing between hospitals significantly increase patient flows between them. Employing explainable machine learning techniques, we further identify that sharing unstructured information, such as clinical records and radiology reports, plays a more critical role in facilitating patient flows. Additionally, PageRank analysis shows that high-value information exchange enhances hospitals’ ultimate patient inflow within patient networks, providing competitive advantages. Our findings contribute to the relational view by empirically comparing the importance of different types of information in establishing knowledge-sharing routines and shaping patient mobility.
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21-Healthcare