JOURNAL ARTICLE
How Hospitals Differentiate Health Information Technology Portfolios for Clinical Care Efficiency: Insights from the HITECH Act.
Published In: Information Systems Research (INFORMS), 2025, v. 36, n. 1. P. 239 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Pye, Jessica; Rai, Arun; Dong, John Qi 3 of 3
Abstract
This article examines how hospitals in the U.S. healthcare industry strategically differentiate their search for health information technology (HIT) to address cost-based performance shortfalls, with a focus on the evolving policy uncertainty surrounding the Health Information Technology for Economic and Clinical Health (HITECH) Act. Using a panel dataset of 3,319 hospitals from 2007 to 2014, the study finds that hospitals facing higher operating costs relative to peers tend to implement newer HIT technologies earlier than others, a behavior termed "search differentiation." Importantly, this relationship is moderated by the level of policy uncertainty across three phases of the HITECH Act: during moderate uncertainty (enactment phase), hospitals show greater search differentiation, while under high (conceptualization phase) or low uncertainty (enforcement phase), hospitals' HIT choices converge due to mimetic behaviors or coercive and normative pressures. The findings integrate behavioral theory of the firm and institutional theory to highlight how dynamic regulatory environments influence hospitals' technological innovation strategies, offering implications for hospital management and healthcare policymakers aiming to balance innovation exploration with widespread technology adoption.
Additional Information
- Source:Information Systems Research (INFORMS). 2025/03, Vol. 36, Issue 1, p239
- Document Type:Article
- Subject Area:Information Technology
- Publication Date:2025
- ISSN:1047-7047
- DOI:10.1287/isre.2021.0260
- Accession Number:184136934
- Copyright Statement:Copyright of Information Systems Research (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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