Alliance Network in AI Healthcare Startups for Data Acquisition

Abstract

This paper examines the critical role of alliance networks in data acquisition for AI healthcare startups, particularly within highly regulated healthcare sectors. Our study investigates how these startups overcome challenges related to data accessibility and regulatory constraints by forming strategic alliances with hospitals, universities, and industry partners. Based on a qualitative analysis of 47 U.S.-based AI healthcare startups, this study highlights how these ventures secure essential data resources, addressing privacy and interoperability issues in clinical settings. Our findings underscore that these alliances facilitate access to proprietary data and foster credibility and innovation by leveraging institutional expertise. I identify three key mechanisms driving data acquisition: collaboration with large healthcare organizations for clinical trial data, partnerships with smaller clinics for flexibility in data access, and alliances with research institutions to validate AI models. This study draws on RBV to explain the necessity of developing alliances that provide access to external resources (data). RBV herein shows that AI healthcare startups need to build alliances with external partners to gain a significant competitive advantage, demonstrating that early-stage AI healthcare startups rely extensively on external data sources to refine their AI algorithms. I propose a conceptual framework that views alliance-building as a pathway for overcoming structural data limitations, with implications for policy designed to support data-sharing and collaboration in regulated environments.

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