Algorithmic Isomorphism and System-Driven Convergence in Healthcare Provider Organizations

• In Event: Institutional Dynamics in Policy: Convergence, Coordination, and Governance Under Complexity

Abstract

Artificial intelligence (AI) has rapidly transitioned from experimental deployment to routine use across public organizations. This is true in environments defined by elevated uncertainty and complex decision demands. Governments in countries such as the United States, Canada, the United Kingdom, and Saudi Arabia have increasingly incorporated AI systems into public administration functions ranging from policing and social services to health regulation and service delivery. Under these conditions, public organizations face mounting pressure to delegate routine and semi-routine decisions to data-driven systems capable of producing rapid, standardized outputs. In many instances, these decisions are administrative and operational, increasingly made under urgency. AI systems are frequently positioned as tools that reduce cognitive burden. Also, AI enhances consistency and supports compliance with performance and accountability expectations. This article argues that AI-driven systems in the public sector are increasingly functioning not merely as decision-support tools. Nevertheless, they are also being used as governance infrastructure. Without knowing the side effects, AI systems are structuring how authority, accountability, and compliance are enacted within public organizations. This is often beyond the reach of traditional oversight mechanisms. Healthcare governance offers a particularly salient illustration of this dynamic. While healthcare delivery often involves a mix of public, nonprofit, and private providers, the sector is profoundly shaped by public regulatory authority, public funding, and government-imposed performance expectations. Thus, these organizations continue to operate under persistent resource constraints while striving to provide excellent services to the public. Many healthcare providers use AI systems that apply cutting-edge algorithms integrated into clinical predictive analytics and patient risk stratification. The goal is to continue supporting clinicians in assessing patient conditions and anticipating health risks. Automated clinical decision-support systems are therefore expected to continue expanding across medical facilities. Such AI systems have proven to assist in directing treatment decisions and improving the allocation of limited medical resources. In addition to individual treatment recommendations, these systems aid in identifying high-risk patient groups. This helps medical staff prioritize intervention approaches like promoting greater efficiency, consistency, and objectivity in service delivery.

Author

• Pedro Robles, Pennsylvania State University
  Presenting Author