Preparing hospitals and health organizations for AI: practical guidelines for the required infrastructure

摘要

IntroductionAI is rapidly transforming the medical field: From predictive algorithms to enhanced robotic surgeries, there is relatively wide potential for AI to improve clinical outcomes (1) (2) (3). Yet, evidence suggests that AI's capabilities cannot be fully realized without first ensuring a solid foundation, be it technological, educational, clinical, or ethical. Previous studies have reported that AI implementation without adequate infrastructure leads to inefficiencies and gaps in its development, execution, and monitoring (4) (5), and according to the literature, hospitals tend not to possess the mentioned infrastructure (6) (7).Healthcare organizations tend to rush to adopt AI technologies (8) (9), and this pattern mirrors historical health IT adoption challenges where technology outpaced organizational readiness. This can lead to challenges like poor AI performance, data silos, regulatory compliance issues, and privacy risks (10), as well as compromising patient safety, eroding trust in AI systems, delaying the r AI's benefits, and resulting in wasted resources (11) (12) (13).Hence, healthcare organizations are recommended to prioritize the development of a robust infrastructure before integrating AI. With a list of requirements and how to achieve them, healthcare organizations can create an environment where AI reaches its potential.This Opinion paper answers the following research question: According to the latest findings, what guidelines should healthcare organizations follow to increase their chance of optimal AI deployment? The paper aims to 1) discover cross-departmental foundational requirements that influence AI adoption and 2) elaborate strategies for achieving such requirements.MethodologyTo meet its goals, this paper has a two-step methodology to first define the requirements and secondly to discuss the strategies to achieve them:Being a group synergetic, cross-departmental frameworks more effective than single generic one (14) (15), this paper uses the Design Science Research (DSR) practical approach to problem solving (16) (17) to find and choose said frameworks with a focus on highlighted digital transformation themes (18) (19) (20):Organizational alignment and integration.Accountability and decision-making.Accessibility and usability of data.Collaborative learning and knowledge transferImproved clinical practiceTo discuss what strategies achieve these goals, this paper employs the Consolidated Framework for Implementation Research (CFIR), a proven methodology to evaluate the various factors that influence the implementation of health interventions (21) (22). Its five domains are:Intervention Characteristics.Outer Setting.Inner Setting.Characteristics of Individuals.Implementation Process.Key infrastructure frameworks for healthcare organizationsTo analyze the five digital transformation themes mentioned above, the respective chosen frameworks according to DSR are:Enterprise Architecture (EA)IT GovernanceFAIR Principles and StandardizationKnowledge Management & Knowledge Sharing (KM-KS)Clinical Decision Support System (CDSS)Enterprise Architecture (EA)EA aligns an organization's processes, information systems, and infrastructure with its business goals. In healthcare, EA ensures interoperability, data standardization, and seamless integration of emerging technologies. Enterprise Transformation Projects (ETPs) are large-scale initiatives that leverage EA to modernize healthcare systems, optimizing workflows, enhancing data governance, and fostering innovation (23). Successful implementations often use phased approaches to minimize disruption to clinical operations. By implementing robust EA through strategic ETPs, healthcare organizations can systematically transition toward AI-ready infrastructures, improving efficiency and patient outcomes as well as understanding the impact of their digital interventions, as demonstrated by real-world applications (24).Enhancements in data governance frameworks within