AI in Hospitals: The Real Challenge Isn’t the Algorithm—It’s the Data

JAKARTA, Jakartaweekly.com — Artificial intelligence (AI) is widely regarded as one of the most transformative technologies in healthcare, yet large-scale adoption remains limited. While hospitals around the world are experimenting with AI in areas such as radiology analysis, clinical decision support, and operational optimization, only a relatively small number have integrated AI deeply into everyday clinical workflows.

According to Luciano Brustia, Regional Managing Director, Asia Pacific at InterSystems, the challenge is not a lack of AI technology. Instead, many healthcare organizations are still working to build the foundations required for AI to deliver meaningful outcomes.

“AI is certainly one of the most discussed topics in healthcare today, but large-scale adoption is still in its early stages. Many hospitals are experimenting with AI in areas such as radiology analysis, clinical decision support, or operational optimization, yet only a relatively small number have integrated AI deeply into their daily clinical workflows.”

Brustia noted that healthcare organizations typically face three major concerns when evaluating AI adoption: trust and reliability, data security and patient privacy, and integration with existing systems. Clinicians need confidence that AI supports rather than replaces clinical judgment, while healthcare providers must ensure regulatory compliance and protect sensitive patient information. At the same time, introducing AI into complex hospital environments requires systems that can work seamlessly with existing workflows.

The Foundation Behind Successful AI Adoption

A more fundamental challenge, however, lies in the quality and accessibility of healthcare data. Hospitals often rely on multiple systems that store information in different formats, making it difficult to generate a complete view of the patient or create reliable AI models. According to Brustia, interoperability standards such as HL7® and FHIR® play a critical role in addressing this issue by enabling healthcare data to be exchanged and understood consistently across systems. This is particularly relevant in Indonesia, where the SATUSEHAT initiative is helping establish a stronger foundation for healthcare interoperability.

“The real challenge is whether healthcare organizations have the right data infrastructure to support them. I recently heard the following comment: “There is no AI strategy without a data strategy. There is no data strategy without an interoperability strategy”.  AI systems can only deliver value when built on a foundation of clean, connected, and trustworthy data.”

Once healthcare data is standardized, connected, and accessible, AI becomes significantly more practical to deploy. InterSystems describes this approach as a Smart Data Fabric, or connected data layer, which helps healthcare organizations make diverse data usable in real time across clinical and operational environments.

Despite the challenges, hospitals that have successfully implemented AI powered Electronic Health Record are already seeing measurable benefits. According to Brustia, the first major impact is reducing clinician burnout. AI-powered capabilities such as ambient listening, automated documentation and intelligent assistants can help automate patient summary, retrieve patient information, and reduce time spent navigating multiple systems, focus on what matters most–excellent patient care.

The second area is AI-driven intelligence, enabling clinicians to interpret information more quickly, identify potential risks earlier, and make more informed decisions at the point of care. The third is operational efficiency, where hospitals use AI to optimize patient flow, manage capacity, and improve resource allocation.

Indonesia is beginning to see examples of this transition. EMC Healthcare has implemented InterSystems TrakCare® across eight hospital units and has since migrated to InterSystems IntelliCare™, becoming one of the first healthcare groups in Asia Pacific to go live with an AI-powered clinical system. According to Brustia, this represents an important shift from digitalization to AI-enabled care delivery.

Looking ahead, he believes healthcare organizations should focus on three priorities before pursuing AI at scale: interoperability, high-quality standardized data, and real-time analytics. He also sees significant opportunity for Indonesia to accelerate AI adoption through stronger health data platforms and collaboration between healthcare providers, technology companies, and government stakeholders.

“Digital transformation in healthcare is ultimately not just about technology. It is about creating a connected, intelligent health system that improves outcomes and expands access for every patient,” Brustia conclude.