Enhancing Real-Time Clinical Decision-Making through AI-Integrated FHIR Solutions: A Medplum Implementation

Abstract

In recent years, the healthcare sector has experienced rapid advancements in data interoperability, artificial intelligence (AI), and real-time decision support systems. Fast Healthcare Interoperability Resources (FHIR), an open standard for healthcare data sharing, has emerged as a key framework for facilitating interoperability among healthcare platforms. This paper presents a comprehensive analysis of integrating FHIR with Medplum, an open-source platform, to enhance real-time clinical decision-making through AI-driven solutions. The system was evaluated in real-world settings, demonstrating significant improvements in both efficiency and accuracy. The AI-powered clinical documentation system reduced the time healthcare providers spent on paperwork by 50%, enabling them to allocate more time to direct patient care. Furthermore, the system lowered documentation error rates from 6% to 2% under low-load conditions and from 10% to 4% under high-load conditions, resulting in an average error reduction of 60%. Additionally, the system exhibited strong scalability, maintaining acceptable response times as the number of users increased. Response times rose from 50 milliseconds (ms) under low-load conditions to 300 ms under extremely high-load conditions (1000 users).

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