Xinli Information Wins Taiwan Ministry of Health's '50 Excellent SMART App': Promoting Healthcare Efficiency with AI Coding Support
昕力資訊獲衛福部「臺灣50 優良SMART 應用程式獎」 - iThome
Summary
Xinli Information, a digital transformation leader, was recognized by the Taiwan Ministry of Health for being selected as one of the first 50 'Excellent SMART Applications,' specifically in the Medical AI category. The company aims to improve healthcare administration efficiency and accuracy by providing an AI-supported tool that solves challenges in radiology's ICD-10-PCS coding process.
Details
Xinli Information’s key module, “Yidian Jiutong” (PCS Coding Smart Assistant), part of its smart medical solution digiCare, was honored at the first selection of the 'Taiwan 50 Excellent SMART Applications: Medical AI Category,' organized by the Taiwan Ministry of Health. This initiative aims to promote cross-hospital and cross-system healthcare information interoperability based on the SMART on FHIR standardization architecture, which the MOH is promoting starting in 2025. The tool addresses complex issues inherent in traditional radiology ICD-10-PCS coding, such as manually interpreting multi-axial seven-digit codes from unstructured data, increased administrative burden due to switching between systems, and risks of human error or claim rejection. By utilizing an AI inference engine and Natural Language Processing (NLP) technology, the system automatically identifies and extracts information—such as contrast agents and anatomical sites—from radiology reports compliant with the international standard DICOM. This allows medical staff merely to review and approve the coding performed by the AI. Furthermore, it integrates with existing systems like Electronic Health Records (EHR) and HIS, eliminating the need for manual re-entry of patient data. Company executives highlighted that this award demonstrates Xinli's capability in medical IT. They predict that future healthcare applications will become 'plug-and-play,' significantly lowering adoption barriers and costs, thereby accelerating overall system efficiency and quality improvement across fragmented medical systems. The company also articulated a broader vision of building a national-level medical data research database for Taiwan.
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