Yutong Wen

Background: Continuous patient monitoring generates vast volumes of high-frequency monitoring data, such as electrocardiogram, electroencephalography, and invasive blood pressure tracings, that are often persisted in heterogeneous, noninteroperable formats, limiting integration with electronic health records and secondary use. Although Fast Healthcare Interoperability Resources (FHIR) supports general health data exchange, native support for high-frequency medical monitoring data, including associated metadata and data quality metrics, remains limited. Objective: This study aims to develop a modality-agnostic FHIR package implementing a Metadata-Proxy pattern to enable scalable, semantically precise representation of high-frequency medical monitoring data, decoupling structured clinical metadata from raw signal storage. Methods: This study provides the FHIR package Time Series Integration and Data in Endpoints (TIDE), a collection of FHIR Release 4 artifacts for interoperable exchange and the standardization of high-frequency monitoring clinical data, using electroencephalography as the primary use case while remaining applicable to generic medical monitoring data. The core TIDE Observation profile separates technical metadata and analytical summaries stored directly in FHIR from high-volume raw data referenced via Endpoint resources. Quantitative results are standardized with pattern slicing, including sampling frequency, statistical summaries, and data quality metrics. Semantic interoperability was achieved through a hybrid terminology binding, using Systematized Nomenclature of Medicine Clinical Terms, Logical Observation Identifiers Names and Codes for modalities, and custom CodeSystems for technical metadata, waveform modalities, channel identifiers, and connection types not covered by standard terminologies. All profiles were authored in FHIR Shorthand and compiled with SUSHI to produce a validated Implementation Guide conformant with the FHIR Release 4 core specification. Results: The resulting framework provides a modality-agnostic structure for high-frequency monitoring data, comprising 7 primary FHIR artifacts that define 16 distinct component slices for metadata and signal analysis results. Observation resources reference associated Device and Endpoint instances, encapsulate acquisition parameters, and link to full-time-series repositories. Controlled vocabularies ensure unambiguous exchange, thereby enabling downstream use in research and decision support. The architecture decouples the metadata layer from the storage layer via a standardized Endpoint extension, enabling efficient querying of signals without retrieving large amounts of raw high-frequency monitoring data. Conclusions: TIDE provides a reusable, standards-compliant framework for integrating continuous high-frequency monitoring data into FHIR-based ecosystems. By separating semantically rich, queryable metadata from raw signal storage, TIDE enables longitudinal patient monitoring and automated signal quality-based triage without requiring modification of existing signal storage infrastructure.

Background: Although Digital Imaging and Communications in Medicine (DICOM) metadata are widely used to manage medical imaging data and support clinical workflows, their suitability as a sole basis for automatic computed tomography (CT) series labeling and characterization is limited. DICOM metadata are frequently inconsistently populated, institution specific, use unregulated private tags, and have variable reliability even within standardized fields. Consequently, automated series selection for downstream AI applications often remains unreliable, necessitating manual curation within clinical workflows. Objective: This study presents Orchestrate, a modular AI framework for automated orchestration of CT imaging data. By integrating a hierarchy of deep learning models, Orchestrate enables pixel-level classification and routing of CT series and accurate metadata-independent identification of anatomical regions, contrast-enhanced series, and reconstruction kernels, supporting seamless downstream AI integration without manual curation. Methods: Orchestrate combined 3 pretrained models for anatomical regions, landmarks, and body part classification and 4 newly developed You Only Look Once (YOLO) v8–based models to classify contrast enhancement, recognize reconstruction kernels, and infer laterality. Three datasets were used: an internal development dataset comprising 27,418 CT studies for individual model development, an internal framework evaluation dataset comprising 200 CT studies to assess the complete framework under a simulated real-world scenario, and an external dataset from The Cancer Imaging Archive comprising 100 CT studies. For the internal development data, reference standards were derived from complete and unambiguous DICOM metadata following institutional definitions. For the 2 evaluation datasets, 3 radiographers independently established reference standards as DICOM metadata were not assumed to be complete or consistent. Clinical utility was assessed through cohort selection tasks involving 3 predefined target cases, with 3 radiologists reviewing selection accuracy. The interrater agreement was assessed using the Fleiss κ. The model performance was evaluated using F1-scores. Results: DICOM metadata were incomplete in the internal framework evaluation dataset and external dataset, with missing rates of 42.9% (413/963) and 92.9% (105/113) for contrast enhancement, respectively, and a missing reconstruction kernel information rate of 0.4% (4/963) in the internal framework evaluation dataset. During model development, individual models achieved macro–F1-scores ranging from 0.982 to 0.989. At the framework level, Orchestrate achieved high classification performance across internal (weighted F1-score ranged from 0.920 to 1.000; macro–F1-score ranged from 0.879 to 1.000) and external (weighted F1-score ranged from 0.946 to 1.000; macro–F1-score ranged from 0.777 to 0.929) cohorts. For clinical use cases, the overall selection accuracy was 97.7% (217/222). Conclusions: Orchestrate enables automated pixel-based classification, detection, and semantic description of CT series, reducing reliance on manual selection and the risk of inconsistent metadata. By generating standardized semantic content, the framework provides a proof of concept for improving interoperability with clinical systems and supports the reliable, reproducible integration of AI-driven imaging pipelines into clinical workflows.

BACKGROUND Although Digital Imaging and Communications in Medicine (DICOM) metadata are widely used to manage medical imaging data and support clinical workflows, their suitability as a sole basis for automatic computed tomography (CT) series labeling and characterization is limited. DICOM metadata are frequently inconsistently populated, institution specific, use unregulated private tags, and have variable reliability even within standardized fields. Consequently, automated series selection for downstream AI applications often remains unreliable, necessitating manual curation within clinical workflows. OBJECTIVE This study presents Orchestrate, a modular AI framework for automated orchestration of CT imaging data. By integrating a hierarchy of deep learning models, Orchestrate enables pixel-level classification and routing of CT series and accurate metadata-independent identification of anatomical regions, contrast-enhanced series, and reconstruction kernels, supporting seamless downstream AI integration without manual curation. METHODS Orchestrate combined 3 pretrained models for anatomical regions, landmarks, and body part classification and 4 newly developed You Only Look Once (YOLO) v8–based models to classify contrast enhancement, recognize reconstruction kernels, and infer laterality. Three datasets were used: an internal development dataset comprising 27,418 CT studies for individual model development, an internal framework evaluation dataset comprising 200 CT studies to assess the complete framework under a simulated real-world scenario, and an external dataset from The Cancer Imaging Archive comprising 100 CT studies. For the internal development data, reference standards were derived from complete and unambiguous DICOM metadata following institutional definitions. For the 2 evaluation datasets, 3 radiographers independently established reference standards as DICOM metadata were not assumed to be complete or consistent. Clinical utility was assessed through cohort selection tasks involving 3 predefined target cases, with 3 radiologists reviewing selection accuracy. The interrater agreement was assessed using the Fleiss κ. The model performance was evaluated using F1-scores. RESULTS DICOM metadata were incomplete in the internal framework evaluation dataset and external dataset, with missing rates of 42.9% (413/963) and 92.9% (105/113) for contrast enhancement, respectively, and a missing reconstruction kernel information rate of 0.4% (4/963) in the internal framework evaluation dataset. During model development, individual models achieved macro–F1-scores ranging from 0.982 to 0.989. At the framework level, Orchestrate achieved high classification performance across internal (weighted F1-score ranged from 0.920 to 1.000; macro–F1-score ranged from 0.879 to 1.000) and external (weighted F1-score ranged from 0.946 to 1.000; macro–F1-score ranged from 0.777 to 0.929) cohorts. For clinical use cases, the overall selection accuracy was 97.7% (217/222). CONCLUSIONS Orchestrate enables automated pixel-based classification, detection, and semantic description of CT series, reducing reliance on manual selection and the risk of inconsistent metadata. By generating standardized semantic content, the framework provides a proof of concept for improving interoperability with clinical systems and supports the reliable, reproducible integration of AI-driven imaging pipelines into clinical workflows.

Background: Recent natural language processing breakthroughs, particularly with the emergence of large language models (LLMs), have demonstrated remarkable capabilities on general knowledge benchmarks. However, there is limited data on the performance and understanding of these models in relation to the Fast Healthcare Interoperability Resources (FHIR) standard. The complexity and specialized nature of FHIR present challenges for LLMs, which are typically trained on broad datasets and may have a limited understanding of the nuances required for domain-specific tasks. Improving health data interoperability can greatly benefit the use of clinical data and interaction with electronic health records. Objective: This study presents the Fast Healthcare Interoperability Resources (FHIR) Workbench, a comprehensive suite of datasets designed to evaluate the ability of LLMs to understand and apply the FHIR standard. Methods: In total, 4 evaluation datasets were created to assess the FHIR knowledge and capabilities of LLMs. These tasks include multiple-choice questions on general FHIR concepts and the FHIR Representational State Transfer (REST) application programming interface, as well as correctly identifying the resource type and generating FHIR resources from unstructured clinical patient notes. In addition, we evaluate open-source LLMs, such as Qwen 2.5 Coder and DeepSeek-V3, and commercial LLMs, including GPT-4o and Gemini 2, on these tasks in a zero-shot setting. To provide context for interpreting LLM performance, a subset of the datasets was human-evaluated by recruiting 6 participants with varying levels of FHIR expertise. Results: Our evaluation across multiple FHIR tasks revealed nuanced performance metrics. Commercial models demonstrated exceptional capabilities, with GPT-4o achieving a 0.9990 F₁-score on the FHIR-ResourceID task, 0.9400 on the FHIR-QA task, and 0.9267 on the FHIR-RESTQA task. Open-source models also demonstrated strong performance, with DeepSeek-v3 achieving 0.9400 on FHIR-QA, 0.9400 on FHIR-RESTQA, and 0.9142 on FHIR-ResourceID. Qwen 2.5 Coder-7B-Instruct demonstrated high accuracy, scoring 0.9533 on FHIR-QA and 0.8920 on FHIR-ResourceID. However, all models struggled with the Note2FHIR task, with performance ranging from 0.0382 (OLMo) to a maximum of 0.3633 (GPT-4.5-preview), highlighting the significant challenge of converting unstructured clinical text into FHIR-compliant resources. Human participants achieved accuracy scores ranging from 0.50 to 1.0 across the first 3 tasks. Conclusions: This study highlights the competitive performance of both open-source models, such as Qwen and DeepSeek, and commercial models, such as GPT-4o and Gemini, in FHIR-related tasks. While open-source models are advancing rapidly, commercial models still have an advantage for specific, complex tasks. The FHIR Workbench offers a valuable platform for evaluating the capabilities of these models and promoting improvements in health data interoperability.

Background: Fast Healthcare Interoperability Resources (FHIR) is a widely used standard for storing and exchanging health care data. At the same time, image-based artificial intelligence (AI) models for quantifying relevant body structures and organs from routine computed tomography (CT)/magnetic resonance imaging scans have emerged. The missing link, simultaneously a needed step in advancing personalized medicine, is the incorporation of measurements delivered by AI models into an interoperable and standardized format. Incorporating image-based measurements and biomarkers into FHIR profiles can standardize data exchange, enabling timely, personalized treatment decisions and improving the precision and efficiency of patient care. Objective: This study aims to present the synergistic incorporation of CT-derived body organ and composition measurements with FHIR, delineating an initial paradigm for storing image-based biomarkers. Methods: This study integrated the results of the Body and Organ Analysis (BOA) model into FHIR profiles to enhance the interoperability of image-based biomarkers in radiology. The BOA model was selected as an exemplary AI model due to its ability to provide detailed body composition and organ measurements from CT scans. The FHIR profiles were developed based on 2 primary observation types: Body Composition Analysis (BCA Observation) for quantitative body composition metrics and Body Structure Observation for organ measurements. These profiles were structured to interoperate with a specially designed Diagnostic Report profile, which references the associated Imaging Study, ensuring a standardized linkage between image data and derived biomarkers. To ensure interoperability, all labels were mapped to SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms) or RadLex terminologies using specific value sets. The profiles were developed using FHIR Shorthand (FSH) and SUSHI, enabling efficient definition and implementation guide generation, ensuring consistency and maintainability. Results: In this study, 4 BOA profiles, namely, Body Composition Analysis Observation, Body Structure Volume Observation, Diagnostic Report, and Imaging Study, have been presented. These FHIR profiles, which cover 104 anatomical landmarks, 8 body regions, and 8 tissues, enable the interoperable usage of the results of AI segmentation models, providing a direct link between image studies, series, and measurements. Conclusions: The BOA profiles provide a foundational framework for integrating AI-derived imaging biomarkers into FHIR, bridging the gap between advanced imaging analytics and standardized health care data exchange. By enabling structured, interoperable representation of body composition and organ measurements, these profiles facilitate seamless integration into clinical and research workflows, supporting improved data accessibility and interoperability. Their adaptability allows for extension to other imaging modalities and AI models, fostering a more standardized and scalable approach to using imaging biomarkers in precision medicine. This work represents a step toward enhancing the integration of AI-driven insights into digital health ecosystems, ultimately contributing to more data-driven, personalized, and efficient patient care.

BACKGROUND Fast Healthcare Interoperability Resources (FHIR) is a widely used standard for storing and exchanging health care data. At the same time, image-based artificial intelligence (AI) models for quantifying relevant body structures and organs from routine computed tomography (CT)/magnetic resonance imaging scans have emerged. The missing link, simultaneously a needed step in advancing personalized medicine, is the incorporation of measurements delivered by AI models into an interoperable and standardized format. Incorporating image-based measurements and biomarkers into FHIR profiles can standardize data exchange, enabling timely, personalized treatment decisions and improving the precision and efficiency of patient care. OBJECTIVE This study aims to present the synergistic incorporation of CT-derived body organ and composition measurements with FHIR, delineating an initial paradigm for storing image-based biomarkers. METHODS This study integrated the results of the Body and Organ Analysis (BOA) model into FHIR profiles to enhance the interoperability of image-based biomarkers in radiology. The BOA model was selected as an exemplary AI model due to its ability to provide detailed body composition and organ measurements from CT scans. The FHIR profiles were developed based on 2 primary observation types: Body Composition Analysis (BCA Observation) for quantitative body composition metrics and Body Structure Observation for organ measurements. These profiles were structured to interoperate with a specially designed Diagnostic Report profile, which references the associated Imaging Study, ensuring a standardized linkage between image data and derived biomarkers. To ensure interoperability, all labels were mapped to SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms) or RadLex terminologies using specific value sets. The profiles were developed using FHIR Shorthand (FSH) and SUSHI, enabling efficient definition and implementation guide generation, ensuring consistency and maintainability. RESULTS In this study, 4 BOA profiles, namely, Body Composition Analysis Observation, Body Structure Volume Observation, Diagnostic Report, and Imaging Study, have been presented. These FHIR profiles, which cover 104 anatomical landmarks, 8 body regions, and 8 tissues, enable the interoperable usage of the results of AI segmentation models, providing a direct link between image studies, series, and measurements. CONCLUSIONS The BOA profiles provide a foundational framework for integrating AI-derived imaging biomarkers into FHIR, bridging the gap between advanced imaging analytics and standardized health care data exchange. By enabling structured, interoperable representation of body composition and organ measurements, these profiles facilitate seamless integration into clinical and research workflows, supporting improved data accessibility and interoperability. Their adaptability allows for extension to other imaging modalities and AI models, fostering a more standardized and scalable approach to using imaging biomarkers in precision medicine. This work represents a step toward enhancing the integration of AI-driven insights into digital health ecosystems, ultimately contributing to more data-driven, personalized, and efficient patient care.