Exchange of High-Frequency Medical Monitoring Data Using Fast Healthcare Interoperability Resources for Interoperable Integration Into Clinical Practice and Research: Methodological Development Study.
Overview
13 affiliations
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany, 49 20172377817
- Data Integration Center, Central IT Department, University Hospital Essen, Essen, Germany
- Institute for Transfusion Medicine, University Hospital Essen, Essen, Germany
- Center of Sleep and Telemedicine, University Hospital Essen - Ruhrlandklinik, Essen, Germany
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
- Institute of Medical Informatics, Heidelberg University, Heidelberg, Germany
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Essen, University Duisburg-Essen, Essen, Germany
- Applied Medical Informatics (AMI) Lab, Institute for Medical Biometry, Informatics, and Epidemiology, University Hospital Bonn, Bonn, Germany
- Applied Mathematical Physiology (AMP) Lab, Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany
- Staff Unit for Medical & Scientific Technology Development & Coordination (MWTek), University Hospital Bonn, Bonn, Germany
- Department of Pediatrics I, University Hospital Essen, Essen, Germany
- Berlin Institute of Health (BIH) at Charité - University Hospital Berlin, Berlin, Germany
Abstract
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.
Reproduced under the paper's license (CC BY), from the paper cited above.
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Data
Datasets cited
- doi:10.18112/
openneuro.ds007808.v1.0. , at OpenNeuro; found in the references0 - doi:10.18112/
openneuro.ds007823.v1.0. , at OpenNeuro; found in the references1
Versions
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
Version 1, 4 November 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 19 authors, 6 keywords, 6 MeSH terms, 28 references.
Cite
This paper
Kaya, S. E., Wen, Y., Schmidt, C. S., Warmer, S., Schäfer, H., Choo, V. Y., Pelka, O., Richter, J., Goos, P., Schmidt, K., Krefting, D., Zenker, S., Erdfelder, F., Bruns, N., Borys, K., Thun, S., Nensa, F., Arzideh, K., & Hosch, R. (2026). Exchange of High-Frequency Medical Monitoring Data Using Fast Healthcare Interoperability Resources for Interoperable Integration Into Clinical Practice and Research: Methodological Development Study. JMIR medical informatics, 14, e94282. https://
BibTeX
@article{kaya2026exchang
author = {Kaya, Sara Erma and Wen, Yutong and Schmidt, Cynthia Sabrina and Warmer, Sina and Schäfer, Henning and Choo, Vin Yeang and Pelka, Obioma and Richter, Jendrik and Goos, Philipp and Schmidt, Karsten and Krefting, Dagmar and Zenker, Sven and Erdfelder, Felix and Bruns, Nora and Borys, Katarzyna and Thun, Sylvia and Nensa, Felix and Arzideh, Kamyar and Hosch, René},
title = {{Exchange of High-Frequency Medical Monitoring Data Using Fast Healthcare Interoperability Resources for Interoperable Integration Into Clinical Practice and Research: Methodological Development Study}},
journal = {JMIR medical informatics},
year = {2026},
month = nov,
volume = {14},
pages = {e94282},
publisher = {JMIR Publications Inc.},
issn = {2291-9694},
doi = {10.2196/
url = {https://
pmid = {42970757},
pmcid = {PMC13727023}
}
RIS
TY - JOUR
AU - Kaya, Sara Erma
AU - Wen, Yutong
AU - Schmidt, Cynthia Sabrina
AU - Warmer, Sina
AU - Schäfer, Henning
AU - Choo, Vin Yeang
AU - Pelka, Obioma
AU - Richter, Jendrik
AU - Goos, Philipp
AU - Schmidt, Karsten
AU - Krefting, Dagmar
AU - Zenker, Sven
AU - Erdfelder, Felix
AU - Bruns, Nora
AU - Borys, Katarzyna
AU - Thun, Sylvia
AU - Nensa, Felix
AU - Arzideh, Kamyar
AU - Hosch, René
TI - Exchange of High-Frequency Medical Monitoring Data Using Fast Healthcare Interoperability Resources for Interoperable Integration Into Clinical Practice and Research: Methodological Development Study
T2 - JMIR medical informatics
J2 - JMIR Med Inform
PY - 2026
DA - 2026/
VL - 14
SP - e94282
SN - 2291-9694
PB - JMIR Publications Inc.
DO - 10.2196/
UR - https://
LA - en
ER -
CSL-JSON
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