Healthcare Interoperability Research Articles

OMOP-on-FHIR: A FHIR Server Development to Facilitate Data Interaction with the OMOP-CDM and FHIR for PGHD.

This paper introduces the concept of the implementation of a FHIR server for bidirectional data exchange with the OMOP-CDM. Leveraging FHIR as a metamodel, the implementation aims to promote a more interconnected and patient-centric healthcare ecosystem. The methodology involves utilizing the Java HAPI FHIR API for server architecture and validating the solution through patient data exchange with a FHIR reference implementation server. This initiative signifies a significant advancement in healthcare data interoperability, promising improved patient care quality and clinical research vigor.

HL7 FHIR in Health Research: A FHIR Specification for Metadata in Clinical, Epidemiological, and Public Health Studies.

This work presents the Fast Healthcare Interoperability Resources (FHIR®) specification of the NFDI4Health Metadata schema based on FHIR Version 4: We created 16 profiles to facilitate the integration of clinical, epidemiological, and public health study data. Despite challenges arising from the extensive MDS as well as missing concepts in semantic standards, it marks a significant advance in applying information technology standards to health research.

Synthetic Data Generation in Hematology - Paving the Way for OMOP and FHIR Integration.

This study advances the utility of synthetic study data in hematology, particularly for Acute Myeloid Leukemia (AML), by facilitating its integration into healthcare systems and research platforms through standardization into the Observational Medical Outcomes Partnership (OMOP) and Fast Healthcare Interoperability Resources (FHIR) formats. In our previous work, we addressed the need for high-quality patient data and used CTAB-GAN+ and Normalizing Flow (NFlow) to synthesize data from 1606 patients across four multicenter AML clinical trials. We published the generated synthetic cohorts, that accurately replicate the distributions of key demographic, laboratory, molecular, and cytogenetic variables, alongside patient outcomes, demonstrating high fidelity and usability. The conversion to the OMOP format opens avenues for comparative observational multi-center research by enabling seamless combination with related OMOP datasets, thereby broadening the scope of AML research. Similarly, standardization into FHIR facilitates further developments of applications, e.g. via the SMART-on-FHIR platform, offering realistic test data. This effort aims to foster a more collaborative research environment and facilitate the development of innovative tools and applications in AML care and research.

Using Health IT Standards to Facilitate Metadata Exchange Between Research Data Management Systems.

The National Research Data Infrastructure for Personal Health Data (NFDI4Health) uses Local Data Hubs (LDHs) to manage locally research studies, documents and sensitive personal data to support controlled data sharing. While research data management (RDM) systems facilitate the storage and preparation of data and metadata as well as organizational access, they often lack support for interoperability standards of the application domain. To support the exchange with external registries of research studies, we chose 17 attributes to characterize the most relevant aspects of clinical trials (in the following named "metadata profile"). We implemented the metadata profile in the RDM system FAIRDOM SEEK using core attributes and SEEK's extended metadata feature and created a mapping conforming to the Health Level 7 Fast Healthcare Interoperability Resources (FHIR) standard version R4. Finally, we implemented a prototype application interface for exports in FHIR-JSON format. We plan to extend the interface to serve central registries and support specific FHIR Implementation Guides from various use cases.

Comparing the Accuracy of Traditional vs. FHIR®-Based Extraction of Electronic Health Record Data for Two Clinical Trials.

While pilots and production use of software based on the Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard are increasing in clinical research, we lack consistent evaluative data on important outcomes, such as data accuracy. We compared the accuracy of EHR collected, FHIR® extracted data (called EHR-to-eCRF data collection) to traditional clinical trial data collection. The accuracy rate for EHR-collected data was significantly higher than for the same data collected through traditional methods. It is possible that EHR-collected (FHIR® extracted) data can substantially improve data quality in clinical studies while decreasing the burden on study sites.

Advancing Healthcare Through Interoperability: Implementing Scalable Solutions for Patient Data Integration.

Healthcare faces significant challenges in exchanging and utilizing health information across diverse providers, necessitating innovative solutions for improved interoperability. This study presents a comprehensive exploration of scalable technical and semantic solutions for patient care integration, emphasizing the implementation of these solutions within the framework of the Fast Healthcare Interoperability Resources (FHIR) standard. Our approach revolves around the development and deployment of Technical Interoperability Suite (TIS) and Semantic Interoperability Suite (SIS) technology solutions to disparate health information systems, predominantly Electronic Health Records (EHRs) into a unified Patient Care Platform, fostering comprehensive data exchange and utilization. The integration process involves importing data from various EHR systems and transforming imported patient data into FHIR-standardized formats. The provided solution supports various functionalities, including automatic and manual importation of patient data, through standard computer-readable templates. The integration of TIS and SIS solutions is underpinned by a robust technological framework, incorporating technologies such as Typescript, Deno, and document-oriented databases such as MongoDB. The effectiveness of our interoperability solutions was validated through deployment in multinational EU projects: ADLIFE and CAREPATH. The scalability and generalizability of our approach underscore its potential for diverse healthcare settings.

Integrating FHIR and UMLS in an Intelligent Tutoring System.

Our novel Intelligent Tutoring System (ITS) architecture integrates HL7 Fast Healthcare Interoperability Resources (FHIR) for data exchange and Unified Medical Language System (UMLS) codes for content mapping.

Data sources (LLM) for a Clinical Decision Support Model (SSDC) using a Healthcare Interoperability Resources (HL7-FHIR) platform for an ICU Ecosystem

Data sources (LLM) for a Clinical Decision Support Model (SSDC) using a Healthcare Interoperability Resources (HL7-FHIR) platform for an ICU Ecosystem

Musculoskeletal Disorder (MSD) Health Data Collection, Personalized Management and Exchange Using Fast Healthcare Interoperability Resources (FHIR).

With the proliferation and growing complexity of healthcare systems emerges the challenge of implementing scalable and interoperable solutions to seamlessly integrate heterogenous data from sources such as wearables, electronic health records, and patient reports that can provide a comprehensive and personalized view of the patient's health. Lack of standardization hinders the coordination between systems and stakeholders, impacting continuity of care and patient outcomes. Common musculoskeletal conditions affect people of all ages and can have a significant impact on quality of life. With physical activity and rehabilitation, these conditions can be mitigated, promoting recovery and preventing recurrence. Proper management of patient data allows for clinical decision support, facilitating personalized interventions and a patient-centered approach. Fast Healthcare Interoperability Resources (FHIR) is a widely adopted standard that defines healthcare concepts with the objective of easing information exchange and enabling interoperability throughout the healthcare sector, reducing implementation complexity without losing information integrity. This article explores the literature that reviews the contemporary role of FHIR, approaching its functioning, benefits, and challenges, and presents a methodology for structuring several types of health and wellbeing data, that can be routinely collected as observations and then encapsulated in FHIR resources, to ensure interoperability across systems. These were developed considering health industry standard guidelines, technological specifications, and using the experience gained from the implementation in various study cases, within European health-related research projects, to assess its effectiveness in the exchange of patient data in existing healthcare systems towards improving musculoskeletal disorders (MSDs).

The sample locator: A federated search tool for biosamples and associated data in Europe using HL7 FHIR

BackgroundThis study outlines the development of a highly interoperable federated IT infrastructure for academic biobanks located at the major university hospital sites across Germany. High-quality biosamples linked to clinical data, stored in biobanks are essential for biomedical research. We aimed to facilitate the findability of these biosamples and their associated data. Networks of biobanks provide access to even larger pools of samples and data even from rare diseases and small disease subgroups. The German Biobank Alliance (GBA) established in 2017 under the umbrella of the German Biobank Node (GBN), has taken on the mission of a federated data discovery service to make biosamples and associated data available to researchers across Germany and Europe. MethodsIn this context, we identified the requirements of researchers seeking human biosamples from biobanks and the needs of biobanks for data sovereignty over their samples and data in conjunction with the sample donor's consent. Based on this, we developed a highly interoperable federated IT infrastructure using standards such as Fast Healthcare Interoperability Resources (HL7 FHIR) and Clinical Quality Language (CQL). ResultsThe infrastructure comprises two major components enabling federated real-time access to biosample metadata, allowing privacy-compliant queries and subsequent project requests. It has been in use since 2019, connecting 16 German academic biobanks, with additional European biobanks joining. In production since 2019 it has run 4941 queries over the span of one year on more than 900,000 biosamples collected from more than 170,000 donors. ConclusionThis infrastructure enhances the visibility and accessibility of biosamples for research, addressing the growing demand for human biosamples and associated data in research. It also underscores the need for improvements in processes beyond IT infrastructure, aiming to advance biomedical research and similar infrastructure development in other fields.

Evolving Software Architecture Design in Telemedicine: A PRISMA-based Systematic Review.

This article presents a systematic review of recent advancements in telemedicine architectures for continuous monitoring, providing a comprehensive overview of the evolving software engineering practices underpinning these systems. The review aims to illuminate the critical role of telemedicine in delivering healthcare services, especially during global health crises, and to emphasize the importance of effectiveness, security, interoperability, and scalability in these systems. A systematic review methodology was employed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. As the primary research method, the PubMed, IEEE Xplore, and Scopus databases were searched to identify articles relevant to telemedicine architectures for continuous monitoring. Seventeen articles were selected for analysis, and a methodical approach was employed to investigate and synthesize the findings. The review identified a notable trend towards the integration of emerging technologies into telemedicine architectures. Key areas of focus include interoperability, security, and scalability. Innovations such as cognitive radio technology, behavior-based control architectures, Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) standards, cloud computing, decentralized systems, and blockchain technology are addressing challenges in remote healthcare delivery and continuous monitoring. This review highlights major advancements in telemedicine architectures, emphasizing the integration of advanced technologies to improve interoperability, security, and scalability. The findings underscore the successful application of cognitive radio technology, behavior-based control, HL7 FHIR standards, cloud computing, decentralized systems, and blockchain in advancing remote healthcare delivery.

Need for Data Standardization and Infrastructure of Research Data Management to Promote Using Real-world Data

Real-world data (RWD) have been increasingly used for regulatory decision-making and as a control group for new drug approval applications. RWD is also helpful in understanding information such as risk factors (e.g., pre-existing medical conditions, personal protective equipment, travel, contacts, smoking, and exposure to animals) and vaccination status for the coronavirus disease 2019 (COVID-19). The methodology of utilizing RWD is inconsistent across healthcare institutions. However, there are possible solutions to standardize RWD for clinical data use, which include the use of Clinical Data Interchange Standards Consortium (CDISC) standards, tools, and concepts. This study examines the availability of CDISC and other international standards for the utilization of RWD with concrete examples and presents the potential platform for implementation. We consider the solution currently available to temporarily convert clinical data-warehouse (DWH) data into the Fast Healthcare Interoperability Resources (FHIR) format to comply with the CDISC standard. This approach would allow for converting institution-level standards to national standards as an interim solution until FHIR is supported, mapping national standards to international standards. We believe that the ideal research environment is a data platform that complies with national and international regulations related to RWD applications. Within such a platform, users can share data freely, rather than rely on a specific facility or vendor. Data platform developments are progressing in Japan and globally. In Japan, initiatives to use research data on research data platforms are being conducted. We are experimenting with implementing tools and knowledge shared by CDISC.

A scalable and transparent data pipeline for AI-enabled health data ecosystems.

Transparency and traceability are essential for establishing trustworthy artificial intelligence (AI). The lack of transparency in the data preparation process is a significant obstacle in developing reliable AI systems which can lead to issues related to reproducibility, debugging AI models, bias and fairness, and compliance and regulation. We introduce a formal data preparation pipeline specification to improve upon the manual and error-prone data extraction processes used in AI and data analytics applications, with a focus on traceability. We propose a declarative language to define the extraction of AI-ready datasets from health data adhering to a common data model, particularly those conforming to HL7 Fast Healthcare Interoperability Resources (FHIR). We utilize the FHIR profiling to develop a common data model tailored to an AI use case to enable the explicit declaration of the needed information such as phenotype and AI feature definitions. In our pipeline model, we convert complex, high-dimensional electronic health records data represented with irregular time series sampling to a flat structure by defining a target population, feature groups and final datasets. Our design considers the requirements of various AI use cases from different projects which lead to implementation of many feature types exhibiting intricate temporal relations. We implement a scalable and high-performant feature repository to execute the data preparation pipeline definitions. This software not only ensures reliable, fault-tolerant distributed processing to produce AI-ready datasets and their metadata including many statistics alongside, but also serve as a pluggable component of a decision support application based on a trained AI model during online prediction to automatically prepare feature values of individual entities. We deployed and tested the proposed methodology and the implementation in three different research projects. We present the developed FHIR profiles as a common data model, feature group definitions and feature definitions within a data preparation pipeline while training an AI model for "predicting complications after cardiac surgeries". Through the implementation across various pilot use cases, it has been demonstrated that our framework possesses the necessary breadth and flexibility to define a diverse array of features, each tailored to specific temporal and contextual criteria.

Uncovering Harmonization Potential in Health Care Data Through Iterative Refinement of Fast Healthcare Interoperability Resources Profiles Based on Retrospective Discrepancy Analysis: Case Study.

Cross-institutional interoperability between health care providers remains a recurring challenge worldwide. The German Medical Informatics Initiative, a collaboration of 37 university hospitals in Germany, aims to enable interoperability between partner sites by defining Fast Healthcare Interoperability Resources (FHIR) profiles for the cross-institutional exchange of health care data, the Core Data Set (CDS). The current CDS and its extension modules define elements representing patients' health care records. All university hospitals in Germany have made significant progress in providing routine data in a standardized format based on the CDS. In addition, the central research platform for health, the German Portal for Medical Research Data feasibility tool, allows medical researchers to query the available CDS data items across many participating hospitals. In this study, we aimed to evaluate a novel approach of combining the current top-down generated FHIR profiles with the bottom-up generated knowledge gained by the analysis of respective instance data. This allowed us to derive options for iteratively refining FHIR profiles using the information obtained from a discrepancy analysis. We developed an FHIR validation pipeline and opted to derive more restrictive profiles from the original CDS profiles. This decision was driven by the need to align more closely with the specific assumptions and requirements of the central feasibility platform's search ontology. While the original CDS profiles offer a generic framework adaptable for a broad spectrum of medical informatics use cases, they lack the specificity to model the nuanced criteria essential for medical researchers. A key example of this is the necessity to represent specific laboratory codings and values interdependencies accurately. The validation results allow us to identify discrepancies between the instance data at the clinical sites and the profiles specified by the feasibility platform and addressed in the future. A total of 20 university hospitals participated in this study. Historical factors, lack of harmonization, a wide range of source systems, and case sensitivity of coding are some of the causes for the discrepancies identified. While in our case study, Conditions, Procedures, and Medications have a high degree of uniformity in the coding of instance data due to legislative requirements for billing in Germany, we found that laboratory values pose a significant data harmonization challenge due to their interdependency between coding and value. While the CDS achieves interoperability, different challenges for federated data access arise, requiring more specificity in the profiles to make assumptions on the instance data. We further argue that further harmonization of the instance data can significantly lower required retrospective harmonization efforts. We recognize that discrepancies cannot be resolved solely at the clinical site; therefore, our findings have a wide range of implications and will require action on multiple levels and by various stakeholders.

FHIR-standardized data collection on the clinical rehabilitation pathway of trans-femoral amputation patients

Lower limb amputation is a medical intervention which causes motor disability and may compromise quality of life. Several factors determine patients’ health outcomes, including an appropriate prosthetic provision and an effective rehabilitation program, necessitating a thorough quantitative observation through different data sources. In this context, the role of interoperability becomes essential, facilitating the reuse of real-world data through the provision of structured and easily accessible databases. This study introduces a comprehensive 10-year dataset encompassing clinical features, mobility measurements, and prosthetic knees of 1006 trans-femoral amputees during 1962 hospital stays for rehabilitation. The dataset is made available in both comma-separated values (CSV) format and HL7 Fast Healthcare Interoperability Resources (FHIR)-based representation, ensuring broad utility and compatibility for researchers and healthcare practitioners. This initiative contributes to advancing community understanding of post-amputation rehabilitation and underscores the significance of interoperability in promoting seamless data sharing for meaningful insights into healthcare outcomes.

MoCab: A framework for the deployment of machine learning models across health information systems

Background and Objective:Machine learning models are vital for enhancing healthcare services. However, integrating them into health information systems (HISs) introduces challenges beyond clinical decision making, such as interoperability and diverse electronic health records (EHR) formats. We proposed Model Cabinet Architecture (MoCab), a framework designed to leverage fast healthcare interoperability resources (FHIR) as the standard for data storage and retrieval when deploying machine learning models across various HISs, addressing the challenges highlighted by platforms such as EPOCH®, ePRISM®, KETOS, and others. Methods:The MoCab architecture is designed to streamline predictive modeling in healthcare through a structured framework incorporating several specialized parts. The Data Service Center manages patient data retrieval from FHIR servers. These data are then processed by the Knowledge Model Center, where they are formatted and fed into predictive models. The Model Retraining Center is crucial in continuously updating these models to maintain accuracy in dynamic clinical environments. The framework further incorporates Clinical Decision Support (CDS) Hooks for issuing clinical alerts. It uses Substitutable Medical Apps Reusable Technologies (SMART) on FHIR to develop applications for displaying alerts, prediction results, and patient records. Results:The MoCab framework was demonstrated using three types of predictive models: a scoring model (qCSI), a machine learning model (NSTI), and a deep learning model (SPC), applied to synthetic data that mimic a major EHR system. The implementations showed how MoCab integrates predictive models with health data for clinical decision support, utilizing CDS Hooks and SMART on FHIR for seamless HIS integration. The demonstration confirmed the practical utility of MoCab in supporting clinical decision making, validated by its application in various healthcare settings. Conclusions:We demonstrate MoCab’s potential in promoting the interoperability of machine learning models and enhancing its utility across various EHRs. Despite facing challenges like FHIR adoption, MoCab addresses key challenges in adapting machine learning models within healthcare settings, paving the way for further enhancements and broader adoption.

Data Sources for use in a Healthcare Interoperability Resources (HL7-FHIR) Platform. A Decision Guide for Clinicians and Data Scientists in Public ICUs

Data Sources for use in a Healthcare Interoperability Resources (HL7-FHIR) Platform. A Decision Guide for Clinicians and Data Scientists in Public ICUs

Block chain technology for e-health

There is a dearth of interoperability between apps, data streams, and predictability in the healthcare industry for a significant amount of the data generated by multiple digital ecosystems. Real-time data streams can be derived as meaningful and scalable enough to enable real-time healthcare predictive analytics thanks to the new technology approach in distributed messaging and Blockchain, which has become a fundamental component of many healthcare technology stacks. Additionally, absorbing data streams from multiple sources from patterns of data can enhance models that are hampered by complex and lengthy analyses by raising the level of prediction and accuracy. Improved responses, lowered availability requirements, and unified predictive modeling will speed up healthcare interoperability and, in turn, improve diagnosis accuracy, move evidence-based medicine (EBM) in the right direction, and produce other positive effects on healthcare that improve best results and quality.

Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension.

Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge. This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research. Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data. This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings. These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.

Data quality and timeliness analysis for post-vaccination adverse event cases reported through healthcare data exchange to FDA BEST pilot platform.

This study is part of the U.S. Food and Drug Administration (FDA)'s Biologics Effectiveness and Safety (BEST) initiative, which aims to improve the FDA's postmarket surveillance capabilities by using real-world data (RWD). In the United States, using RWD for postmarket surveillance has been hindered by the inability to exchange clinical data between healthcare providers and public health organizations in an interoperable format. However, the Office of the National Coordinator for Health Information Technology (ONC) has recently enacted regulation requiring all healthcare providers to support seamless access, exchange, and use of electronic health information through the interoperable HL7 Fast Healthcare Interoperability Resources (FHIR) standard. To leverage the recent ONC changes, BEST designed a pilot platform to query and receive the clinical information necessary to analyze suspected AEs. This study assessed the feasibility of using the RWD received through the data exchange of FHIR resources to study post-vaccination AE cases by evaluating the data volume, query response time, and data quality. The study used RWD from 283 post-vaccination AE cases, which were received through the platform. We used descriptive statistics to report results and apply 322 data quality tests based on a data quality framework for EHR. The volume analysis indicated the average clinical resources for a post-vaccination AE case was 983.9 for the median partner. The query response time analysis indicated that cases could be received by the platform at a median of 3 min and 30 s. The quality analysis indicated that most of the data elements and conformance requirements useful for postmarket surveillance were met. This study describes the platform's data volume, data query response time, and data quality results from the queried postvaccination adverse event cases and identified updates to current standards to close data quality gaps.