Minimal Common Oncology Data Elements Genomics Pilot Project: Enhancing Oncology Research Through Electronic Health Record Interoperability at Vanderbilt University Medical Center.

PURPOSE: The expanding presence of the electronic health record (EHR) underscores the necessity for improved interoperability. To test the interoperability within the field of oncology research, our team at Vanderbilt University Medical Center (VUMC) enabled our Epic-based EHR to be compatible with the Minimal Common Oncology Data Elements (mCODE), which is a Fast Healthcare Interoperability Resources (FHIR)-based consensus data standard created to facilitate the transmission of EHRs for patients with cancer. METHODS: Our approach used an extract, transform, load tool for converting EHR data from the VUMC Epic Clarity database into mCODE-compatible profiles. We established a sandbox environment on Microsoft Azure for data migration, deployed a FHIR server to handle application programming interface (API) requests, and mapped VUMC data to align with mCODE structures. In addition, we constructed a web application to demonstrate the practical use of mCODE profiles in health care. RESULTS: We developed an end-to-end pipeline that converted EHR data into mCODE-compliant profiles, as well as a web application that visualizes genomic data and provides cancer risk assessments. Despite the complexities of aligning traditional EHR databases with mCODE standards and the limitations of FHIR APIs in supporting advanced statistical methodologies, this project successfully demonstrates the practical integration of mCODE standards into existing health care infrastructures. CONCLUSION: This study provides a proof of concept for the interoperability of mCODE within a major health care institution's EHR system, highlighting both the potential and the current limitations of FHIR APIs in supporting complex data analysis for oncology research.

A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis.

The development of platforms for distributed analytics has been driven by a growing need to comply with various governance-related or legal constraints. Among these platforms, the so-called Personal Health Train (PHT) is one representative that has emerged over the recent years. However, in projects that require data from sites featuring different PHT infrastructures, institutions are facing challenges emerging from the combination of multiple PHT ecosystems, including data governance, regulatory compliance, or the modification of existing workflows. In these scenarios, the interoperability of the platforms is preferable. In this work, we introduce a conceptual framework for the technical interoperability of the PHT covering five essential requirements: Data integration, unified station identifiers, mutual metadata, aligned security protocols, and business logic. We evaluated our concept in a feasibility study that involves two distinct PHT infrastructures: PHT-meDIC and PADME. We analyzed data on leukodystrophy from patients in the University Hospitals of Tübingen and Leipzig, and patients with differential diagnoses at the University Hospital Aachen. The results of our study demonstrate the technical interoperability between these two PHT infrastructures, allowing researchers to perform analyses across the participating institutions. Our method is more space-efficient compared to the multi-homing strategy, and it shows only a minimal time overhead.

Advancing Interoperability and Prior Authorization Reform.

This Viewpoint discusses the provisions and potential of the new Centers for Medicare and Medicaid Services (CMS) Interoperability and Prior Authorization Final Rule.

Making Science Computable Using Evidence-Based Medicine on Fast Healthcare Interoperability Resources: Standards Development Project.

BACKGROUND: Evidence-based medicine (EBM) has the potential to improve health outcomes, but EBM has not been widely integrated into the systems used for research or clinical decision-making. There has not been a scalable and reusable computer-readable standard for distributing research results and synthesized evidence among creators, implementers, and the ultimate users of that evidence. Evidence that is more rapidly updated, synthesized, disseminated, and implemented would improve both the delivery of EBM and evidence-based health care policy. OBJECTIVE: This study aimed to introduce the EBM on Fast Healthcare Interoperability Resources (FHIR) project (EBMonFHIR), which is extending the methods and infrastructure of Health Level Seven (HL7) FHIR to provide an interoperability standard for the electronic exchange of health-related scientific knowledge. METHODS: As an ongoing process, the project creates and refines FHIR resources to represent evidence from clinical studies and syntheses of those studies and develops tools to assist with the creation and visualization of FHIR resources. RESULTS: The EBMonFHIR project created FHIR resources (ie, ArtifactAssessment, Citation, Evidence, EvidenceReport, and EvidenceVariable) for representing evidence. The COVID-19 Knowledge Accelerator (COKA) project, now Health Evidence Knowledge Accelerator (HEvKA), took this work further and created FHIR resources that express EvidenceReport, Citation, and ArtifactAssessment concepts. The group is (1) continually refining FHIR resources to support the representation of EBM; (2) developing controlled terminology related to EBM (ie, study design, statistic type, statistical model, and risk of bias); and (3) developing tools to facilitate the visualization and data entry of EBM information into FHIR resources, including human-readable interfaces and JSON viewers. CONCLUSIONS: EBMonFHIR resources in conjunction with other FHIR resources can support relaying EBM components in a manner that is interoperable and consumable by downstream tools and health information technology systems to support the users of evidence.

Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association.

Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.

[Interoperability Working Group: core dataset and information systems for data integration and data exchange in the Medical Informatics Initiative]. / Arbeitsgruppe Interoperabilität: Kerndatensatz und Informationssysteme für Integration und Austausch von Daten in der Medizininformatik-Initiative.

The interoperability Working Group of the Medical Informatics Initiative (MII) is the platform for the coordination of overarching procedures, data structures, and interfaces between the data integration centers (DIC) of the university hospitals and national and international interoperability committees. The goal is the joint content-related and technical design of a distributed infrastructure for the secondary use of healthcare data that can be used via the Research Data Portal for Health. Important general conditions are data privacy and IT security for the use of health data in biomedical research. To this end, suitable methods are used in dedicated task forces to enable procedural, syntactic, and semantic interoperability for data use projects. The MII core dataset was developed as several modules with corresponding information models and implemented using the HL7® FHIR® standard to enable content-related and technical specifications for the interoperable provision of healthcare data through the DIC. International terminologies and consented metadata are used to describe these data in more detail. The overall architecture, including overarching interfaces, implements the methodological and legal requirements for a distributed data use infrastructure, for example, by providing pseudonymized data or by federated analyses. With these results of the Interoperability Working Group, the MII is presenting a future-oriented solution for the exchange and use of healthcare data, the applicability of which goes beyond the purpose of research and can play an essential role in the digital transformation of the healthcare system.

Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange.

BACKGROUND: Efficient data exchange and health care interoperability are impeded by medical records often being in nonstandardized or unstructured natural language format. Advanced language models, such as large language models (LLMs), may help overcome current challenges in information exchange. OBJECTIVE: This study aims to evaluate the capability of LLMs in transforming and transferring health care data to support interoperability. METHODS: Using data from the Medical Information Mart for Intensive Care III and UK Biobank, the study conducted 3 experiments. Experiment 1 assessed the accuracy of transforming structured laboratory results into unstructured format. Experiment 2 explored the conversion of diagnostic codes between the coding frameworks of the ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) using a traditional mapping table and a text-based approach facilitated by the LLM ChatGPT. Experiment 3 focused on extracting targeted information from unstructured records that included comprehensive clinical information (discharge notes). RESULTS: The text-based approach showed a high conversion accuracy in transforming laboratory results (experiment 1) and an enhanced consistency in diagnostic code conversion, particularly for frequently used diagnostic names, compared with the traditional mapping approach (experiment 2). In experiment 3, the LLM showed a positive predictive value of 87.2% in extracting generic drug names. CONCLUSIONS: This study highlighted the potential role of LLMs in significantly improving health care data interoperability, demonstrated by their high accuracy and efficiency in data transformation and exchange. The LLMs hold vast potential for enhancing medical data exchange without complex standardization for medical terms and data structure.

Extracting Electronic Health Record Neuroblastoma Treatment Data With High Fidelity Using the REDCap Clinical Data Interoperability Services Module.

PURPOSE: Although the International Neuroblastoma Risk Group Data Commons (INRGdc) has enabled seminal large cohort studies, the research is limited by the lack of real-world, electronic health record (EHR) treatment data. To address this limitation, we evaluated the feasibility of extracting treatment data directly from EHRs using the REDCap Clinical Data Interoperability Services (CDIS) module for future submission to the INRGdc. METHODS: Patients enrolled on the Children's Oncology Group neuroblastoma biology study ANBL00B1 (ClinicalTrials.gov identifier: NCT00904241) who received care at the University of Chicago (UChicago) or the Vanderbilt University Medical Center (VUMC) after the go-live dates for the Fast Healthcare Interoperability Resources (FHIR)-compliant EHRs were identified. Antineoplastic drug orders were extracted using the CDIS module. To validate the CDIS output, antineoplastic agents extracted through FHIR were compared with those queried through EHR relational databases (UChicago's Clinical Research Data Warehouse and VUMC's Epic Clarity database) and manual chart review. RESULTS: The analytic cohort consisted of 41 patients at UChicago and 32 VUMC patients. Antineoplastic drug orders were identified in the extracted EHR records of 39 (95.1%) UChicago patients and 26 (81.3%) VUMC patients. Manual chart review confirmed that patients with missing (n = 8) or discontinued (n = 1) orders in the CDIS output did not receive antineoplastic agents during the timeframe of the study. More than 99% of the antineoplastic drug orders in the EHR relational databases were identified in the corresponding CDIS output. CONCLUSION: Our results demonstrate the feasibility of extracting EHR treatment data with high fidelity using HL7-FHIR via REDCap CDIS for future submission to the INRGdc.

Dual Implementation Guides in FHIR and CDA.

BACKGROUND: The Fast Healthcare Interoperability Resources (FHIR) and Clinical Document Architecture (CDA) are standards for the healthcare industry, designed to improve the exchange of health data by interoperability. Both standards are constrained through what are known as Implementation Guides (IG) for specific use. OBJECTIVES: Both of these two standards are widely in use and play an important role in the Austrian healthcare system. Concepts existing in CDA and FHIR must be aligned between both standards. METHODS: Many existing approaches are presented and discussed, none are fully suited to the needs in Austria. RESULTS: The IG Publisher has already been used for CDA IGs, beside of its intended FHIR support, but never for both in one IG. Even the International Patient Summary (IPS), existing as CDA and FHIR specification, does not solve the needed comparability between these two. CONCLUSION: As the IG Publisher is widely used and supports CDA, it should be used for Dual Implementation Guides. Further work and extension of IG Publisher is necessary to enhance the readability of the resulting IGs.

Comparison of the Performance of the Fast Healthcare Interoperability Resources (FHIR) Subscription Channels.

BACKGROUND: The Fast Health Interoperability Resources (FHIR) standard was proposed and released to solve the interoperability problems of the electronic health records. The FHIR Subscription resources are used to establish real-time event notifications from the FHIR server to another system. There are several communication channels such as rest-hook and websocket. The objective of our work is to compare the performance of the FHIR subscription using the rest-hook and websocket channels. METHODS: HAPI FHIR server, python websocket clients and HTTP endpoints were used to measure the processor and memory usage of the two subscription channels. Tests were performed with 5, 10, 15, 20, 30, 40, 50, 60, 70 and 80 clients. The performance was logged using windows performance monitor. RESULTS: The rest-hook subscription showed near six-fold increase in resource utilization when increasing the clients from 5 to 80. On the contrary, the websocket subscription channel did not reach a two-fold increase. CONCLUSION: The type of the subscription channel should be carefully selected and load distribution should be considered when the number of clients grows.

Comparison of WebSocket and Hypertext Transfer Protocol for Transfer of Electronic Health Records.

BACKGROUND: Electronic health records (EHR) emerged as a digital record of the data that is generated in the healthcare. OBJECTIVES: In this paper the transfer times of EHRs using the Hypertext Transfer Protocol and WebSocket in both local network and wide area network (WAN) are compared. METHODS: A python web application to serve Fast Health Interoperability Resources (FHIR) records is created and the transfer times of the EHRs over both HTTP and WebSocket connection are measured. 45000 test Patient resources in 20, 50, 100 and 200 resources per Bundle transfers are used. RESULTS: WebSocket showed much better transfer times of large amount of data. These were 18 s shorter in the local network and 342 s shorter in WAN for the 20 resource per Bundle transfer. CONCLUSION: RESTful APIs are a convenient way to implement EHR servers; on the other hand, HTTP becomes a bottleneck when transferring large amount of data. WebSocket shows better transfer times and thus its superiority in such situations. The problem can be addressed by developing a new communication protocol or by using network tunneling to handle large data transfer of EHRs.

Building consensus on common features and interoperability use cases for community health information systems: a Delphi study.

INTRODUCTION: Information systems for community health have become increasingly sophisticated and evidence-based in the last decade and they are now the most widely used health information systems in many low-income and middle-income countries. This study aimed to establish consensus regarding key features and interoperability priorities for community health information systems (CHISs). METHODS: A Delphi study was conducted among a systematically selected panel of CHIS experts. This impressive pool of experts represented a range of leading global health institutions, with gender and regional balance as well as diversity in their areas of expertise. Through five rounds of iterative surveys and follow-up interviews, the experts established a high degree of consensus. We supplemented the Delphi study findings with a series of focus group discussions with 10 community health worker (CHW) leaders. RESULTS: CHISs today are expected to adapt to a wide range of local contextual requirements and to support and improve care delivery. While once associated with a single role type (CHWs), these systems are now expected to engage other end users, including patients, supervisors, clinicians and data managers. Of 30 WHO-classified digital health interventions for care providers, experts identified 23 (77%) as being important for CHISs. Case management and care coordination features accounted for more than one-third (14 of 37, 38%) of the core features expected of CHISs today, a higher proportion than any other category. The highest priority use cases for interoperability include CHIS to health management information system monthly reporting and CHIS to electronic medical record referrals. CONCLUSION: CHISs today are expected to be feature-rich, to support a range of user roles in community health systems, and to be highly adaptable to local contextual requirements. Future interoperability efforts, such as CHISs in general, are expected not only to move data efficiently but to strengthen community health systems in ways that measurably improve care.

recruIT: A cloud-native clinical trial recruitment support system based on Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) and the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM).

BACKGROUND: Clinical trials (CTs) are foundational to the advancement of evidence-based medicine and recruiting a sufficient number of participants is one of the crucial steps to their successful conduct. Yet, poor recruitment remains the most frequent reason for premature discontinuation or costly extension of clinical trials. METHODS: We designed and implemented a novel, open-source software system to support the recruitment process in clinical trials by generating automatic recruitment recommendations. The development is guided by modern, cloud-native design principles and based on Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) as an interoperability standard with the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) being used as a source of patient data. We evaluated the usability using the system usability scale (SUS) after deploying the application for use by study personnel. RESULTS: The implementation is based on the OMOP CDM as a repository of patient data that is continuously queried for possible trial candidates based on given clinical trial eligibility criteria. A web-based screening list can be used to display the candidates and email notifications about possible new trial participants can be sent automatically. All interactions between services use HL7 FHIR as the communication standard. The system can be installed using standard container technology and supports more sophisticated deployments on Kubernetes clusters. End-users (n = 19) rated the system with a SUS score of 79.9/100. CONCLUSION: We contribute a novel, open-source implementation to support the patient recruitment process in clinical trials that can be deployed using state-of-the art technologies. According to the SUS score, the system provides good usability.

Pediatric Consent on FHIR.

BACKGROUND: Standardizing and formalizing consent processes and forms can prevent ambiguities, convey a more precise meaning, and support machine interpretation of consent terms. OBJECTIVES: Our goal was to introduce a systematic approach to standardizing and digitizing pediatric consent forms, which are complex due to legal requirements for child and legal guardian involvement. METHODS: First, we reviewed the consent requirements from the Arizona regulation, and we used 21 pediatric treatment consents from five Arizona health care organizations to propose and evaluate an implementation-agnostic Consent for Treatment Framework. Second, we assessed the adequacy of the Fast Healthcare Interoperability Resources (FHIR) to support the proposed framework. RESULTS: The resulting Consent for Treatment Framework supports compliance with the state consent requirements and has been validated with pediatric consent forms. We also demonstrated that the FHIR standard has the required expressiveness to compute the framework's specifications and express the 21 consent forms. CONCLUSION: Health care organizations can apply the shared open-source code and FHIR implementation guidelines to standardize the design of machine-interpretable pediatric treatment consent forms. The resulting FHIR-based executable models may support compliance with the law and support interoperability and data sharing.

Human Factor Health Data Interoperability.

Comprehensive health data interoperability is recognized as an essential element of high-functioning and accountable health service. Canada is lagging in health data interoperability compared to international comparators, and lacks a comprehensive approach to human factor interoperability, defined as system-level relationships that impact the capacity of health sector stakeholders to adopt harmonized health data standards and technology. Without addressing these system-level relationships, the adoption of harmonized health data standards and technology will be obstructed and Canadians will be underserved. The proposed health data interoperability framework articulates the factors that Canada needs to address to optimize health data design to support quality health programs and services.

Integrating a Novel Eye Imaging System into Clinical Practice: An Open-Source DICOM Simulation Platform.

BACKGROUND: The integration of innovative imaging technologies into clinical workflows requires the utilization of a standardized data exchange format. The "Digital Imaging and Communications in Medicine" (DICOM) standard is widely used in healthcare to process and transfer medical images. However, no simulation environment is ready to use for development. The objective of this paper is to provide an open-source DICOM platform for integrating a novel ocular photography system into a clinical setting. METHODS: After a thorough analysis of the available open-source tools, a subset was selected and aligned with the U-Eyecare Profile of Integrating the Healthcare Enterprise (IHE). RESULTS: Orthanc served as a PACS with a modality worklist and the OHIF-Viewer for image retrieval. Minimal interfaces for the electronic health record and image modality were created. Docker-compose further integrates all components and simulates the network protocols DICOM Message Service Element and DICOMWeb. DISCUSSION: The platform was used to develop and integrate the developed image modality and will be further utilized for teleophthalmology and processing eye images. The platform could be useful for other researchers to integrate an image modality into a clinical environment along IHE.

Interoperability in a Post-Roe Era: Sustaining Progress While Protecting Reproductive Health Information.

This Viewpoint proposes a solution to better safeguard reproductive health information in patient records that are now more complete owing to the interoperability of health information exchange networks.

Mapping of nursing interventions for elderly women with vulnerability related to HIV/AIDS / Mapeo de las intervenciones de enfermería para mujeres mayores con vulnerabilidad relacionada con el VIH/SIDA / Mapeamento de intervenções de enfermagem para a mulher idosa com vulnerabilidade relacionada ao HIV/AIDS

Abstract Objective: To map the nursing interventions of the Terminology Subset for elderly women with HIV/AIDS-related vulnerabilities in the International Classification for Nursing Practice 2019/2020, according to the guidelines of the ABNT Standard ISO/TR 12.300/2016. Method: This is a descriptive exploratory study of terminological mapping, in which interventions underwent the technique of validation by consensus and human mapping. Interventions reaching 100% agreement regarding practical usefulness and classification in the Theory of Nursing Systems were validated. Finally, human mapping was performed with a single purpose and oriented from source concepts to target concepts. Results: A total of 218 interventions were validated. Following mapping, the numbers were updated due to the cardinality relationship, resulting in 221 interventions, 170 of which are not, and 51 are included in the International Classification for Nursing Practice 2019/2020. Conclusion: Mapping of the Terminological Subset of the International Classification for Nursing Practice 2019/2020 culminated in the review and update of the proposed terminology, and confirmed the usefulness of the classification system through pre-coordinated concepts.

RESUMEN Objetivo: Mapear el subconjunto terminológico de intervenciones de enfermería para mujeres ancianas con vulnerabilidades relacionadas con el VIH/SIDA según la Clasificación Internacional para la Práctica de Enfermería 2019/2020, de acuerdo con los lineamientos de la Norma ABNT ISO/TR 12.300/2016. Método: Estudio descriptivo exploratorio de mapeo terminológico, en el que las intervenciones pasaron por la técnica de validación por consenso y mapeo humano. Se validaron las intervenciones que alcanzaron un 100% de acuerdo en cuanto a utilidad práctica y clasificación en la Teoría de los Sistemas de Enfermería. Finalmente, el mapeo humano se realizó con un solo propósito y dirección desde los conceptos de origen hasta los conceptos de destino. Resultados: Un total de 218 intervenciones fueron validadas. Después del mapeo, los números fueron actualizados debido a la relación de cardinalidad, dando como resultado 221 intervenciones, de las cuales 170 no están y 51 están en la Clasificación Internacional para la Práctica de Enfermería 2019/2020. Conclusión: El mapeo del Subconjunto Terminológico con la Clasificación Internacional para la Práctica de Enfermería 2019/2020 culminó con la revisión y actualización de la terminología propuesta, además de constatar la utilidad del sistema de clasificación a través de conceptos precoordinados.

RESUMO Objetivo: Mapear as intervenções de enfermagem do Subconjunto terminológico para mulheres idosas com vulnerabilidades relacionadas ao HIV/Aids junto à Classificação Internacional para a Prática de Enfermagem 2019/2020, segundo as diretrizes da Norma ABNT ISO/TR 12.300/2016. Método: Estudo exploratório descritivo, de mapeamento terminológico, no qual as intervenções passaram pela técnica de validação por consenso e mapeamento humano. Foram validadas as intervenções que atingiram 100% de concordância em relação à utilidade prática e à classificação na Teoria dos Sistemas de Enfermagem. Por último, executou-se o mapeamento humano com propósito único e direção dos conceitos-fonte aos conceitos-alvo. Resultados: Totalizaram-se 218 intervenções validadas. Após o mapeamento, os números sofreram atualização devido à relação de cardinalidade, resultando em 221 intervenções, sendo 170 não constantes e 51 constantes na Classificação Internacional para a Prática de Enfermagem 2019/2020. Conclusão: O mapeamento do Subconjunto Terminológico junto à Classificação Internacional para a Prática de Enfermagem 2019/2020 culminou com a revisão e atualização da terminologia proposta, além de ratificar a utilidade do sistema de classificação por meio dos conceitos pré-coordenados.

Gobernanza de datos en salud: interoperabilidad y estándares

Dentro del marco de la gobernanza de datos, es necesario la adopción de arquitecturas empresariales que nos permitan tener un marco referencial para el abordaje de los distintos dominios en la gestión de los datos. La construcción de una infoestructura sólida es fundamentada en la integración de estándares y buenas prácticas que permitan a las organizaciones de salud mejora el ciclo de los datos: recolección, integración, transferencia, interoperabilidad y uso de los datos para la toma de decisiones, de una forma óptima, con calidad y seguridad.Por lo anterior, la adopción de estándares para la codificación de los datos de salud (Por ejemplo CIE 9,10 y 11), así como para la interoperabilidad de datos (FHIR/HL7 por ejemplo) y marcos de intercambio en salud en general (OpenHIE y otros más), se tornan fundamentales para que la infoestructura del sistema de información para la salud en el país funcione eficientemente con una arquitectura de salud pública robusta y acorde a los retos de la transformación digital del sector salud.