Taiwan’s National Health Insurance Research Database (NHIRD): in the Era of Artificial Intelligence, Causal Inference, and Data Security

BackgroundInteroperability between health information systems is a fundamental requirement to guarantee the continuity of health care for the population. The Fast Healthcare Interoperability Resource (FHIR) is the standard that enables the design and development of interoperable systems with broad adoption worldwide. However, FHIR training curriculums need an easily administered web-based self-learning platform with modules to create scenarios and questions that the learner answers. This paper proposes a system for teaching FHIR that automatically evaluates the answers, providing the learner with continuous feedback and progress.ObjectiveWe are designing and developing a learning management system for creating, applying, deploying, and automatically assessing FHIR web-based courses.MethodsThe system requirements for teaching FHIR were collected through interviews with experts involved in academic and professional FHIR activities (universities and health institutions). The interviews were semistructured, recording and documenting each meeting. In addition, we used an ad hoc instrument to register and analyze all the needs to elicit the requirements. Finally, the information obtained was triangulated with the available evidence. This analysis was carried out with Atlas-ti software. For design purposes, the requirements were divided into functional and nonfunctional. The functional requirements were (1) a test and question manager, (2) an application programming interface (API) to orchestrate components, (3) a test evaluator that automatically evaluates the responses, and (4) a client application for students. Security and usability are essential nonfunctional requirements to design functional and secure interfaces. The software development methodology was based on the traditional spiral model. The end users of the proposed system are (1) the system administrator for all technical aspects of the server, (2) the teacher designing the courses, and (3) the students interested in learning FHIR.ResultsThe main result described in this work is Huemul, a learning management system for training on FHIR, which includes the following components: (1) Huemul Admin: a web application to create users, tests, and questions and define scores; (2) Huemul API: module for communication between different software components (FHIR server, client, and engine); (3) Huemul Engine: component for answers evaluation to identify differences and validate the content; and (4) Huemul Client: the web application for users to show the test and questions. Huemul was successfully implemented with 416 students associated with the 10 active courses on the platform. In addition, the teachers have created 60 tests and 695 questions. Overall, the 416 students who completed their courses rated Huemul highly.ConclusionsHuemul is the first platform that allows the creation of courses, tests, and questions that enable the automatic evaluation and feedback of FHIR operations. Huemul has been implemented in multiple FHIR teaching scenarios for health care professionals. Professionals trained on FHIR with Huemul are leading successful national and international initiatives.

Electronic health records (EHRs) can provide researchers with extraordinary opportunities for population-based research. The National Health Insurance system of Taiwan was established in 1995 and covers more than 99.6% of the Taiwanese population; this system’s claims data are released as the National Health Insurance Research Database (NHIRD). All data from primary outpatient departments and inpatient hospital care settings after 2000 are included in this database. After a change and update in 2016, the NHIRD is maintained and regulated by the Data Science Centre of the Ministry of Health and Welfare of Taiwan. Datasets for approved research are released in three forms: sampling datasets comprising 2 million subjects, disease-specific databases, and full population datasets. These datasets are de-identified and contain basic demographic information, disease diagnoses, prescriptions, operations, and investigations. Data can be linked to government surveys or other research datasets. While only a small number of validation studies with small sample sizes have been undertaken, they have generally reported positive predictive values of over 70% for various diagnoses. Currently, patients cannot opt out of inclusion in the database, although this requirement is under review. In conclusion, the NHIRD is a large, powerful data source for biomedical research.

BackgroundCancer is a leading cause of death worldwide. Early detection through screening, diagnosis, and effective management can reduce cancer mortality. Risk assessment is crucial for improving outcomes by identifying high-risk individuals based on family history, genetics, lifestyle, and environment. Such targeted screening enhances accuracy and resource efficiency. However, the complex nature of oncology data—which includes clinical observations, lab results, radiology images, treatment regimens, and genetic information—presents significant challenges for data interoperability and exchange.ObjectiveThis study proposes an oncology data model (ODM) based on the Fast Healthcare Interoperability Resources (FHIR) standard to facilitate the capturing, sharing, and processing of oncology data across various cancer care stages. We particularly focused on screening and risk assessment for 5 cancers: breast, cervical, esophageal, lung, and oral, within the Meghalaya Fourth Industrial Revolution for Sustainable Transformation Cancer Care pilot project in India.MethodsThe ODM incorporates data elements from a cancer patient’s journey across 5 phases: encounter, risk assessment, clinical investigation, treatment, and outcome. Essential oncology data elements were modeled using the Health Level 7 FHIR Revision 4 standard. Custom FHIR profiles were developed for cancer-specific use cases, with terminology mapped to Systematized Nomenclature of Medicine–Clinical Terms, Logical Observation Identifiers Names and Codes, and the International Classification of Diseases, 10th Revision. The implementation guide (IG) was created using FHIR Shorthand, SUSHI Unshortens Short Hand Inputs, and the Health Level 7 IG Publisher. Technical and clinical validation and a stakeholder usability assessment were conducted using a demonstration tool designed for implementer training and adoption.ResultsThe data model enhances interoperability across the cancer care continuum, from screening to treatment. The resulting IG includes 25 oncology-specific resource profiles and 50 standardized terminology value sets that support both semantic and syntactic interoperability. Central to the model are the FHIR Questionnaire and QuestionnaireResponse resources, customized for structured data collection in clinical and community settings, supporting cancer screening workflows. Technical validation yielded FHIR conformance and terminology binding, while clinical validation by oncologists and public health experts confirmed the usability and relevance of 5 screening questionnaires. The demonstration tool promoted stakeholder engagement and practical evaluation of the FHIR profiles.ConclusionsThe FHIR-based ODM offers a unified framework for structured, interoperable cancer data exchange from screening to after treatment. This study marks the first comprehensive Indian initiative to apply FHIR standards for oncology screening and risk assessment. Integrating with national digital health systems, like the Ayushman Bharat Digital Mission, can ensure consistent data sharing across screening programs, hospitals, and registries. Future work will focus on real-world model deployment, evaluation in multiple districts, expanding to treatment and survivorship data, and promoting national adoption to inform cancer policy, research, and precision oncology efforts.

In radiology, technological progress has led to an enormous increase in data volumes. To effectively use these data during diagnostics or subsequent clinical evaluations, they have to be aggregated at a central location and be meaningfully retrievable in context. Radiology data warehouses undertake this task: they integrate diverse data sources, enable patient-specific and examination-specific evaluations, and thus offer numerous benefits in patient care, education, and clinical research.The international standard Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is particularly suitable for the implementation of such a data warehouse. FHIR allows for easy and fast data access, supports modern web-based frontends, and offers high interoperability due to the integration of medical ontologies such as SNOMED-CT or RadLex. Furthermore, FHIR has a robust data security concept. Because of these properties, FHIR has been selected by the Medical Informatics Initiative (MII) as the data standard for the core data set and is intended to be promoted as an international standard in the European Health Data Space (EHDS).Implementing the FHIR standard in radiology data warehouses is therefore a logical and sensible step towards data-driven medicine. · A data warehouse is essential for data-driven medicine, clinical care, and research purposes.. · Data warehouses enable efficient integration of AI results and structured report templates.. · Fast Healthcare Interoperability Resources (FHIR) is a suitable standard for a data warehouse.. · FHIR provides an interoperable data standard, supported by proven web technologies.. · FHIR improves semantic consistency and facilitates secure data exchange.. · Arnold P, Pinto dos Santos D, Bamberg F et al. FHIR - Overdue Standard for Radiology Data Warehouses. Rofo 2025; 197: 518-524.

BackgroundIn a multisite clinical research collaboration, institutions may or may not use the same common data model (CDM) to store clinical data. To overcome this challenge, we proposed to use Health Level 7’s Fast Healthcare Interoperability Resources (FHIR) as a meta-CDM—a single standard to represent clinical data.ObjectiveIn this study, we aimed to create an open-source application termed the Clinical Asset Mapping Program for FHIR (CAMP FHIR) to efficiently transform clinical data to FHIR for supporting source-agnostic CDM-to-FHIR mapping.MethodsMapping with CAMP FHIR involves (1) mapping each source variable to its corresponding FHIR element and (2) mapping each item in the source data’s value sets to the corresponding FHIR value set item for variables with strict value sets. To date, CAMP FHIR has been used to transform 108 variables from the Informatics for Integrating Biology & the Bedside (i2b2) and Patient-Centered Outcomes Research Network data models to fields across 7 FHIR resources. It is designed to allow input from any source data model and will support additional FHIR resources in the future.ResultsWe have used CAMP FHIR to transform data on approximately 23,000 patients with asthma from our institution’s i2b2 database. Data quality and integrity were validated against the origin point of the data, our enterprise clinical data warehouse.ConclusionsWe believe that CAMP FHIR can serve as an alternative to implementing new CDMs on a project-by-project basis. Moreover, the use of FHIR as a CDM could support rare data sharing opportunities, such as collaborations between academic medical centers and community hospitals. We anticipate adoption and use of CAMP FHIR to foster sharing of clinical data across institutions for downstream applications in translational research.

BackgroundQuality indicators (QIs) can help assess intensive care quality, identify potential for improvement, and ultimately enhance patient outcomes. Therefore, the German Interdisciplinary Association of Critical Care and Emergency Medicine (DIVI) has developed QIs for intensive care medicine. However, variability in how these are technically implemented across health care facilities currently limits their comparability.ObjectiveThe aim of the study is to develop unambiguous computer-interpretable representations of the DIVI QIs for intensive care medicine using Fast Healthcare Interoperability Resources (FHIR) and to establish a replicable process for translating narrative QIs into standardized digital formats.MethodsWe first decomposed the narrative DIVI intensive care medicine QIs into two sets of semantic concepts that characterize (1) the targeted patient population and (2) the care aspect specified by each indicator. We mapped the concepts to international vocabularies, defining a supplementary code system for concepts not appropriately represented in existing vocabularies. The decomposed and semantically mapped QIs were then implemented in FHIR using an implementation guide we previously developed to represent clinical practice guideline recommendations. As the translation process holds risks of inducing logical and semantic deviations, the final FHIR representations were back-translated into a narrative form and reviewed with clinical experts, including the authors of the original QIs. The decomposition and semantic mapping were iteratively adjusted based on the experts’ feedback until the results accurately reflected the original intent of the QIs.ResultsThe 10 DIVI QIs were decomposed into 31 separately measurable indicators, including 9 structural indicators, 17 process indicators, and 5 outcome indicators. All process and outcome indicators were successfully specified as computer-interpretable representations in FHIR. In total, 58 unique medical concepts were used, of which 52 (90%) could be mapped to concepts from international vocabularies. The remaining 6 concepts—mostly intensive care unit–specific scores or roles—were defined in a supplementary code system. Nested Boolean logic and temporal conditions were fully supported using standard FHIR mechanisms. After iterative adjustments, the final representations were approved as accurate representations of the DIVI QIs by the clinical expert panel.ConclusionsOur work demonstrates that the structured process developed here enables the unambiguous, computer-interpretable representation of QIs for intensive care. These representations can be used in automated quality management systems to standardize quality assessments across health care facilities. Our newly defined structured process can serve as a blueprint for similar efforts in other specialties. The here-developed computer-interpretable QIs are openly available for reuse and ongoing maintenance. Future work will focus on piloting these indicators in real-world clinical systems and extending the framework to include structural indicators.

PurposeDigital Imaging and Communications in Medicine (DICOM), a standard file format for medical imaging data, contains metadata describing each file. However, metadata are often incomplete, and there is no standardized format for recording metadata, leading to inefficiency during the metadata-based data retrieval process. Here, we propose a novel standardization method for DICOM metadata termed the Radiology Common Data Model (R-CDM).Materials and MethodsR-CDM was designed to be compatible with Health Level Seven International (HL7)/Fast Healthcare Interoperability Resources (FHIR) and linked with the Observational Medical Outcomes Partnership (OMOP)-CDM to achieve a seamless link between clinical data and medical imaging data. The terminology system was standardized using the RadLex playbook, a comprehensive lexicon of radiology. As a proof of concept, the R-CDM conversion process was conducted with 41.7 TB of data from the Ajou University Hospital. The R-CDM database visualizer was developed to visualize the main characteristics of the R-CDM database.ResultsInformation from 2801360 cases and 87203226 DICOM files was organized into two tables constituting the R-CDM. Information on imaging device and image resolution was recorded with more than 99.9% accuracy. Furthermore, OMOP-CDM and R-CDM were linked to efficiently extract specific types of images from specific patient cohorts.ConclusionR-CDM standardizes the structure and terminology for recording medical imaging data to eliminate incomplete and unstandardized information. Successful standardization was achieved by the extract, transform, and load process and image classifier. We hope that the R-CDM will contribute to deep learning research in the medical imaging field by enabling the securement of large-scale medical imaging data from multinational institutions.

This comprehensive review underscores the paramount importance of interoperability within the digital health landscape, emphasizing the necessity for a standardized framework to facilitate effective communication among healthcare professionals and institutions. The primary focus of this discourse centres on implementing a Fast Healthcare Interoperability Resources (FHIR) server, recognised as a pivotal solution addressing technical, semantic, and process interoperability failures. This standardised framework ensures uniformity and facilitates efficient communication and real-time data access within Primary Care Health Information Systems (HIS). The adaptability and scalability inherent in FHIR play a critical role in supporting the dynamic needs of healthcare systems, fostering interoperability, and enabling integration across diverse components. The narrative delves into the complexities of patient data management, accentuating the pivotal role of semantic interoperability in ensuring the seamless continuation of patient care. The transition from paper-based documentation to repository storage necessitates effective data retrieval through clinical correlation, emphasising presenting health data in a manner aligned with clinical findings—an innovative concept introduced as a health-aware presentation. Integrating FHIR standards amplifies these efforts, enriching multiple pathways for data search and retrieval. This interconnectedness not only fosters efficient interoperability within healthcare institutions but also facilitates a comprehensive approach to accessing health data across diverse organizations. The FHIR server implementation project, guided by the principles of the ADR method, systematically addresses challenges associated with patient identity criteria, biometrics, and data security, demonstrating a steadfast commitment to inclusive and patientcentric care. The detailed exploration of the development phases of the FHIR server implementation accentuates the significance of architecture design, API integration, and security measures. The concluding stages underscore a forward-looking approach, incorporating HHIMS Synthetic Dataset testing for future utilization. Ultimately, positioning the integration of an FHIR server in Primary Care HIS as a transformative step, this abstract envisions the fostering of a dynamic and responsive healthcare information environment harmonizing with the evolving landscape of digital health.

Analyzing the incidence of silicosis across various industries in Taiwan: a study of occupational disease surveillance by linking national-based workers’ and medicoadministrative databases

PurposeTo elucidate the association of presurgical sarcopenia and long-term non-opioid analgesic and opioid use after elective surgery under general anesthesia.MethodsWe conducted this population-based propensity score matched to investigate the effects...

Background: Hemorrhoids, a gastrointestinal tract disorder, are common during pregnancy. However, large-scale epidemiological studies on hemorrhoids during pregnancy are limited. Therefore, this study used analyzed data from a nationwide population-based database to investigate the prevalence, characteristics, and treatment of hemorrhoids in Taiwan. Materials and Methods: This retrospective population-based study used data from the National Health Insurance Research Database and Taiwan Birth Certificate Application to collect the medical records of women who were pregnant at any time during 2009-2018. Hemorrhoids was defined by International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) (455. X) and International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) (K64.X, O22.4X) with related treatment. Results: We enrolled 1,608,804 deliveries in 1,070,708 women. The proportion of hemorrhoids increased with age in both primipara and multipara women. Of the pregnant women, 31% received oral medication, and 93.2% used the topical ointment to treat their hemorrhoids. Few patients (1.8%) required procedure or surgery during pregnancy, and 41.4% of those patients underwent procedure or surgery in their first trimester. The cumulative incidence of hemorrhoids during pregnancy was significantly higher in women with a history of hemorrhoids and those with multifetal pregnancies. No significant difference in the incidence of hemorrhoids was observed between multiparous and primiparous women. Conclusion: Women with a history of hemorrhoids or those carrying multiple fetuses had an increased risk of hemorrhoids during pregnancy. The most commonly used treatment for hemorrhoids during pregnancy was topical ointments, with only a small proportion (1.8%) of patients requiring procedure or surgery.

BackgroundThe National Disaster Management Agency (Badan Nasional Penanggulangan Bencana) handles disaster management in Indonesia as a health cluster by collecting, storing, and reporting information on the state of survivors and their health from various sources during disasters. Data were collected on paper and transferred to Microsoft Excel spreadsheets. These activities are challenging because there are no standards for data collection. The World Health Organization (WHO) introduced a standard for health data collection during disasters for emergency medical teams (EMTs) in the form of a minimum dataset (MDS). Meanwhile, the Ministry of Health of Indonesia launched the SATUSEHAT platform to integrate all electronic medical records in Indonesia based on Fast Healthcare Interoperability Resources (FHIR).ObjectiveThis study aims to implement the WHO EMT MDS to create a disaster profile for the SATUSEHAT platform using FHIR.MethodsWe extracted variables from 2 EMT MDS medical records—the WHO and Association of Southeast Asian Nations (ASEAN) versions—and the daily reporting form. We then performed a mapping process to match these variables with the FHIR resources and analyzed the gaps between the variables and base resources. Next, we conducted profiling to see if there were any changes in the selected resources and created extensions to fill the gap using the Forge application. Subsequently, the profile was implemented using an open-source FHIR server.ResultsThe total numbers of variables extracted from the WHO EMT MDS, ASEAN EMT MDS, and daily reporting forms were 30, 32, and 46, with the percentage of variables matching FHIR resources being 100% (30/30), 97% (31/32), and 85% (39/46), respectively. From the 40 resources available in the FHIR ID core, we used 10, 14, and 9 for the WHO EMT MDS, ASEAN EMT MDS, and daily reporting form, respectively. Based on the gap analysis, we found 4 variables in the daily reporting form that were not covered by the resources. Thus, we created extensions to address this gap.ConclusionsWe successfully created a disaster profile that can be used as a disaster case for the SATUSEHAT platform. This profile may standardize health data collection during disasters.

The article discusses how to use the Russian profile of the Fast Healthcare Interoperability Resources (FHIR) RU-core protocol for medical information systems developing. An enhanced qualified electronic signature has been used for information protection for a long time; however, it is currently being implemented for the first time with the FHIR RU-core protocol to protect medical information systems. The goal of the research is enhanced qualified electronic signature integration for organizations developing secure software for medical information systems. To reach the goal, the following tasks are solved: previous works including foreign ones are analyzed and the table with different variants of FHIR protocol using is presented; the step-by-step plan of an enhanced qualified electronic signature integration has elaborated. A software code has been created to ensure the safe transmission of sensitive medical data to meet the challenge of implementing an enhanced qualified electronic signature. Russian standards were used to implement cryptographic protection of information in various medical information systems. To ensure secure data exchange, an enhanced qualified electronic signature was incorporated into the domestic version of the FHIR protocol. The use of Russian version of the protocol and certificates result in the correct exchange of medical documents. New functionality for medical information systems was standardized through the application of the Russian profile of FHIR protocol. Medical information systems deployed in the certified data processing centers are now using the FHIR RU-core protocol. The medical community easily uses FHIR RU-core, which is the most advanced tool for domestic medical systems. The method is aimed at integrating health information systems safely to develop regional services for doctors, patients, and digital health care organizers. The scientific novelty and relevance of the research lies in the field of adaptation international experience of using FHIR protocol under Russian circumstances and refinement of an enhanced qualified electronic signature integration method without capacity loss. The practical result demonstrates that the use of the Russian enhanced qualified electronic signature satisfies the information security requirements of new medical information systems and allows sensitive data to be transmitted without loss of quality and speed. It has been concluded that a systematic approach to using the Russian profile of the FHIR RU-core protocol for new medical information systems, with the aim of implementing digital healthcare, is highly recommended. This article is a valuable resource for medical information systems software architects and developers, as well as information security specialists.

The world’s aging population increasingly faces challenges in accessing healthcare due to a shortage of healthcare professionals. Telemedicine and remote patient monitoring solutions offer a promising avenue for improving access to care, allowing for the monitoring of physiological data, activities performed, and the conditions of the patient’s environment. However, such systems must address numerous challenges, such as interoperability, security, integrity, and confidentiality of medical data. In this paper, we propose a repository architecture for medical data obtained through remote patient monitoring. Our solution relies on the Fast Healthcare Interoperability Resources (FHIR) standard to address interoperability issues, while the inherent characteristics of blockchain technology provide security, integrity, and confidentiality of stored data. In addition to remote patient monitoring, the proposed repository has the potential to be used for scientific research, data mining and analysis applications among other health applications. Ongoing implementation and testing of the repository in a real-world setting will demonstrate its performance and scalability. Meanwhile, we present the architecture and constituent elements, including data flow and smart contracts, with their responsibilities described. Overall, our proposed solution offers a promising approach to addressing the challenges of remote patient monitoring and storing medical data securely and efficiently.

Data models are crucial for clinical research as they enable researchers to fully use the vast amount of clinical data stored in medical systems. Standardized data and well-defined relationships between data points are necessary to guarantee semantic interoperability. Using the Fast Healthcare Interoperability Resources (FHIR) standard for clinical data representation would be a practical methodology to enhance and accelerate interoperability and data availability for research. This research aims to provide a comprehensive overview of the state-of-the-art and current landscape in FHIR-based data models and structures. In addition, we intend to identify and discuss the tools, resources, limitations, and other critical aspects mentioned in the selected research papers. To ensure the extraction of reliable results, we followed the instructions of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We analyzed the indexed articles in PubMed, Scopus, Web of Science, IEEE Xplore, the ACM Digital Library, and Google Scholar. After identifying, extracting, and assessing the quality and relevance of the articles, we synthesized the extracted data to identify common patterns, themes, and variations in the use of FHIR-based data models and structures across different studies. On the basis of the reviewed articles, we could identify 2 main themes: dynamic (pipeline-based) and static data models. The articles were also categorized into health care use cases, including chronic diseases, COVID-19 and infectious diseases, cancer research, acute or intensive care, random and general medical notes, and other conditions. Furthermore, we summarized the important or common tools and approaches of the selected papers. These items included FHIR-based tools and frameworks, machine learning approaches, and data storage and security. The most common resource was "Observation" followed by "Condition" and "Patient." The limitations and challenges of developing data models were categorized based on the issues of data integration, interoperability, standardization, performance, and scalability or generalizability. FHIR serves as a highly promising interoperability standard for developing real-world health care apps. The implementation of FHIR modeling for electronic health record data facilitates the integration, transmission, and analysis of data while also advancing translational research and phenotyping. Generally, FHIR-based exports of local data repositories improve data interoperability for systems and data warehouses across different settings. However, ongoing efforts to address existing limitations and challenges are essential for the successful implementation and integration of FHIR data models.