Management of cirrhosis suffers from poor guideline adherence due to fragmented electronic health record (EHR) systems that scatter critical patient data across multiple modules, creating cognitive burden for clinicians and impeding evidence-based care delivery. We developed SMARTLiver, a Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources (SMART-on-FHIR) clinical decision support application employing human-centered design principles to consolidate patient data, incorporate evidence-based guidelines, and enhance cirrhosis care workflows. Following literature reviews of cirrhosis management guidelines and clinical workflow analysis within our health system, we created a FHIR-based application integrating automated task management, prognostic scoring, patient-reported outcomes, and real-time clinical decision support features. Usability evaluation with five clinical staff members using Think-Aloud protocols and the validated Health-ITUES survey revealed high satisfaction scores for Clinical Utility (4.4-4.6/5.0) and User Interface design (4.2/5.0), with moderate scores for workflow integration (4.0/5.0) and decision support (3.8-4.0/5.0). Qualitative feedback aligned with quantitative results, identifying enhancement opportunities in customization controls and notification management. The SMARTLiver prototype demonstrated technical feasibility in aggregating fragmented clinical data into a unified interface, automating evidence-based task generation, and maintaining interoperability across healthcare systems. This pilot study provides initial evidence for the potential of SMART-on-FHIR technology to address EHR fragmentation in cirrhosis care, though clinical effectiveness remains to be demonstrated.

Management of cirrhosis suffers from poor guideline adherence due to fragmented electronic health record (EHR) systems that scatter critical patient data across multiple modules, creating cognitive burden for clinicians and impeding evidence-based care delivery. We developed SMARTLiver, a Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources (SMART-on-FHIR) clinical decision support application employing human-centered design principles to consolidate patient data, incorporate evidence-based guidelines, and enhance cirrhosis care workflows. Following literature reviews of cirrhosis management guidelines and clinical workflow analysis within our health system, we created a FHIR-based application integrating automated task management, prognostic scoring, patient-reported outcomes, and real-time clinical decision support features. Usability evaluation with five clinical staff members using Think-Aloud protocols and the validated Health-ITUES survey revealed high satisfaction scores for Clinical Utility (4.4-4.6/5.0) and User Interface design (4.2/5.0), with moderate scores for workflow integration (4.0/5.0) and decision support (3.8-4.0/5.0). Qualitative feedback aligned with quantitative results, identifying enhancement opportunities in customization controls and notification management. The SMARTLiver prototype demonstrated technical feasibility in aggregating fragmented clinical data into a unified interface, automating evidence-based task generation, and maintaining interoperability across healthcare systems. This pilot study provides initial evidence for the potential of SMART-on-FHIR technology to address EHR fragmentation in cirrhosis care, though clinical effectiveness remains to be demonstrated.

Background:Institutions that participate in PCORnet® transform their local electronic health record (EHR) data into the PCORnet® Common Data Model (CDM), which is then used to generate data extracts for PCORnet® Studies. PCORnet® Studies can also include institutions that do not participate in PCORnet, and for these organizations, the cost of instantiating a PCORnet® CDM can be prohibitive. Fast Health care Interoperability Resources (FHIR) provides an alternative method of obtaining EHR data.Objective:To determine whether data obtained through FHIR might be a viable study solution for those sites that do not participate in PCORnet.® This mixed-methods project had 2 objectives: (1) survey sites participating in PCORnet on the availability of FHIR (FHIR survey); (2) compare the coverage of a FHIR-based data extract using REDCap with one from the PCORnet® CDM across 3 sites (FHIR extract).Methods:(1) FHIR survey: A series of questions were asked about the use of FHIR in a production capacity. (2) FHIR extract: REDCap FHIR and PCORnet® CDM extracts were created based on study variables from 2 prior PCORnet® Studies. Data were extracted for 40 patients and concordance measures were computed between the 2 sources.Results:(1) FHIR survey: Of responding organizations, 73% (n=49) reported that FHIR was deployed in a production capacity. (2) FHIR extract: Results were highly variable. Cohen kappa ranged from 0.01 to 0.76 for certain diagnoses, 0.24 to 0.84 for laboratory results, and 0.1 to 0.87 for medications.Conclusions:Despite differences in data, certain studies may be well-suited for FHIR-based extracts.

The International Patient Summary (IPS) is a minimal data standard enabling rapid access to essential health information across institutions and borders. Korea provides Fast Healthcare Interoperability Resources (FHIR) data through the “My Health Record” application, which utilizes an implementation guide (IG) inheriting from the KR Core FHIR profiles. However, a standardized workflow for transforming these domestic FHIR resources into IPS-compliant data has not yet been established. This study aimed to assess the feasibility of implementing an IPS-compliant patient summary in Korea using existing FHIR-based resources and national profiles. First, a literature review confirmed IPS as a global standard supporting interoperability and patient-centered care. Second, a gap analysis revealed that six of the seven IPS-required and recommended components successfully mapped to ten KR Core profiles. However, the Device component and the MedicationStatement profile remained unmapped due to the lack of corresponding definitions in the KR Core. Third, real-world FHIR data from three individuals were transformed using ChatGPT-4o into IPS-compatible formats and validated via HAPI FHIR and SMART FRED tools. Fourth, user requirements were identified through personas and expert consultations, highlighting the need for summary and timeline-based UI elements. Fifth, a user interface was developed using Figma based on these requirements.Overall, approximately 86% of required IPS data elements were represented using existing Korean FHIR-based resources. These findings demonstrate the technical feasibility of IPS implementation in Korea, while also highlighting current gaps in terminology coverage and profile alignment. Future work should focus on multi-site validation, increased automation of mapping processes, and governance frameworks to support scalable and reproducible IPS deployment.

Using FHIR for data sharing: A scoping review of challenges and facilitators in healthcare settings.

Skilled Nursing Facilities (SNF) hospital readmissions continue to be a significant issue in terms of healthcare quality, patient safety and cost management in the Centres for Medicare and Medicaid Services (CMS) Hospital Readmissions Reduction Program (HRRP). A large number of SNFs do not have sophisticated analytical software to integrate clinical and social data to determine high-risk residents of early readmission. By training and testing a machine learning model that is interpretable and based on interoperable Fast Healthcare Interoperability Resources (FHIR) data, this study will fulfill this gap and predict 30-day hospital readmissions among SNF residents. The analysis was based on de-identified, FHIR-mapped data of 14,250 SNF residents, namely medications, vital sign, functional status, prior utilisation and social risk indicators. The gradient-boosted machine (GBM) model was constructed and compared to a basis of logistic regression. The performance of the models was assessed in terms of the AUROC, AUPRC, calibration analysis, and the decision curve analysis. The explainability was done by SHapley Additive exPlanations (SHAP) which allowed transparent understanding of the individual risk factors. SHAP analysis gave easily understandable, clinically significant explanations, which justified actionable care planning. The unmanned pilot ensured stable performance over a period of time with slight drift. On the whole, this paper proves that interoperable FHIR data combined with explainable machine learning can help to make SNFs predict readmission risks ethically, transparently, and effectively. The strategy complies with policy, privacy and quality improvement objectives, and provides value to work conveniently to clinicians, administrators and policymakers aiming to minimize preventable hospital readmissions.

The increasing volume of digital medical data offers substantial research opportunities, though its complete utilization is hindered by ongoing privacy and security obstacles. This proof-of-concept study explores and confirms the viability of using Secure Multi-Party Computation (SMPC) to ensure protection and integrity of sensitive patient data, allowing the construction of clinical trial cohorts. Our findings reveal that SMPC facilitates collaborative data analysis on distributed, private datasets with negligible computational costs and optimized data partition sizes. The established architecture incorporates patient information via a blockchain-based decentralized healthcare platform and employs the MPyC library in Python for secure computations on Fast Healthcare Interoperability Resources (FHIR)-format data. The outcomes affirm SMPC’s capacity to maintain patient privacy during cohort formation, with minimal overhead. It illustrates the potential of SMPC-based methodologies to expand access to medical research data. A key contribution of this work is eliminating the need for complex cryptographic key management while maintaining patient privacy, illustrating the potential of SMPC-based methodologies to expand access to medical research data by reducing implementation barriers.

The integration of Fast Healthcare Interoperability Resources (FHIR) with generative artificial intelligence (GenAI) systems is accelerating across the healthcare landscape, particularly within pharmacy ecosystems responsible for medication dispensing, formulary management, prior authorization, and real-time benefit determination. While FHIR provides a standardized framework for interoperable data exchange, GenAI introduces new risks such as prompt injection, hallucinated clinical assertions, privacy violations, model inversion, and inadvertent leakage of protected health information (PHI). These risks become more pronounced when GenAI models are connected to high-sensitivity medication APIs used by payers, pharmacies, and clinical systems. This paper presents a comprehensive security architecture for FHIR-GenAI integration within pharmacy workflows. It proposes a multilayer strategy incorporating Zero Trust, privacy-preserving computation, API governance, model guardrails, and secure machine-learning pipelines. Code examples are provided to illustrate key enforcement mechanisms. The analysis demonstrates that secure implementation of GenAI in pharmacy systems must harmonize conventional enterprise security controls with new GenAI-specific risks while honoring regulatory requirements such as HIPAA, HITECH, and HITRUST CSF.

Drug–drug interactions (DDIs) represent a persistent and clinically significant challenge, contributing to preventable adverse events and suboptimal therapeutic outcomes, especially among patients exposed to polypharmacy. While traditional Clinical Decision Support Systems (CDSS) provide rule-based DDI alerts, these mechanisms often suffer from low specificity, high override rates, and limited adaptability to patient context. In recent years, substantial progress in artificial intelligence (AI) has enabled more sophisticated prediction of DDIs based on molecular representations, deep learning architectures, and transformer driven extraction of textual evidence. However, most published AI models remain detached from operational CDSS workflows and lack standardized mechanisms for integration into electronic health record (EHR) environments. This article proposes an interoperable, multiagent CDSS architecture that integrates the author’s previously validated AI-based DDI prediction models into a clinically deployable workflow. The system incorporates a Graph– Vector Hybrid GCN–FFNN model for synergistic and antagonistic DDI prediction and a BiomedBERT–LoRA classifier for extracting polarity and mechanistic cues from biomedical literature. These previously validated models are the main parts of a network of autonomous agents that work together to do things like data preprocessing, inference routing, contextual adjustment, alert generation, explanation synthesis, and governance. Health Level 7 – Fast Healthcare Interoperability Resources (HL7 FHIR) is the interoperability backbone for the architecture. This means that it works with EHR systems in real time and uses DetectedIssue resources to show alerts in a standard way. This research shows that AI is beneficial for medication safety not due to its capacity for independent accurate predictions, but because it can be incorporated into a system that is transparent, scalable, clinically pertinent, and sustainable over time.

Fast Healthcare Interoperability Resources (FHIR), developed by Health Level Seven International (HL7), has emerged as the leading healthcare data standard to address persistent barriers in interoperability, fragmented exchange, and inconsistent data harmonization. As health systems worldwide undergo digital transformation, FHIR offers a flexible framework for integrating electronic health records, analytics platforms, and decision-support tools. Its growth has been accelerated by policy mandates such as the 21st Century Cures Act, as well as the availability of application programming interfaces (APIs), software development kits (SDKs), and web standards. Globally, FHIR has been adopted or piloted by national health systems in the United States, United Kingdom, Canada, and Australia, and incorporated into World Health Organization data initiatives, underscoring its role in global digital health strategy. Documented outcomes of this review include comprehensive mapping of FHIR applications across clinical, research, and public health domains; identification of adoption barriers and enablers; insights into integration with generative AI and large language models for predictive modeling, automated documentation, and decision support; and guidance for future innovations such as blockchain-enabled infrastructure and cloud-native scalability. Nonetheless, challenges remain, including uneven implementation, workforce training gaps, scalability limitations, and unresolved concerns around privacy, security, and regulatory compliance. This synthesis provides actionable insights for providers, researchers, policymakers, and developers to advance global health interoperability.

Cross-Platform Health Data Harmonization: A Modular Framework for Scalable Mobile Wellness Ecosystems

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.

The widespread fragmentation of patient information across heterogeneous systems and the lack of standardized integration mechanisms hinder efficient and comprehensive medical diagnostics. To address these limitations, this work presents an architecture framework designed to support physicians in the diagnostic process by integrating clinical and socio-health information (patient medical histories), structured documents extracted from Health Information System (HIS), and data automatically extracted from diagnostic images using Artificial Intelligence (AI) techniques. The proposed architecture is made by several modules, in particular a Decision Support System (DSS) that enables risk assessment related to specific patient’s clinical conditions. In addition, the clinical information retrieved is aggregated, standardized, and transmitted to external systems for follow up. Standardization and data interoperability are ensured through the adoption of the international HL7 Fast Healthcare Interoperability Resources (FHIR) standard, which facilitates seamless connection with HIS. An Android application has been developed to communicate with different HISs in order to: (i) retrieve information, (ii) aggregate clinical data, (iii) calculate patient risk scores using AI algorithms, (iv) display results to healthcare professionals, and (v) generate and share relevant clinical information with external systems in a standardized format. To demonstrate architecture’s applicability, a case study on breast cancer diagnosis is presented. In this context, an AI-based Risk Assessment module was developed using the Breast Ultrasound Images Dataset (BUSI), which includes benign, malignant, and normal cases. Machine Learning algorithms were applied to perform the classification task. Model performance was evaluated using a 4-fold cross-validation strategy to ensure robustness and generalizability. The best results were achieved using the Multilayer Perceptron method, with a competitive F1-score of 0.97.

A real-time dashboard for optimizing blood product utilization through a FHIR-based data integration pipeline.

Decoding the plasma proteome: Advancing precision medicine in cardiovascular health.

India’s healthcare landscape is undergoing a significant transformation, yet the management of chronic diseases like Thalassemia remains encumbered by fragmented, insecure, and non-standardized patient data. This fragmentation hinders effective care coordination and prevents seamless integration with the nation’s ambitious digital health vision. This paper presents a novel research framework for a modern, interoperable Thalassemia data ecosystem designed to integrate with India’s Ayushman Bharat Digital Mission (ABDM). The framework leverages Health Level 7 (HL7) Fast Healthcare Interoperabil- ity Resources (FHIR) for data standardization, ensures robust privacy through compliance with the Digital Personal Data Protection (DPDP) Act, 2023, and proposes a multi-stakeholder architecture connecting patients, clinicians, blood banks, and donors. The primary contribution of this research lies in its disease-specific focus for ABDM integration, its privacy-centric design, and its patient-empowerment approach through a mobile- first strategy. By establishing a standardized and secure data exchange, this framework has the potential to significantly improve data accessibility, enhance patient safety, and enable superior care coordination for Thalassemia management across India, serving as a model for other chronic diseases.

Global healthcare delivery systems are faced with considerable challenges due to technological fragmentation, wherein electronic health records, laboratory information systems, radiology platforms, and billing infrastructures exist as autonomous silos, not facilitating the smooth flow of information. The lack of connection creates huge delays in clinical decision-making, increases medical error risks during patient handoffs, and places tremendous administrative burdens on healthcare providers who are required to manually reconcile information on different disconnected platforms. Standards-based interoperability frameworks built on top of Fast Healthcare Interoperability Resources and Health Level Seven protocols have become key enablers of coordinated care delivery. Building on these interoperability foundations, workflow engines add advanced orchestration capabilities, automating intricate clinical and administrative workflows across discharge planning, medication reconciliation, and referral management. The paper analyzes architectural paradigms supporting interoperability-driven workflow engines, such as modular design principles facilitating vendor-agnostic communication and event-driven orchestration reacting dynamically to clinical cues. Infrastructure needs such as containerized microservices architecture and robust messaging mechanisms providing scalable deployment are thoroughly elaborated. Regulatory compliance frameworks controlling patient data privacy, in addition to ethical considerations tackling alert fatigue, algorithmic bias, and maintenance of clinical judgment, are explored in depth. The synthesis illustrates how workflow engines address persistent coordination failures in helping care continuity along complicated healthcare shipping chains with numerous specialties, corporations, and care settings.

While Research Electronic Data Capture (REDCap) is widely adopted in rare disease research, its unconstrained data format often lacks native interoperability with global health standards, limiting secondary use. We developed RareLink, an open-source framework implementing our published ontology-based rare disease common data model. It enables standardised data exchange between REDCap, international registries, and downstream analysis tools by linking Global Alliance for Genomics and Health Phenopackets and Health Level 7 Fast Healthcare Interoperability Resources (FHIR) instances conforming to International Patient Summary and Genomics Reporting profiles. RareLink was developed in three phases across Germany, Canada, South Africa, and Japan for registry and data analysis purposes. We defined a simulated Kabuki syndrome cohort and demonstrated data export to Phenopackets and FHIR. RareLink can enhance the clinical utility of REDCap through its global applicability, supporting equitable rare disease research. Broader adoption and coordination with international entities are thus essential to realise its full potential.

Since 2018, The University of Queensland's (UQ) Faculty of Engineering, Architecture and Information Technology and CSIRO's Australian e-Health Research Centre (AEHRC) have collaborated on UQ's COMP3820 Digital Health Software Project course [1]. Students study an edX-based MOOC and attend lectures by domain experts to learn about healthcare, healthcare informatics and healthcare standards with a focus on SNOMED CT, HL7's Fast Healthcare Interoperability Resources (FHIR®) and the SMART on FHIR (SMART®) Application Programming Interface (API) standards. They form teams that develop SMART on FHIR based electronic health record (EHR) connected apps under the mentorship of domain experts who propose problems suitable for a FHIR app solution. In the 2024 autumn/winter semester, eighty students formed sixteen project teams. The course and the three highest rated projects are described.

The integration of artificial intelligence (AI) into clinical workflows is advancing even before full compliance with the European Union Cross-Border eHealth Network (MyHealth@EU) framework is achieved. While AI-based clinical decision support systems are automatically classified as high risk under the European Union’s AI Act, cross-border health data exchange must also satisfy MyHealth@EU interoperability requirements. This creates a dual-compliance challenge: vertical safety and ethics controls mandated by the AI Act and horizontal semantic transport requirements enforced through Open National Contact Point (OpenNCP) gateways, many of which are still maturing toward production readiness. This paper provides a practical, phase-oriented tutorial that enables developers and providers to embed AI Act safeguards before approaching MyHealth@EU interoperability tests. The goal is to show how AI-specific metadata can be included in the Health Level Seven International Clinical Document Architecture and Fast Healthcare Interoperability Resources messages without disrupting standard structures, ensuring both compliance and trustworthiness in AI-assisted clinical decisions. We systematically analyzed Regulation (EU) 2024/1689 (AI Act) and the OpenNCP technical specifications, extracting a harmonized set of overlapping obligations. The AI Act provisions on transparency, provenance, and robustness are mapped directly onto MyHealth@EU workflows, identifying the points where outgoing messages must record AI involvement, log provenance, and trigger validation. To operationalize this mapping, we propose a minimal extension set, covering AI contribution status, rationale, risk classification, and Annex IV documentation links, together with a phase-based compliance checklist that aligns AI Act controls with MyHealth@EU conformance steps. A simulated International Patient Summary transmission demonstrates how Clinical Document Architecture/Fast Healthcare Interoperability Resources extensions can annotate AI involvement, how OpenNCP processes such enriched payloads, and how clinicians in another member state view the result with backward compatibility preserved. We expand on security considerations (eg, Open Worldwide Application Security Project generative AI risks such as prompt injection and adversarial inputs), continuous postmarket risk assessment, monitoring, and alignment with MyHealth@EU’s incident aggregation system. Limitations reflect the immaturity of current infrastructures and regulations, with real-world validation pending the rollout of key dependencies. AI-enabled clinical software succeeds only when AI Act safeguards and MyHealth@EU interoperability rules are engineered together from day 0. This tutorial provides developers with a forward-looking blueprint that reduces duplication of effort, streamlines conformance testing, and embeds compliance early. While the concept is still in its early phases of practice, it represents a necessary and worthwhile direction for ensuring that future AI-enabled clinical systems can meet both European Union regulatory requirements from day 1.risks such as prompt injection and adversarial inputs), continuous postmarket risk assessment, monitoring, and alignment with MyHealth@EU’s incident aggregation system. Limitations reflect the immaturity of current infrastructures and regulations, with real-world validation pending the rollout of key dependencies.