HL7 Fast Healthcare Interoperability Resources Research Articles

Leveraging Genetic Reports and Electronic Health Records for the Prediction of Primary Cancers: Algorithm Development and Validation Study.

BackgroundPrecision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions.ObjectiveThis study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries.MethodsWe extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic’s electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance.ResultsWith 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review.ConclusionsAccurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer.

Development of a FHIR RDF data transformation and validation framework and its evaluation

Resource Description Framework (RDF) is one of the three standardized data formats in the HL7 Fast Healthcare Interoperability Resources (FHIR) specification and is being used by healthcare and research organizations to join FHIR and non-FHIR data. However, RDF previously had not been integrated into popular FHIR tooling packages, hindering the adoption of FHIR RDF in the semantic web and other communities. The objective of the study is to develop and evaluate a Java based FHIR RDF data transformation toolkit to facilitate the use and validation of FHIR RDF data. We extended the popular HAPI FHIR tooling to add RDF support, thus enabling FHIR data in XML or JSON to be transformed to or from RDF. We also developed an RDF Shape Expression (ShEx)-based validation framework to verify conformance of FHIR RDF data to the ShEx schemas provided in the FHIR specification for FHIR versions R4 and R5. The effectiveness of ShEx validation was demonstrated by testing it against 2693 FHIR R4 examples and 2197 FHIR R5 examples that are included in the FHIR specification. A total of 5 types of errors including missing properties, unknown element, missing resource Type, invalid attribute value, and unknown resource name in the R5 examples were revealed, demonstrating the value of the ShEx in the quality assurance of the evolving R5 development. This FHIR RDF data transformation and validation framework, based on HAPI and ShEx, is robust and ready for community use in adopting FHIR RDF, improving FHIR data quality, and evolving the FHIR specification.

OSIRIS: A Minimum Data Set for Data Sharing and Interoperability in Oncology.

PURPOSEMany institutions throughout the world have launched precision medicine initiatives in oncology, and a large amount of clinical and genomic data is being produced. Although there have been attempts at data sharing with the community, initiatives are still limited. In this context, a French task force composed of Integrated Cancer Research Sites (SIRICs), comprehensive cancer centers from the Unicancer network (one of Europe's largest cancer research organization), and university hospitals launched an initiative to improve and accelerate retrospective and prospective clinical and genomic data sharing in oncology.MATERIALS AND METHODSFor 5 years, the OSIRIS group has worked on structuring data and identifying technical solutions for collecting and sharing them. The group used a multidisciplinary approach that included weekly scientific and technical meetings over several months to foster a national consensus on a minimal data set.RESULTSThe resulting OSIRIS set and event-based data model, which is able to capture the disease course, was built with 67 clinical and 65 omics items. The group made it compatible with the HL7 Fast Healthcare Interoperability Resources (FHIR) format to maximize interoperability. The OSIRIS set was reviewed, approved by a National Plan Strategic Committee, and freely released to the community. A proof-of-concept study was carried out to put the OSIRIS set and Common Data Model into practice using a cohort of 300 patients.CONCLUSIONUsing a national and bottom-up approach, the OSIRIS group has defined a model including a minimal set of clinical and genomic data that can be used to accelerate data sharing produced in oncology. The model relies on clear and formally defined terminologies and, as such, may also benefit the larger international community.

ACTION-EHR: Patient-Centric Blockchain-Based Electronic Health Record Data Management for Cancer Care.

BackgroundWith increased specialization of health care services and high levels of patient mobility, accessing health care services across multiple hospitals or clinics has become very common for diagnosis and treatment, particularly for patients with chronic diseases such as cancer. With informed knowledge of a patient’s history, physicians can make prompt clinical decisions for smarter, safer, and more efficient care. However, due to the privacy and high sensitivity of electronic health records (EHR), most EHR data sharing still happens through fax or mail due to the lack of systematic infrastructure support for secure, trustable health data sharing, which can also cause major delays in patient care.ObjectiveOur goal was to develop a system that will facilitate secure, trustable management, sharing, and aggregation of EHR data. Our patient-centric system allows patients to manage their own health records across multiple hospitals. The system will ensure patient privacy protection and guarantee security with respect to the requirements for health care data management, including the access control policy specified by the patient.MethodsWe propose a permissioned blockchain-based system for EHR data sharing and integration. Each hospital will provide a blockchain node integrated with its own EHR system to form the blockchain network. A web-based interface will be used for patients and doctors to initiate EHR sharing transactions. We take a hybrid data management approach, where only management metadata will be stored on the chain. Actual EHR data, on the other hand, will be encrypted and stored off-chain in Health Insurance Portability and Accountability Act–compliant cloud-based storage. The system uses public key infrastructure–based asymmetric encryption and digital signatures to secure shared EHR data.ResultsIn collaboration with Stony Brook University Hospital, we developed ACTION-EHR, a system for patient-centric, blockchain-based EHR data sharing and management for patient care, in particular radiation treatment for cancer. The prototype was built on Hyperledger Fabric, an open-source, permissioned blockchain framework. Data sharing transactions were implemented using chaincode and exposed as representational state transfer application programming interfaces used for the web portal for patients and users. The HL7 Fast Healthcare Interoperability Resources standard was adopted to represent shared EHR data, making it easy to interface with hospital EHR systems and integrate a patient’s EHR data. We tested the system in a distributed environment at Stony Brook University using deidentified patient data.ConclusionsWe studied and developed the critical technology components to enable patient-centric, blockchain-based EHR sharing to support cancer care. The prototype demonstrated the feasibility of our approach as well as some of the major challenges. The next step will be a pilot study with health care providers in both the United States and Switzerland. Our work provides an exemplar testbed to build next-generation EHR sharing infrastructures.

Developing a Mobile App for Monitoring Medical Record Changes Using Blockchain: Development and Usability Study

BackgroundAlthough we are living in an era of transparency, medical documents are often still difficult to access. Blockchain technology allows records to be both immutable and transparent.ObjectiveUsing blockchain technology, the aim of this study was to develop a medical document monitoring system that informs patients of changes to their medical documents. We then examined whether patients can effectively verify the monitoring of their primary care clinical medical records in a system based on blockchain technology.MethodsWe enrolled participants who visited two primary care clinics in Korea. Three substudies were performed: (1) a survey of the recognition of blockchain medical records changes and the digital literacy of participants; (2) an observational study on participants using the blockchain-based mobile alert app; and (3) a usability survey study. The participants’ medical documents were profiled with HL7 Fast Healthcare Interoperability Resources, hashed, and transacted to the blockchain. The app checked the changes in the documents by querying the blockchain.ResultsA total of 70 participants were enrolled in this study. Considering their recognition of changes to their medical records, participants tended to not allow these changes. Participants also generally expressed a desire for a medical record monitoring system. Concerning digital literacy, most questions were answered with “good,” indicating fair digital literacy. In the second survey, only 44 participants—those who logged into the app more than once and used the app for more than 28 days—were included in the analysis to determine whether they exhibited usage patterns. The app was accessed a mean of 5.1 (SD 2.6) times for 33.6 (SD 10.0) days. The mean System Usability Scale score was 63.21 (SD 25.06), which indicated satisfactory usability.ConclusionsPatients showed great interest in a blockchain-based system to monitor changes in their medical records. The blockchain system is useful for informing patients of changes in their records via the app without uploading the medical record itself to the network. This ensures the transparency of medical records as well as patient empowerment.

Business model canvas for adoption of International Patient Summary standards in mHealth industry

Purpose This article intends to present and discuss two promising business models for deploying international patient summary standards in mobile health (mHealth) apps, enhancing the value brought by standards, in particular by HL7 (Health Level Seven International) FHIR (Fast Healthcare Interoperability Resources) IPS (International Patient Summary). More specifically, it will explain how these standards may be understood as the strategic and financial incentives for organisations – and nations – to adopt the IPS. Approach A thorough analysis of concepts will be followed by the presentation of a business canvas for mHealth applications, that aggregates basic information on a common framework for the adoption of IPS standards. Findings To better understand this framework, two illustrative use cases (Disaster management and MOCHA - Models of Child Health Appraised/Vaccination) are presented and the benefits of IPS standard adoption in these situations are specifically highlighted as they may translate better quality care and well-being and, at the same time, represent reduction of costs on health-related expenditure. Originality This article is a summary of work developed specifically as part of the Trillium II project, primarily by collaboration by the organisations represented by the authors. Logically, it uses concepts previously developed, and referenced in the text as appropriate, however the authors maintain that the business models presented represent an original synthesis of these concepts specifically appropriate to the task of encouraging widespread adoption of the HL7 FHIR IPS.

Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support.

This paper is an extension of work originally presented to pHealth 2019—16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely operated by healthcare professionals, such as hospital information systems (HISs), or by patients deploying their personal health records (PHR). CDSSs should be able to use the semantics and the clinical context of the data imported from other systems and data repositories. A CDSS platform was developed as a set of separate microservices. In this context, we implemented the core components of a CDSS platform, namely its communication services and logical inference components. A fast healthcare interoperability resources (FHIR)-based CDSS platform addresses the ease of access to clinical decision support services by providing standard-based interfaces and workflows. This type of CDSS may be able to improve the quality of care for doctors who are using HIS without CDSS features. The HL7 FHIR interoperability standards provide a platform usable by all HISs that are FHIR enabled. The platform has been implemented and is now productive, with a rule-based engine processing around 50,000 transactions a day with more than 400 decision support models and a Bayes Engine processing around 2000 transactions a day with 128 Bayesian diagnostics models.

Voice Biometrics and Blockchain: Secure Interoperable Data Exchange for Healthcare

PURPOSE The healthcare system in the United States is unique. From payor to provider, patients have many choices but they lack in the ability to manage or share their health information. This complicated care paradigm places patients at a distinct disadvantage. Legislation clearly defines government expectations of data availability but not how to achieve exchange. Because methods of sharing are left to the discretion of care providers and software vendors, non-interoperability is the standard. METHODS The OpenPharma Blockchain on Fast Healthcare Interoperability Resources (FHIR) (OBF) solution is interoperable by design. OBF empowers patients with data access through biometric identity authentication, blockchain, and machine-–to-machine secure data access. OBF provides authenticated users read-only, real-time access to patient records using the healthcare interoperability standard HL7 FHIR. OBF is built around a modern, browser-based user interface, blockchain technologies (leveraging either Ethereum or the Hedera protocols) and modular, modern software exposed as Application Programming Interfaces (APIs). This allows OBF to meet the Office of National Coordinator for Health Information (ONC) metrics, which include sending, receiving, and finding information from outside sources and using that information to make informed clinical decisions without additional burden on clinicians or patients. RESULTS Building on the HL7 FHIR application community practices, OBF is a SMART-on-FHIR plug-in for Electronic Medical Record (EMR) systems. Using OBF, patients can identify themselves and gain access to their medical records using their voice. This unique feature is accomplished through the Saavha voice print biometrics technology. Saavha returns a unique member ID that is passed directly to the OBF blockchain smart contract for storage and interoperable patient record access (the ID does not contain public health information [PHI]). To ensure complete privacy, all information is passed through multiple layers of encryption where no keys are stored locally. Additionally, no PHI is shared to the blockchain. To ensure privacy, OBF creates a new encrypted address for the FHIR patient record object, using the Saavha generated member ID as the unique identifier. This encrypted address is then published on chain, making it available to participating providers. Providers must register their relationships to patients before OBF will permit online viewing of patient records. Patient record access is accomplished through voice verification and real-time surfacing of encrypted patient data through the OBF FHIR Viewer. CONCLUSIONS OBF is a lightweight, flexible, secure, and stable interoperable solution that places data stewardship with patients. Using industry-wide data standards, biometrics, Smart contracts, Ethereum, and OpenPharma’s data viewer for the first-time patients can authorize read-only record exchange using their voice.

Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data.

ObjectiveTo design, develop, and evaluate a scalable clinical data normalization pipeline for standardizing unstructured electronic health record (EHR) data leveraging the HL7 Fast Healthcare Interoperability Resources (FHIR) specification.MethodsWe established an FHIR-based clinical data normalization pipeline known as NLP2FHIR that mainly comprises: (1) a module for a core natural language processing (NLP) engine with an FHIR-based type system; (2) a module for integrating structured data; and (3) a module for content normalization. We evaluated the FHIR modeling capability focusing on core clinical resources such as Condition, Procedure, MedicationStatement (including Medication), and FamilyMemberHistory using Mayo Clinic’s unstructured EHR data. We constructed a gold standard reusing annotation corpora from previous NLP projects.ResultsA total of 30 mapping rules, 62 normalization rules, and 11 NLP-specific FHIR extensions were created and implemented in the NLP2FHIR pipeline. The elements that need to integrate structured data from each clinical resource were identified. The performance of unstructured data modeling achieved F scores ranging from 0.69 to 0.99 for various FHIR element representations (0.69–0.99 for Condition; 0.75–0.84 for Procedure; 0.71–0.99 for MedicationStatement; and 0.75–0.95 for FamilyMemberHistory).ConclusionWe demonstrated that the NLP2FHIR pipeline is feasible for modeling unstructured EHR data and integrating structured elements into the model. The outcomes of this work provide standards-based tools of clinical data normalization that is indispensable for enabling portable EHR-driven phenotyping and large-scale data analytics, as well as useful insights for future developments of the FHIR specifications with regard to handling unstructured clinical data.

Developing a FHIR-based EHR phenotyping framework: A case study for identification of patients with obesity and multiple comorbidities from discharge summaries.

Standards-based clinical data normalization has become a key component of effective data integration and accurate phenotyping for secondary use of electronic healthcare records (EHR) data. HL7 Fast Healthcare Interoperability Resources (FHIR) is an emerging clinical data standard for exchanging electronic healthcare data and has been used in modeling and integrating both structured and unstructured EHR data for a variety of clinical research applications. The overall objective of this study is to develop and evaluate a FHIR-based EHR phenotyping framework for identification of patients with obesity and its multiple comorbidities from semi-structured discharge summaries leveraging a FHIR-based clinical data normalization pipeline (known as NLP2FHIR). We implemented a multi-class and multi-label classification system based on the i2b2 Obesity Challenge task to evaluate the FHIR-based EHR phenotyping framework. Two core parts of the framework are: (a) the conversion of discharge summaries into corresponding FHIR resources - Composition, Condition, MedicationStatement, Procedure and FamilyMemberHistory using the NLP2FHIR pipeline, and (b) the implementation of four machine learning algorithms (logistic regression, support vector machine, decision tree, and random forest) to train classifiers to predict disease state of obesity and 15 comorbidities using features extracted from standard FHIR resources and terminology expansions. We used the macro- and micro-averaged precision (P), recall (R), and F1 score (F1) measures to evaluate the classifier performance. We validated the framework using a second obesity dataset extracted from the MIMIC-III database. Using the NLP2FHIR pipeline, 1237 clinical discharge summaries from the 2008 i2b2 obesity challenge dataset were represented as the instances of the FHIR Composition resource consisting of 5677 records with 16 unique section types. After the NLP processing and FHIR modeling, a set of 244,438 FHIR clinical resource instances were generated. As the results of the four machine learning classifiers, the random forest algorithm performed the best with F1-micro(0.9466)/F1-macro(0.7887) and F1-micro(0.9536)/F1-macro(0.6524) for intuitive classification (reflecting medical professionals' judgments) and textual classification (reflecting the judgments based on explicitly reported information of diseases), respectively. The MIMIC-III obesity dataset was successfully integrated for prediction with minimal configuration of the NLP2FHIR pipeline and machine learning models. The study demonstrated that the FHIR-based EHR phenotyping approach could effectively identify the state of obesity and multiple comorbidities using semi-structured discharge summaries. Our FHIR-based phenotyping approach is a first concrete step towards improving the data aspect of phenotyping portability across EHR systems and enhancing interpretability of the machine learning-based phenotyping algorithms.

A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard

BackgroundMobile health (MH) technologies including clinical decision support systems (CDSS) provide an efficient method for patient monitoring and treatment. A mobile CDSS is based on real-time sensor data and historical electronic health record (EHR) data. Raw sensor data have no semantics of their own; therefore, a computer system cannot interpret these data automatically. In addition, the interoperability of sensor data and EHR medical data is a challenge. EHR data collected from distributed systems have different structures, semantics, and coding mechanisms. As a result, building a transparent CDSS that can work as a portable plug-and-play component in any existing EHR ecosystem requires a careful design process. Ontology and medical standards support the construction of semantically intelligent CDSSs.MethodsThis paper proposes a comprehensive MH framework with an integrated CDSS capability. This cloud-based system monitors and manages type 1 diabetes mellitus. The efficiency of any CDSS depends mainly on the quality of its knowledge and its semantic interoperability with different data sources. To this end, this paper concentrates on constructing a semantic CDSS based on proposed FASTO ontology.ResultsThis realistic ontology is able to collect, formalize, integrate, analyze, and manipulate all types of patient data. It provides patients with complete, personalized, and medically intuitive care plans, including insulin regimens, diets, exercises, and education sub-plans. These plans are based on the complete patient profile. In addition, the proposed CDSS provides real-time patient monitoring based on vital signs collected from patients’ wireless body area networks. These monitoring include real-time insulin adjustments, mealtime carbohydrate calculations, and exercise recommendations. FASTO integrates the well-known standards of HL7 fast healthcare interoperability resources (FHIR), semantic sensor network (SSN) ontology, basic formal ontology (BFO) 2.0, and clinical practice guidelines. The current version of FASTO includes 9577 classes, 658 object properties, 164 data properties, 460 individuals, and 140 SWRL rules. FASTO is publicly available through the National Center for Biomedical Ontology BioPortal at https://bioportal.bioontology.org/ontologies/FASTO.ConclusionsThe resulting CDSS system can help physicians to monitor more patients efficiently and accurately. In addition, patients in rural areas can depend on the system to manage their diabetes and emergencies.

Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology.

The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.

Design and testing of Medivate, a mobile app to achieve medication list portability via Fast Healthcare Interoperability Resources

ObjectivesTo describe the architecture, design, and testing of an innovative mobile application (Medivate) that facilities accurate sharing of medication lists with linked education. SettingThe deployment and testing of this app occurred in both the community and hospital settings in Pittsburgh, PA. Practice innovationMedivate is an iOS smartphone application and cloud architecture for patients and providers to keep medication and vaccine lists accurate by providing a method and tool to easily exchange these data. Medications are added directly to the app from the electronic health record (EHR) or by the patient manually. Quick response (QR) code technology is used to trigger the secure transfer and sharing of medications on demand via HL-7 Fast Healthcare Interoperability Resources–based data transfer. An iterative user-centered design process involving patients and student pharmacists practicing in community settings was used to develop and refine functionality. Practice descriptionAdults with an iPhone were approached for participation in the design and evaluation of Medivate. Its functionality and integration into clinical workflow at hospital discharge or vaccine administration in the community were determined. EvaluationIn the community setting, interviews of pharmacists were conducted. In the hospital, metrics of study participation and experience with app deployment were determined. ResultsThe app was deployed in the community for patients that received vaccinations. Interviews provided insight into barriers and logistics for successful engagement. The app was integrated into hospital workflow and demonstrated interoperability with the inpatient EHR. Thirteen patients were provided the app before discharge. Engagement with the app was evident through medication list shares, education access, and changes to medication lists. Patients noted strong agreement with usefulness of the app to learn more about the purposes and adverse effects of medications prescribed. ConclusionA mobile app to achieve medication and vaccine list portability was successfully designed and integrated into the inpatient and community settings.

An Interactive Visualization Tool for HL7 FHIR Specification Browsing and Profiling.

The rich semantic representation and sophisticated structure definition of the HL7 Fast Healthcare Interoperability Resources (FHIR) specification requires relatively great efforts to understand and utilize. The objective of our study is to design, develop and evaluate an open-source and user-friendly visualization interface for exploring the FHIR specification. We prototyped an interactive visualization tool for navigating and manipulating the FHIR core resources, profiles and extensions. The utility of the tool was evaluated using evaluation metrics mainly focusing on its interactive mechanism and content expressiveness. We demonstrated that the visualization techniques are helpful for navigating the HL7 FHIR specification and aiding its profiling.

Validation and Testing of Fast Healthcare Interoperability Resources Standards Compliance: Data Analysis.

BackgroundThere is wide recognition that the lack of health data interoperability has significant impacts. Traditionally, health data standards are complex and test-driven methods played important roles in achieving interoperability. The Health Level Seven International (HL7) standard Fast Healthcare Interoperability Resources (FHIR) may be a technical solution that aligns with policy, but systems need to be validated and tested.ObjectiveOur objective is to explore the question of whether or not the regular use of validation and testing tools improves server compliance with the HL7 FHIR specification.MethodsWe used two independent validation and testing tools, Crucible and Touchstone, and analyzed the usage and result data to determine their impact on server compliance with the HL7 FHIR specification.ResultsThe use of validation and testing tools such as Crucible and Touchstone are strongly correlated with increased compliance and “practice makes perfect.” Frequent and thorough testing has clear implications for health data interoperability. Additional data analysis reveals trends over time with respect to vendors, use cases, and FHIR versions.ConclusionsValidation and testing tools can aid in the transition to an interoperable health care infrastructure. Developers that use testing and validation tools tend to produce more compliant FHIR implementations. When it comes to health data interoperability, “practice makes perfect.”

Data standards for interoperability of care team information to support care coordination of complex pediatric patients

ObjectiveSeamless access to information about the individuals and organizations involved in the care of a specific patient (“care teams”) is crucial to effective and efficient care coordination. This is especially true for vulnerable and complex patient populations such as pediatric patients with special needs. Despite wide adoption of electronic health records (EHR), current EHR systems do not adequately support the visualization and management of care teams within and across health care organizations. Electronic health information exchange has the potential to address this issue. In the present study, we assessed the adequacy of available health information exchange data standards to support the information needs related to care coordination of complex pediatric patients. MethodsWe derived data elements from the information needs of clinicians and parents to support patient care teams; and mapped them to data elements in the Health Level Seven (HL7) Consolidated Clinical Document Architecture (C-CDA) standard and in the HL7 Fast Healthcare Interoperability Resources (FHIR) standard. We also identified additional C-CDA data elements and FHIR resources that include patients’ care team members. ResultsInformation about care team members involved in patient care is generally well-represented in the C-CDA and FHIR specifications. However, there are gaps related to patients’ non-clinical events and care team actions. In addition, there is no single place to find information about care team members; rather, information about practitioners and organizations may be available in several different types of C-CDA data elements and FHIR resources. ConclusionThrough standards-based electronic health information exchange, it appears to be feasible to build patient care team representations irrespective of the location of patient care. In order to gather care team information across disparate systems, exchange of multiple C-CDA documents and/or execution of multiple FHIR queries will be necessary. This approach has the potential to enable comprehensive patient care team views that may help improve care coordination.

FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data

Secure and scalable data sharing is essential for collaborative clinical decision making. Conventional clinical data efforts are often siloed, however, which creates barriers to efficient information exchange and impedes effective treatment decision made for patients. This paper provides four contributions to the study of applying blockchain technology to clinical data sharing in the context of technical requirements defined in the “Shared Nationwide Interoperability Roadmap” from the Office of the National Coordinator for Health Information Technology (ONC). First, we analyze the ONC requirements and their implications for blockchain-based systems. Second, we present FHIRChain, which is a blockchain-based architecture designed to meet ONC requirements by encapsulating the HL7 Fast Healthcare Interoperability Resources (FHIR) standard for shared clinical data. Third, we demonstrate a FHIRChain-based decentralized app using digital health identities to authenticate participants in a case study of collaborative decision making for remote cancer care. Fourth, we highlight key lessons learned from our case study.

Connecting the clinical IT infrastructure to a service-oriented architecture of medical devices.

The new medical device communication protocol known as IEEE 11073 SDC is well-suited for the integration of (surgical) point-of-care devices, so are the established Health Level Seven (HL7) V2 and Digital Imaging and Communications in Medicine (DICOM) standards for the communication of systems in the clinical IT infrastructure (CITI). An integrated operating room (OR) and other integrated clinical environments, however, need interoperability between both domains to fully unfold their potential for improving the quality of care as well as clinical workflows. This work thus presents concepts for the propagation of clinical and administrative data to medical devices, physiologic measurements and device parameters to clinical IT systems, as well as image and multimedia content in both directions. Prototypical implementations of the derived components have proven to integrate well with systems of networked medical devices and with the CITI, effectively connecting these heterogeneous domains. Our qualitative evaluation indicates that the interoperability concepts are suitable to be integrated into clinical workflows and are expected to benefit patients and clinicians alike. The upcoming HL7 Fast Healthcare Interoperability Resources (FHIR) communication standard will likely change the domain of clinical IT significantly. A straightforward mapping to its resource model thus ensures the tenability of these concepts despite a foreseeable change in demand and requirements.

A Consensus-Based Approach for Harmonizing the OHDSI Common Data Model with HL7 FHIR.

A variety of data models have been developed to provide a standardized data interface that supports organizing clinical research data into a standard structure for building the integrated data repositories. HL7 Fast Healthcare Interoperability Resources (FHIR) is emerging as a next generation standards framework for facilitating health care and electronic health records-based data exchange. The objective of the study was to design and assess a consensus-based approach for harmonizing the OHDSI CDM with HL7 FHIR. We leverage a FHIR W5 (Who, What, When, Where, and Why) Classification System for designing the harmonization approaches and assess their utility in achieving the consensus among curators using a standard inter-rater agreement measure. Moderate agreement was achieved for the model-level harmonization (kappa = 0.50) whereas only fair agreement was achieved for the property-level harmonization (kappa = 0.21). FHIR W5 is a useful tool in designing the harmonization approaches between data models and FHIR, and facilitating the consensus achievement.

Feasibility of Representing a Danish Microbiology Model Using FHIR.

Achieving interoperability in health is a challenge and requires standardization. The newly developed HL7 standard: Fast Healthcare Interoperability Resources (FHIR) promises both flexibility and interoperability. This study investigates the feasibility of expressing a Danish microbiology message model content in FHIR to explore whether complex in-use legacy models can be migrated and what challenges this may pose. The Danish microbiology message model (the DMM) is used as a case to illustrate challenges and opportunities accosted with applying the FHIR standard. Mapping of content from DMM to FHIR was done as close as possible to the DMM to minimize migration costs except when the structure of the content did not fit into FHIR. From the DMM a total of 183 elements were mapped to FHIR. 75 (40.9%) elements were modeled as existing FHIR elements and 96 (52.5%) elements were modeled as extensions and 12 (6.6%) elements were deemed unnecessary because of build-in FHIR characteristics. In this study, it was possible to represent the content of a Danish message model using HL7 FHIR.