Structurally Mapping Healthcare Data to HL7 FHIR through Ontology Alignment

Personalized Diabetes Management with Digital Twins: A Patient-Centric Knowledge Graph Approach

Diabetes management requires constant monitoring and individualized adjustments. This study proposes a novel approach that leverages digital twins and personal health knowledge graphs (PHKGs) to revolutionize diabetes care. Our key contribution lies in developing a real-time, patient-centric digital twin framework built on PHKGs. This framework integrates data from diverse sources, adhering to HL7 standards and enabling seamless information access and exchange while ensuring high levels of accuracy in data representation and health insights. PHKGs offer a flexible and efficient format that supports various applications. As new knowledge about the patient becomes available, the PHKG can be easily extended to incorporate it, enhancing the precision and accuracy of the care provided. This dynamic approach fosters continuous improvement and facilitates the development of new applications. As a proof of concept, we have demonstrated the versatility of our digital twins by applying it to different use cases in diabetes management. These include predicting glucose levels, optimizing insulin dosage, providing personalized lifestyle recommendations, and visualizing health data. By enabling real-time, patient-specific care, this research paves the way for more precise and personalized healthcare interventions, potentially improving long-term diabetes management outcomes.

Electronic Health Record and Semantic Issues Using Fast Healthcare Interoperability Resources: Systematic Mapping Review

BACKGROUND

The increasing use of electronic health records and the Internet of Things has led to interoperability issues at different levels (structural and semantic). Standards are important not only for successfully exchanging data but also for appropriately interpreting them (semantic interoperability). Thus, to facilitate the semantic interoperability of data exchanged in health care, considerable resources have been deployed to improve the quality of shared clinical data by structuring and mapping them to the Fast Healthcare Interoperability Resources (FHIR) standard.

OBJECTIVE

The aims of this study are 2-fold: to inventory the studies on FHIR semantic interoperability resources and terminologies and to identify and classify the approaches and contributions proposed in these studies.

METHODS

A systematic mapping review (SMR) was conducted using 10 electronic databases as sources of information for inventory and review studies published during 2012 to 2022 on the development and improvement of semantic interoperability using the FHIR standard.

RESULTS

A total of 70 FHIR studies were selected and analyzed to identify FHIR resource types and terminologies from a semantic perspective. The proposed semantic approaches were classified into 6 categories, namely mapping (31/126, 24.6%), terminology services (18/126, 14.3%), resource description framework or web ontology language-based proposals (24/126, 19%), annotation proposals (18/126, 14.3%), machine learning (ML) and natural language processing (NLP) proposals (20/126, 15.9%), and ontology-based proposals (15/126, 11.9%). From 2012 to 2022, there has been continued research in 6 categories of approaches as well as in new and emerging annotations and ML and NLP proposals. This SMR also classifies the contributions of the selected studies into 5 categories: framework or architecture proposals, model proposals, technique proposals, comparison services, and tool proposals. The most frequent type of contribution is the proposal of a framework or architecture to enable semantic interoperability.

CONCLUSIONS

This SMR provides a classification of the different solutions proposed to address semantic interoperability using FHIR at different levels: collecting, extracting and annotating data, modeling electronic health record data from legacy systems, and applying transformation and mapping to FHIR models and terminologies. The use of ML and NLP for unstructured data is promising and has been applied to specific use case scenarios. In addition, terminology services are needed to accelerate their use and adoption; furthermore, techniques and tools to automate annotation and ontology comparison should help reduce human interaction.

A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study

BACKGROUND

Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited.

OBJECTIVE

In this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard.

METHODS

We validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database.

RESULTS

We present the FHIR-DHP workflow in respect of the transformation of "raw" hospital records into a harmonized, AI-friendly data representation. The pipeline consists of the following 5 key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonized data into the patient-model database, and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records.

CONCLUSIONS

Our approach enables the scalable and needs-driven data modeling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step toward increasing cooperation, interoperability, and quality of patient care in the clinical routine and for medical research.

"fhircrackr": An R Package Unlocking Fast Healthcare Interoperability Resources for Statistical Analysis

BACKGROUND

The growing interest in the secondary use of electronic health record (EHR) data has increased the number of new data integration and data sharing infrastructures. The present work has been developed in the context of the German Medical Informatics Initiative, where 29 university hospitals agreed to the usage of the Health Level Seven Fast Healthcare Interoperability Resources (FHIR) standard for their newly established data integration centers. This standard is optimized to describe and exchange medical data but less suitable for standard statistical analysis which mostly requires tabular data formats.

OBJECTIVES

The objective of this work is to establish a tool that makes FHIR data accessible for standard statistical analysis by providing means to retrieve and transform data from a FHIR server. The tool should be implemented in a programming environment known to most data analysts and offer functions with variable degrees of flexibility and automation catering to users with different levels of FHIR expertise.

METHODS

We propose the fhircrackr framework, which allows downloading and flattening FHIR resources for data analysis. The framework supports different download and authentication protocols and gives the user full control over the data that is extracted from the FHIR resources and transformed into tables. We implemented it using the programming language R [1] and published it under the GPL-3 open source license.

RESULTS

The framework was successfully applied to both publicly available test data and real-world data from several ongoing studies. While the processing of larger real-world data sets puts a considerable burden on computation time and memory consumption, those challenges can be attenuated with a number of suitable measures like parallelization and temporary storage mechanisms.

CONCLUSION

The fhircrackr R package provides an open source solution within an environment that is familiar to most data scientists and helps overcome the practical challenges that still hamper the usage of EHR data for research.

HL7 FHIR-based tools and initiatives to support clinical research: a scoping review

OBJECTIVES

The HL7® fast healthcare interoperability resources (FHIR®) specification has emerged as the leading interoperability standard for the exchange of healthcare data. We conducted a scoping review to identify trends and gaps in the use of FHIR for clinical research.

MATERIALS AND METHODS

We reviewed published literature, federally funded project databases, application websites, and other sources to discover FHIR-based papers, projects, and tools (collectively, "FHIR projects") available to support clinical research activities.

RESULTS

Our search identified 203 different FHIR projects applicable to clinical research. Most were associated with preparations to conduct research, such as data mapping to and from FHIR formats (n = 66, 32.5%) and managing ontologies with FHIR (n = 30, 14.8%), or post-study data activities, such as sharing data using repositories or registries (n = 24, 11.8%), general research data sharing (n = 23, 11.3%), and management of genomic data (n = 21, 10.3%). With the exception of phenotyping (n = 19, 9.4%), fewer FHIR-based projects focused on needs within the clinical research process itself.

DISCUSSION

Funding and usage of FHIR-enabled solutions for research are expanding, but most projects appear focused on establishing data pipelines and linking clinical systems such as electronic health records, patient-facing data systems, and registries, possibly due to the relative newness of FHIR and the incentives for FHIR integration in health information systems. Fewer FHIR projects were associated with research-only activities.

CONCLUSION

The FHIR standard is becoming an essential component of the clinical research enterprise. To develop FHIR's full potential for clinical research, funding and operational stakeholders should address gaps in FHIR-based research tools and methods.

Vaccine Passport - A blockchain-based architecture for a secure, tamper-resistant, privacy-enhanced credentialing mechanism of vaccination and verification of test results

Background

COVID-19 is an ongoing global pandemic caused by SARS-CoV-2. As of June 2021, 5 emergency vaccines were available for COVID-19 prevention, and with the improvement of vaccination rates and the resumption of activities in each country, verification of vaccination has become an important issue. Currently, in most areas, vaccination and reverse transcription polymerase chain reaction (RT-PCR) test results are certified and validated on paper. This leads to the problem of counterfeit documents. Therefore, a global vaccination record is needed.

Objective

The main objective of this study is to design a vaccine passport (VP) validation system based on a general blockchain architecture for international use in a simulated environment. With decentralized characteristics, the system is expected to have the advantages of low cost, high interoperability, effectiveness, security, and verifiability through blockchain architecture.

Methods

The blockchain decentralized mechanism was used to build an open and anticounterfeiting information platform for VPs. The contents of a vaccination card are recorded according to international Fast Healthcare Interoperability Resource (FHIR) standards, and blockchain smart contracts (SCs) are used for authorization and authentication to achieve hierarchical management of various international hospitals and people receiving injections. The blockchain stores an encrypted vaccination path managed by the user who manages the private key. The blockchain uses the proof-of-authority (PoA) public chain and can access all information through the specified chain. This will achieve the goal of keeping development costs low and streamlining vaccine transit management so that countries in different economies can use them.

Results

The openness of the blockchain helps to create transparency and data accuracy. This blockchain architecture contains a total of 3 entities. All approvals are published on Open Ledger. Smart certificates enable authorization and authentication, and encryption and decryption mechanisms guarantee data protection. This proof of concept demonstrates the design of blockchain architecture, which can achieve accurate global VP verification at an affordable price. In this study, an actual VP case was established and demonstrated. An open blockchain, an individually approved certification mechanism, and an international standard vaccination record were introduced.

Conclusions

Blockchain architecture can be used to build a viable international VP authentication process with the advantages of low cost, high interoperability, effectiveness, security, and verifiability.

islEHR, a model for electronic health records interoperability

Objectives

Due to the diversity, volume, and distribution of ingested data, the majority of current healthcare entities operate independently, increasing the problem of data processing and interchange. The goal of this research is to design, implement, and evaluate an electronic health record (EHR) interoperability solution – prototype – among healthcare organizations, whether these organizations do not have systems that are prepared for data sharing, or organizations that have such systems.

Methods

We established an EHR interoperability prototype model named interoperability smart lane for electronic health record (islEHR), which comprises of three modules: 1) a data fetching APIs for external sharing of patients’ information from participant hospitals; 2) a data integration service, which is the heart of the islEHR that is responsible for extracting, standardizing, and normalizing EHRs data leveraging the fast healthcare interoperability resources (FHIR) and artificial intelligence techniques; 3) a RESTful API that represents the gateway sits between clients and the data integration services.

Results

The prototype of the islEHR was evaluated on a set of unstructured discharge reports. The performance achieved a total time of execution ranging from 0.04 to 84.49 s. While the accuracy reached an F-Score ranging from 1.0 to 0.89.

Conclusions

According to the results achieved, the islEHR prototype can be implemented among different heterogeneous systems regardless of their ability to share data. The prototype was built based on international standards and machine learning techniques that are adopted worldwide. Performance and correctness results showed that islEHR outperforms existing models in its diversity as well as correctness and performance.

Development of an application concerning fast healthcare interoperability resources based on standardized structured medical information exchange version 2 data

BACKGROUND AND OBJECTIVE

A mobile application for personal health records (PHR) would allow patients to access their clinical data easily. When PHR connects with multiple electronic health records (EHRs), doctors and patients can exchange large quantities of patient data from the EHR (e.g., medication list, diagnoses, allergies, and laboratory data). Furthermore, personal daily records can also be retrieved from PHR (e.g., blood pressure, pulse, dietary habits, and exercise). However, no standard interoperability between EHRs and PHR has been established. This study aims to convert clinical data in EHRs into the Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) data format while developing a PHR application to present the FHIR data.

METHODS

In Japan, Standardized Structured Medical Information eXchange version 2 (SS-MIX2) is typically utilized as a health information exchange to preserve and elicit clinical data from EHRs. We converted clinical data in the SS-MIX2 storage at Tohoku University Hospital into the FHIR repository server using the R4 standard. Additionally, we used the Swift programming language to build a PHR application.

RESULTS

We converted patients' basic information, disease names, diagnostic reports, prescriptions, and injection data from the SS-MIX2 to the FHIR server. Besides, we launched a PHR application that could retrieve data from the FHIR server to display patients' clinical information.

CONCLUSIONS

Our work demonstrated the conversion of SS-MIX2 data into the FHIR and presented them with our PHR application. This mechanism may be useful to accelerate the sharing of clinical information among doctors and patients.

The Fast Health Interoperability Resources (FHIR) Standard: Systematic Literature Review of Implementations, Applications, Challenges and Opportunities

Background

Information technology has shifted paper-based documentation in the health care sector into a digital form, in which patient information is transferred electronically from one place to another. However, there remain challenges and issues to resolve in this domain owing to the lack of proper standards, the growth of new technologies (mobile devices, tablets, ubiquitous computing), and health care providers who are reluctant to share patient information. Therefore, a solid systematic literature review was performed to understand the use of this new technology in the health care sector. To the best of our knowledge, there is a lack of comprehensive systematic literature reviews that focus on Fast Health Interoperability Resources (FHIR)-based electronic health records (EHRs). In addition, FHIR is the latest standard, which is in an infancy stage of development. Therefore, this is a hot research topic with great potential for further research in this domain.

Objective

The main aim of this study was to explore and perform a systematic review of the literature related to FHIR, including the challenges, implementation, opportunities, and future FHIR applications.

Methods

In January 2020, we searched articles published from January 2012 to December 2019 via all major digital databases in the field of computer science and health care, including ACM, IEEE Explorer, Springer, Google Scholar, PubMed, and ScienceDirect. We identified 8181 scientific articles published in this field, 80 of which met our inclusion criteria for further consideration.

Results

The selected 80 scientific articles were reviewed systematically, and we identified open questions, challenges, implementation models, used resources, beneficiary applications, data migration approaches, and goals of FHIR.

Conclusions

The literature analysis performed in this systematic review highlights the important role of FHIR in the health care domain in the near future.

Data Sources and Gateways: Design and Open Specification

Introduction:

With With the proliferation of available ICT services, several sensors and health applications have become ubiquitous, while many applications have been developed to detect certain health conditions and early signs of disease. Currently, all these services operate independently, and the available data is heterogeneous with limited value gained from its exploitation.

Aim:

The Data Sources and Gateways component aims at providing an abstracted and unified API to support the data accumulation from various sources including healthcare organisations, biosensors, laboratories and mobile applications. Meanwhile it tackles connectivity and communication issues with such information sources.

Methods:

The CrowdHEALTH Data Sources and Gateways Service incorporates four main services: The Configuration Service, The DB Connection Handling Service, The File Parsing Service and The RESTful Client Service.

Results:

The initial version of the component design has built upon the requirements collected from the use case participants acting also as data providers.

Conclusion:

These four services presented in this paper guide the implementation of the first version of the Data Sources and Gateways component software prototype. The Data Sources and Gateways component remains to be evaluated within the context of the project and be enriched in order to meet additional end user needs.

Interoperability Techniques in CrowdHEALTH project: The Terminology Service

INTRODUCTION

Healthcare information systems' (HIS) lack of interoperability remains a challenge and a barrier for important health-related events detection. While relevant techniques are based on medical standards and technologies, these techniques do not follow a holistic approach. The creation of a set of tools that fulfils the needs of interoperability is needed.

AIM

The aim of this paper is to present the terminology service envisioned while defining the initial design of the Interoperability solution proposed for the CrowdHEALTH project.

METHODS

In the CrowdHEALTH project, specific subcomponents responsible for providing the appropriate functionalities have been designed: The rule engine for the implementation of the business logic, the Structure Mapping Service which is responsible for creating and managing the knowledge related to the link that exists between information structures, or mappings between them and the Terminology Service for providing a set of operations on medical terminologies used for the coding of medical knowledge, which fill the information structures.

RESULTS

Therefore, it is possible to provide a series of functionalities about these information elements found within more complex structures expressed in a local code and translated into other standardized medical terminology. Towards this end, CrowdHEALTH presents the terminology service envisioned in the context of the initial design of the interoperability solution.

CONCLUSION

CrowdHEALTH project provides an infrastructure to convert the clinical information into meaningful data so that healthcare systems communicate effectively. This initial proposal will be further extended and tested during the project life circle.

Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching

BACKGROUND AND OBJECTIVE

Healthcare systems deal with multiple challenges in releasing information from data silos, finding it almost impossible to be implemented, maintained and upgraded, with difficulties ranging in the technical, security and human interaction fields. Currently, the increasing availability of health data is demanding data-driven approaches, bringing the opportunities to automate healthcare related tasks, providing better disease detection, more accurate prognosis, faster clinical research advance and better fit for patient management. In order to share data with as many stakeholders as possible, interoperability is the only sustainable way for letting systems to talk with one another and getting the complete image of a patient. Thus, it becomes clear that an efficient solution in the data exchange incompatibility is of extreme importance. Consequently, interoperability can develop a communication framework between non-communicable systems, which can be achieved through transforming healthcare data into ontologies. However, the multidimensionality of healthcare domain and the way that is conceptualized, results in the creation of different ontologies with contradicting or overlapping parts. Thus, an effective solution to this problem is the development of methods for finding matches among the various components of ontologies in healthcare, in order to facilitate semantic interoperability.

METHODS

The proposed mechanism promises healthcare interoperability through the transformation of healthcare data into the corresponding HL7 FHIR structure. In more detail, it aims at building ontologies of healthcare data, which are later stored into a triplestore. Afterwards, for each constructed ontology the syntactic and semantic similarities with the various HL7 FHIR Resources ontologies are calculated, based on their Levenshtein distance and their semantic fingerprints accordingly. Henceforth, after the aggregation of these results, the matching to the HL7 FHIR Resources takes place, translating the healthcare data into a widely adopted medical standard.

RESULTS

Through the derived results it can be seen that there exist cases that an ontology has been matched to a specific HL7 FHIR Resource due to its syntactic similarity, whereas the same ontology has been matched to a different HL7 FHIR Resource due to its semantic similarity. Nevertheless, the developed mechanism performed well since its matching results had exact match with the manual ontology matching results, which are considered as a reference value of high quality and accuracy. Moreover, in order to furtherly investigate the quality of the developed mechanism, it was also evaluated through its comparison with the Alignment API, as well as the non-dominated sorting genetic algorithm (NSGA-III) which provide ontology alignment. In both cases, the results of all the different implementations were almost identical, proving the developed mechanism's high efficiency, whereas through the comparison with the NSGA-III algorithm, it was observed that the developed mechanism needs additional improvements, through a potential adoption of the NSGA-III technique.

CONCLUSIONS

The developed mechanism creates new opportunities in conquering the field of healthcare interoperability. However, according to the mechanism's evaluation results, it is almost impossible to create syntactic or semantic patterns for understanding the nature of a healthcare dataset. Hence, additional work should be performed in evaluating the developed mechanism, and updating it with respect to the results that will derive from its comparison with similar ontology matching mechanisms and data of multiple nature.