HeXEHRS: FHIR-Based Cloud EHR Services to Support Healthcare in Depopulated Areas Driven by Digital Twin.

HeXEHRS is a FHIR-based cloud EHR service designed to support healthcare in depopulated areas, powered by digital twin technology. Its core functionalities encompass standard EHR tasks including data exchange for healthcare processes. In the first year of this national project, we present the design and define the functionalities of the system.

Federated Electronic Health Records Using a Blockchain.

The Valkyrie project aims to develop a demonstration Federated Electronic Health Record for the use of mental health practitioners in Norway. Information for the record is drawn from existing records in Source Systems operating across primary and secondary care. Recording of information in any such system, in response to a healthcare event, triggers the generation of an Encrypted Token, containing summary metadata about the event, clinical coding indicating its clinical context and a locator that can be used to retrieve the full record of the event from the original Source System. The Valkyrie architecture consists of a number of interlinked Security Domains, each with its own private and public keys, through which the Encrypted Tokens are passed. Each Security Domain performs a specific function on a set of Tokens and only has access to the information within each Token that is necessary to perform that function. This paper describes the structure of the Encrypted Token, the function of each Security Domain and the orchestration of the flow of Tokens through the Domains. Together this allows a user to run a Valkyrie Session, in which they can view the content of a patient record, where all content has been drawn in real-time from heterogenous Source Systems (ISO13606- and openEHR-based) and is destroyed when the session terminates.

Real-World Data in Greece: Mapping the Papageorgiou General Hospital Data to the OMOP Common Data Model.

This paper showcases the results of the Extract-Transform-Load process mapping the Electronic Health Record of Papageorgiou General Hospital in Thessaloniki, Greece, to the Observational Medical Outcomes Partnership Common Data Model. We describe the staged process utilized to account for the intricate structure of the database, along with some general findings from the mapping. Finally, we investigate potential directions for future research.

Developing a Sustainable Shared Child's Health Record in Low Resources Setting, Lao PDR.

Lao PDR's Ministry of Health has problem with fragmented data, lack of standardization, and inability to interoperate hinder child health data collection. Electronic health information exchange (HIE) could improve data quality and shared child health records (SCHR). In this study we developed the SCHR by interoperating with existing healthcare systems. The FHIR IPS standards were customized based on data elements. 14 experts from the technical working group (TWG) had determined the minimum dataset for data transactions and system testing was analyzed on the HAPI FHIR server. Used FHIR IPS standards for interoperability within Client Registry (CR-DHIS2), Electronic Immunization Record (EIR-DHIS2) and Electronic Medical Record (EMR) works well for data transactions. The initial result of the SCHR could be able to provide a real target child and to monitor full vaccination. However, prior to the actual deployment of the system, it is necessary to validate it and guarantee the confidentiality and safety of the data.

Challenges and Lessons Learned in Mapping HL7 v2 Data to openEHR: Insights from UKSH Medical Data Integration Center.

This paper explores the challenges and lessons learned during the mapping of HL7 v2 messages structured using custom schema to openEHR for the Medical Data Integration Center (MeDIC) of the University Hospital, Schleswig-Holstein (UKSH). Missing timestamps in observations, missing units of measurement, inconsistencies in decimal separators and unexpected datatypes were identified as critical inconsistencies in this process. These anomalies highlight the difficulty of automating the transformation of HL7 v2 data to any standard, particularly openEHR, using off-the-shelf tools. Addressing these anomalies is crucial for enhancing data interoperability, supporting evidence-based research, and optimizing clinical decision-making. Implementing proper data quality measures and governance will unlock the potential of integrated clinical data, empowering clinicians and researchers and fostering a robust healthcare ecosystem.

Integration of Oncological Data into openEHR: A Path Towards Improved Cancer Care and Research.

The integration of tumor-related diagnosis and therapy data is a key factor for cancer-related collaborative projects and research projects on-site. The Medical Data Integration Center (MeDIC) of the University Hospital Schleswig-Holstein, resulting from the Medical Informatics Initiative and Network University Medicine in Germany, has agreed on an openEHR-based data management based on a centralized repository with harmonized annotated data. Consequently, the oncological data should be integrated into the MeDIC to interconnect the information and thus gain added value. A uniform national data set for tumor-related reports is already defined for the cancer registries. Therefore, this work aims to transform the national oncological basis data set for tumor documentation (oBDS) so that it can be stored and utilized properly in the openEHR repository of the MeDIC. In a previous work openEHR templates representing the oncological basis data set were modeled. These templates were used to implement a processing pipeline including a metadata repository, which defines the mappings between the elements, a FHIR terminology service for annotation and validation, resulting in a tool to automatically build openEHR compositions from oBDS data. The prototype proved the feasibility of the referred mapping, integration into the MeDIC is straightforward and the architecture introduced is adaptable to future needs by design.

Mapping OMOP-CDM to RDF: Bringing Real-World-Data to the Semantic Web Realm.

Real-world data (RWD) (i.e., data from Electronic Healthcare Records - EHRs, ePrescription systems, patient registries, etc.) gain increasing attention as they could support observational studies on a large scale. OHDSI is one of the most prominent initiatives regarding the harmonization of RWD and the development of relevant tools via the use of a common data model, OMOP-CDM. OMOP-CDM is a crucial step towards syntactic and semantic data interoperability. Still, OMOP-CDM is based on a typical relational database format, and thus, the vision of a fully connected semantically enriched model is not fully realized. This work presents an open-source effort to map the OMOP-CDM model and the data it hosts, to an ontological model using RDF to support the FAIRness of RWD and their interlinking with Linked Open Data (LOD) towards the vision of the Semantic Web.

Optimizing Integration with the Italian EHR: The Use of LOINC Codes.

According to the regulation "Decreto del Presidente del Consiglio dei Ministri" (DPCM) of September 29, 2015, n.178, the Logical Observation Identifiers Names and Codes (LOINC) system is included among the coding systems adopted in the Italian Electronic Health Record (EHR). As part of the Digital Health Solutions in Community Medicine (DHEAL-COM) project, one key goal is to categorize parameters using international classification systems. This enables the identification of appropriate Information and Communication Technology (ICT) solutions tailored to support people's health needs. Our objective is to incorporate LOINC codes for parameter categorization, thus anticipating the future use of EHR.

Privacy-Preserving Linkage of Distributed Pseudonymised Datasets in a Virtual European Rare Disease Platform.

Secondary use of data for research purposes is especially important in rare diseases (RD), since, per definition, data are sparse. The European Joint Programme on Rare Diseases (EJP RD) aims at developing an RD infrastructure which supports the secondary use of data. Significant amounts of RD data are a) distributed and b) available only in pseudonymised format. Privacy-Preserving Record Linkage (PPRL) concerns the linking of such distributed datasets without disclosing the participant's identities. We present a concept for linking a PPRL Service to the EJP RD Virtual Platform (VP). Level 1 (resource discovery) connection is provided by running an FDP within the PPRL Service. On Level 2 (data discoverability), the PPRL Service can represent both, an individual and a catalog endpoint. Our solution can count patients in PPRL-supporting resources, count duplicates only once, and count only patients registered to multiple resources. Currently, we are preparing the deployment within the EJP RD VP.

A Hybrid-Approach for Privacy Preserving Record Linkage - A Case Study from Germany.

Over the last decade, the exponential growth in patient data volume and velocity has transformed it into a valuable resource for researchers. Yet, accessing comprehensive, unique patient data sets remains a challenge, particularly when individuals have received treatments across various practices and hospitals. Traditional record linkage methods fall short in adequately protecting patient privacy in these scenarios. Privacy Preserving Record Linkage (PPRL) offers a solution, employing techniques such as data cryptographic methods to identify common patients occurring in multiple datasets, while maintaining the privacy of other patients. This paper proposes an investigation into combined approaches of two common German PPRL tools, namely E-PIX and MainSEL. Each tool, while aiming for 'privacy preservation', employs distinct methods that offer unique advantages and drawbacks. Our research aims to explore these in a combined approach to leverage their respective strengths and mitigate their limitations. We anticipate that this synergistic approach will not only enhance data privacy but also allow for easier synchronisation of research data. This study is particularly pertinent in light of evolving privacy regulations and the increasing complexity of healthcare data management. By advancing PPRL methodologies, we aim to contribute to more robust, privacy-compliant data analysis practices in healthcare research.

Linking Dutch Intensive Care Registry Data to Socioeconomic Status Scores.

This study aimed to gain insight into the success rate of linking the NICE registry with SES data from CBS and to examine whether the characteristics of linked and non-linked patients differ. Although clinically relevant differences were found, in total 93,4% of the admissions were successfully linked.

Factors Influencing the Strategic Governance of EHR Interoperability: A Rapid Literature Review.

While the importance of Electronic Health Records (EHR) interoperability is widely recognised in the healthcare digitalisation context, its optimal governance structure remains controversial, requiring further research. Through the rapid literature review of 32 articles retrieved from PubMed and EBSCO, 47 distinct factors under ten categories were established. The three most cited factors in the reviewed 32 articles were "Robust inter-institutional connections, trust, and the technologies to ensure security", "Legal adaptations to the evolving digitalisation needs", and "Standardisation of terminologies and codes, and harmonised data structure". This review contributes preliminary results for the ongoing research to optimise EHR interoperability governance.

Advancing Healthcare Through Interoperability: Implementing Scalable Solutions for Patient Data Integration.

Healthcare faces significant challenges in exchanging and utilizing health information across diverse providers, necessitating innovative solutions for improved interoperability. This study presents a comprehensive exploration of scalable technical and semantic solutions for patient care integration, emphasizing the implementation of these solutions within the framework of the Fast Healthcare Interoperability Resources (FHIR) standard. Our approach revolves around the development and deployment of Technical Interoperability Suite (TIS) and Semantic Interoperability Suite (SIS) technology solutions to disparate health information systems, predominantly Electronic Health Records (EHRs) into a unified Patient Care Platform, fostering comprehensive data exchange and utilization. The integration process involves importing data from various EHR systems and transforming imported patient data into FHIR-standardized formats. The provided solution supports various functionalities, including automatic and manual importation of patient data, through standard computer-readable templates. The integration of TIS and SIS solutions is underpinned by a robust technological framework, incorporating technologies such as Typescript, Deno, and document-oriented databases such as MongoDB. The effectiveness of our interoperability solutions was validated through deployment in multinational EU projects: ADLIFE and CAREPATH. The scalability and generalizability of our approach underscore its potential for diverse healthcare settings.

Linking Maternal and Child Health Data to Enhance Public Health Surveillance and Implement Sustainable Interventions.

Our initiative aims to enhance the public health informatics infrastructure for surveillance of maternal and child health (MCH) using data captured from electronic health records (EHRs), public health information systems, and administrative health data. Our work includes development, validation, and application of linkage algorithms across records for mothers and children; integration of data across myriad sources; design of routine surveillance reports; and design of longitudinal studies to examine determinants and outcomes in MCH populations. Our work is conducted in partnership with governmental public health agencies, health care providers, academic institutions, and community-based organizations. Future work will build on the enhanced informatics infrastructure to draw from additional public health data sources and/or expand surveillance efforts to include prioritized MCH outcomes. We will further translate knowledge gained from surveillance into action, working with our partners to improve and sustain better MCH equitably in our population.

From EHR to Machine Learning: A Preliminary Report on an Ingestion Pipeline Based on JSON-LD.

In this paper, we present the preliminary experiments for the development of an ingestion mechanism to move data from Electronic Health Records to machine learning processes, based on the concept of Linked Data and the JSON-LD format.

Improving uptake of population health management through scalable analysis of linked electronic health data.

Population Health Management - often abbreviated to PHM - is a relatively new approach for healthcare planning, requiring the application of analytical techniques to linked patient level data. Despite expectations for greater uptake of PHM, there is a deficit of available solutions to help health services embed it into routine use. This paper concerns the development, application and use of an interactive tool which can be linked to a healthcare system's data warehouse and employed to readily perform key PHM tasks such as population segmentation, risk stratification, and deriving various performance metrics and descriptive summaries. Developed through open-source code in a large healthcare system in South West England, and used by others around the country, this paper demonstrates the importance of a scalable, purpose-built solution for improving the uptake of PHM in health services.

Statewide Linkage Study of Salmonella Infection and Colorectal Cancer Incidence in Michigan, United States.

Objective: Nontyphoidal Salmonella infection is one of the most common foodborne illnesses, and its oncogenic potential has been documented in animal models. The primary goal of this study was to examine whether individuals who were exposed to enteric Salmonella infection are more likely to develop colorectal cancer (CRC) than the general population through the linkage of 2 statewide public health surveillance databases. Materials and Methods: We designed a 2-stage probabilistic linkage, starting with 17,587 records of enteric salmonellosis reported to Michigan Department of Health and Human Services between 1992 and 2020. These records did not include unique identifiers (such as Social Security number [SSN]). The initial linkage to LexisNexis address history was conducted to obtain information to calculate each person's time in Michigan as well as SSN for the second linkage. The linkage to the state cancer registry was performed to obtain the observed number of CRC cases, while the expected number of CRC cases was calculated according to corresponding state CRC incidence by age, sex, and calendar year. Results: Ninety-three percent of the initially identified salmonellosis records were sent to LexisNexis linkage, which returned address history, death, and SSN for 97% of the records. Further linkage to the statewide cancer registry identified 98 incident CRC cases. Overall, the observed-to-expected (O/E) ratio was not different from unity (0.833; 95% CI, 0.627-1.003). Conclusions: While the new linkage strategy was found effective and should be applicable to other health conditions, we cannot rule out bias due to incomplete or underreporting of the infection in estimating the risk of CRC.

Virtual Pooled Registry-Cancer Linkage System: an improved method for ascertaining cancer diagnoses.

BACKGROUND: The National Cancer Institute funds many large cohort studies that rely on self-reported cancer data requiring medical record validation. This is labor intensive, costly, and prone to underreporting or misreporting of cancer and disparity-related differential response. US population-based central cancer registries identify incident cancer within their catchment area, yielding all malignant neoplasms and benign brain and central nervous system tumors with standardized data fields. This manuscript describes the development, implementation, and features of a system to facilitate linkage between cohort studies and cancer registries and the release of cancer registry data for matched cohort participants. METHODS: The Virtual Pooled Registry-Cancer Linkage System (VPR-CLS) provides an online system to link cohorts with multiple state cancer registries by 1) securely transmitting a study file to registries, 2) providing an optimized linkage algorithm to generate preliminary match counts, and 3) providing a streamlined process and templated forms for submitting and tracking data requests for cohort participants who matched with registries. RESULTS: In 2022, the VPR-CLS launched with 45 registries, covering 95% of the US state populations and Puerto Rico. Registries have linked with 15 studies having 14 273-10.9 million participants. Except in 1 study, linkage sensitivity ranged from 87.0% to 99.9%. Numerous registries have adopted the VPR-CLS templated institutional review board-registry application (n = 39), templated data use agreement (n = 25), and central institutional review board (n = 16). CONCLUSIONS: The VPR-CLS markedly improves ascertainment of cancer outcomes and is the preferred approach for determination of outcomes from cohort studies, postmarketing surveillance, and clinical trials.

Reporting tumor genomic test results to SEER registries via linkages.

BACKGROUND: Precision medicine has become a mainstay of cancer care in recent years. The National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) Program has been an authoritative source of cancer statistics and data since 1973. However, tumor genomic information has not been adequately captured in the cancer surveillance data, which impedes population-based research on molecular subtypes. To address this, the SEER Program has developed and implemented a centralized process to link SEER registries' tumor cases with genomic test results that are provided by molecular laboratories to the registries. METHODS: Data linkages were carried out following operating procedures for centralized linkages established by the SEER Program. The linkages used Match*Pro, a probabilistic linkage software, and were facilitated by the registries' trusted third party (an honest broker). The SEER registries provide to NCI limited datasets that undergo preliminary evaluation prior to their release to the research community. RESULTS: Recently conducted genomic linkages included OncotypeDX Breast Recurrence Score, OncotypeDX Breast Ductal Carcinoma in Situ, OncotypeDX Genomic Prostate Score, Decipher Prostate Genomic Classifier, DecisionDX Uveal Melanoma, DecisionDX Preferentially Expressed Antigen in Melanoma, DecisionDX Melanoma, and germline tests results in Georgia and California SEER registries. CONCLUSIONS: The linkages of cancer cases from SEER registries with genomic test results obtained from molecular laboratories offer an effective approach for data collection in cancer surveillance. By providing de-identified data to the research community, the NCI's SEER Program enables scientists to investigate numerous research inquiries.

Implementing privacy preserving record linkage: Insights from Australian use cases.

OBJECTIVE: To describe the use of privacy preserving linkage methods operationally in Australia, and to present insights and key learnings from their implementation. METHODS: Privacy preserving record linkage (PPRL) utilising Bloom filters provides a unique practical mechanism that allows linkage to occur without the release of personally identifiable information (PII), while still ensuring high accuracy. RESULTS: The methodology has received wide uptake within Australia, with four state linkage units with privacy preserving capability. It has enabled access to general practice and private pathology data amongst other, both much sought after datasets previous inaccessible for linkage. CONCLUSION: The Australian experience suggests privacy preserving linkage is a practical solution for improving data access for policy, planning and population health research. It is hoped interest in this methodology internationally continues to grow.