Federated electronic health records research technology to support clinical trial protocol optimization: Evidence from EHR4CR and the InSite platform.

OBJECTIVE: To determine if inclusion/exclusion (I/E) criteria of clinical trial protocols can be represented as structured queries and executed using a secure federated research platform (InSite) on hospital electronic health records (EHR) systems, to estimate the number of potentially eligible patients. METHODS: Twenty-three clinical trial protocols completed during 2011-2017 across diverse disease areas were analyzed to construct queries that were executed with InSite using EHR records from 24 European hospitals containing records of >14 million patients. The number of patients matching I/E criteria of each protocol was estimated. RESULTS: All protocols could be formalized to some extent into a medical coding system (e.g. ICD-10CM, ATC, LOINC, SNOMED) and mapped to local hospital coding systems. The median number of I/E criteria of protocols tested was 29 (range: 14-47). A median of 55% (range 38-89%) of I/E criteria in each protocol could be transformed into a computable format. The median number of eligible patients identified was 26 per hospital site (range: 1-134). CONCLUSION: Clinical trial I/E eligibility criteria can be structured computationally and executed as queries on EHR systems to estimate the patient recruitment pool at each site. The results further suggest that an increase in structured coded information in EHRs would increase the number of I/E criteria that could be evaluated. Additional work is needed on broader deployment of federated platforms such as InSite.

The INTEGRATE project: Delivering solutions for efficient multi-centric clinical research and trials.

The objective of the INTEGRATE project (http://www.fp7-integrate.eu/) that has recently concluded successfully was the development of innovative biomedical applications focused on streamlining the execution of clinical research, on enabling multidisciplinary collaboration, on management and large-scale sharing of multi-level heterogeneous datasets, and on the development of new methodologies and of predictive multi-scale models in cancer. In this paper, we present the way the INTEGRATE consortium has approached important challenges such as the integration of multi-scale biomedical data in the context of post-genomic clinical trials, the development of predictive models and the implementation of tools to facilitate the efficient execution of postgenomic multi-centric clinical trials in breast cancer. Furthermore, we provide a number of key "lessons learned" during the process and give directions for further future research and development.

Using electronic health records for clinical research: the case of the EHR4CR project.

OBJECTIVES: To describe the IMI EHR4CR project which is designing and developing, and aims to demonstrate, a scalable, widely acceptable and efficient approach to interoperability between EHR systems and clinical research systems. METHODS: The IMI EHR4CR project is combining and extending several previously isolated state-of-the-art technical components through a new approach to develop a platform for reusing EHR data to support medical research. This will be achieved through multiple but unified initiatives across different major disease areas (e.g. cardiovascular, cancer) and clinical research use cases (protocol feasibility, patient identification and recruitment, clinical trial execution and serious adverse event reporting), with various local and national stakeholders across several countries and therefore under various legal frameworks. RESULTS: An initial instance of the platform has been built, providing communication, security and terminology services to the eleven participating hospitals and ten pharmaceutical companies located in seven European countries. Proof-of-concept demonstrators have been built and evaluated for the protocol feasibility and patient recruitment scenarios. The specifications of the clinical trial execution and the adverse event reporting scenarios have been documented and reviewed. CONCLUSIONS: Through a combination of a consortium that brings collectively many years of experience from previous relevant EU projects and of the global conduct of clinical trials, of an approach to ethics that engages many important stakeholders across Europe to ensure acceptability, of a robust iterative design methodology for the platform services that is anchored on requirements of an underlying Service Oriented Architecture that has been designed to be scalable and adaptable, EHR4CR could be well placed to deliver a sound, useful and well accepted pan-European solution for the reuse of hospital EHR data to support clinical research studies.

A global federated real-world data and analytics platform for research.

Objective: This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research. Materials and Methods: TriNetX has created a technology platform characterized by a conservative security and governance model that facilitates collaboration and cooperation between industry participants, such as pharmaceutical companies and contract research organizations, and academic and community-based healthcare organizations (HCOs). HCOs participate on the network in return for access to a suite of analytics capabilities, large networks of de-identified data, and more sponsored trial opportunities. Industry participants provide the financial resources to support, expand, and improve the technology platform in return for access to network data, which provides increased efficiencies in clinical trial design and deployment. Results: TriNetX is a growing global network, expanding from 55 HCOs and 7 countries in 2017 to over 220 HCOs and 30 countries in 2022. Over 19 000 sponsored clinical trial opportunities have been initiated through the TriNetX network. There have been over 350 peer-reviewed scientific publications based on the network's data. Conclusions: The continued growth of the TriNetX network and its yield of clinical trial collaborations and published studies indicates that this academic-industry structure is a safe, proven, sustainable path for building and maintaining research-centric data networks.

Building an I2B2-Based Population Repository for Clinical Research.

The present work provides a real-world case of the connection process of a hospital, 12 de Octubre University Hospital in Spain, to the TriNetX research network, transforming a compilation of disparate sources into a single harmonized repository which is automatically refreshed every day. It describes the different integration phases: terminology core datasets, specialized sources and eventually automatic refreshment. It also explains the work performed on semantic normalization of the involved clinical terminologies; as well as the resulting benefits the InSite platform services have enabled in the form of research opportunities for the hospital.

A data protection framework for trans-European genetic research projects.

The paper proposes a data protection framework for trans-European medical research projects, which is based on a technical security infrastructure as well as on organizational measures and contractual obligations. It mainly relies on pseudonymization, an internal Data Protection Authority and on a Trusted Third Party. The outcome is an environment that combines both good research conditions and an extensive protection of patients' privacy.

Cohort Selection and Management Application Leveraging Standards-based Semantic Interoperability and a Groovy DSL.

This paper describes a new Cohort Selection application implemented to support streamlining the definition phase of multi-centric clinical research in oncology. Our approach aims at both ease of use and precision in defining the selection filters expressing the characteristics of the desired population. The application leverages our standards-based Semantic Interoperability Solution and a Groovy DSL to provide high expressiveness in the definition of filters and flexibility in their composition into complex selection graphs including splits and merges. Widely-adopted ontologies such as SNOMED-CT are used to represent the semantics of the data and to express concepts in the application filters, facilitating data sharing and collaboration on joint research questions in large communities of clinical users. The application supports patient data exploration and efficient collaboration in multi-site, heterogeneous and distributed data environments.

Enabling semantic interoperability in multi-centric clinical trials on breast cancer.

BACKGROUND AND OBJECTIVES: Post-genomic clinical trials require the participation of multiple institutions, and collecting data from several hospitals, laboratories and research facilities. This paper presents a standard-based solution to provide a uniform access endpoint to patient data involved in current clinical research. METHODS: The proposed approach exploits well-established standards such as HL7 v3 or SPARQL and medical vocabularies such as SNOMED CT, LOINC and HGNC. A novel mechanism to exploit semantic normalization among HL7-based data models and biomedical ontologies has been created by using Semantic Web technologies. RESULTS: Different types of queries have been used for testing the semantic interoperability solution described in this paper. The execution times obtained in the tests enable the development of end user tools within a framework that requires efficient retrieval of integrated data. CONCLUSIONS: The proposed approach has been successfully tested by applications within the INTEGRATE and EURECA EU projects. These applications have been deployed and tested for: (i) patient screening, (ii) trial recruitment, and (iii) retrospective analysis; exploiting semantically interoperable access to clinical patient data from heterogeneous data sources.

Semantic Normalization and Query Abstraction Based on SNOMED-CT and HL7: Supporting Multicentric Clinical Trials.

Advances in the use of omic data and other biomarkers are increasing the number of variables in clinical research. Additional data have stratified the population of patients and require that current studies be performed among multiple institutions. Semantic interoperability and standardized data representation are a crucial task in the management of modern clinical trials. In the past few years, different efforts have focused on integrating biomedical information. Due to the complexity of this domain and the specific requirements of clinical research, the majority of data integration tasks are still performed manually. This paper presents a semantic normalization process and a query abstraction mechanism to facilitate data integration and retrieval. A process based on well-established standards from the biomedical domain and the latest semantic web technologies has been developed. Methods proposed in this paper have been tested within the EURECA EU research project, where clinical scenarios require the extraction of semantic knowledge from biomedical vocabularies. The aim of this paper is to provide a novel method to abstract from the data model and query syntax. The proposed approach has been compared with other initiatives in the field by storing the same dataset with each of those solutions. Results show an extended functionality and query capabilities at the cost of slightly worse performance in query execution. Implementations in real settings have shown that following this approach, usable interfaces can be developed to exploit clinical trial data outcomes.

Implementation framework for digital signatures for electronic data interchange in healthcare.

This paper aims to propose an action plan for the deployment of the use of digital signatures in Belgian healthcare. This action plan is the result of a number of technical, legal and organisational requirements. It starts by establishing the functional components that are needed to set up a framework for the deployment of digital signatures. The main components should implement an infrastructure for: --the creation of digital signatures; --the verification of digital signatures; --the certification of signature keys; --the certification of attributes; --the handling of revocation. The tasks in the action plan are the logical consequence of all the functions that need to be addressed. The objective of this report is to list what has to be done and how it can be done in the context of healthcare, rather to state who will perform the functions required.

Secure communication and management of clinical and genomic data: the use of pseudonymisation as privacy enhancing technique.

The growing need of managing both clinical and genetic data raises important legal and ethical challenges. This article introduces some of the privacy protection problems related to genomic medicine and highlights the relevance of Trusted Third Parties and of Privacy Enhancing Techniques (PETs) in the context of e.g. research. Practical approaches based on two pseudonymisation models, for both batch and interactive data collection and exchange, are presented.

The PRIDEH project: taking up privacy protection services in eHealth.

PRIDEH (Privacy Enhancement in Data Management in e-Health) is a project that runs for two years and is partly funded by the European Commission. The focus of PRIDEH is on the stimulation of the take-up of privacy enhancing technologies within the health domain. Privacy enhancing technologies build upon available cryptographic and communication technologies. The concept of privacy enhancing services delivery through the use of intermediary trusted third parties has already been described in literature but rarely turned into practice. A number of limited PET (Privacy Enhancing Technologies) applications exist in closed domains. The very few existing solutions are not based on an independent TTP (Trusted Third Party) concept which renders their trustworthiness questionable. PRIDEH wants to enhance the take-up of PET delivery based on sound TTP principles in the domain of healthcare.

Supporting patient screening to identify suitable clinical trials.

To support the efficient execution of post-genomic multi-centric clinical trials in breast cancer we propose a solution that streamlines the assessment of the eligibility of patients for available trials. The assessment of the eligibility of a patient for a trial requires evaluating whether each eligibility criterion is satisfied and is often a time consuming and manual task. The main focus in the literature has been on proposing different methods for modelling and formalizing the eligibility criteria. However the current adoption of these approaches in clinical care is limited. Less effort has been dedicated to the automatic matching of criteria to the patient data managed in clinical care. We address both aspects and propose a scalable, efficient and pragmatic patient screening solution enabling automatic evaluation of eligibility of patients for a relevant set of trials. This covers the flexible formalization of criteria and of other relevant trial metadata and the efficient management of these representations.

Analyzing SNOMED CT and HL7 terminology binding for semantic interoperability on post-genomic clinical trials.

Current post-genomic clinical trials in cancer involve the collaboration of several institutions. Multi-centric retrospective analysis requires advanced methods to ensure semantic interoperability. In this scenario, the objective of the EU funded INTEGRATE project, is to provide an infrastructure to share knowledge and data in post-genomic breast cancer clinical trials. This paper presents the process carried out in this project, to bind domain terminologies in the area, such as SNOMED CT, with the HL7 v3 Reference Information Model (RIM). The proposed terminology binding follow the HL7 recommendations, but should also consider important issues such as overlapping concepts and domain terminology coverage. Although there are limitations due to the large heterogeneity of the data in the area, the proposed process has been successfully applied within the context of the INTEGRATE project. An improvement in semantic interoperability of patient data from modern breast cancer clinical trials, aims to enhance the clinical practice in oncology.