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BackgroundThe harmonization and standardization of digital medical information for research purposes is a challenging and ongoing collaborative effort. Current research data repositories typically require extensive efforts in harmonizing and transforming original clinical data. The Fast Healthcare Interoperability Resources (FHIR) format was designed primarily to represent clinical processes; therefore, it closely resembles the clinical data model and is more widely available across modern electronic health records. However, no common standardized data format is directly suitable for statistical analyses, and data need to be preprocessed before statistical analysis. ObjectiveThis study aimed to elucidate how FHIR data can be queried directly with a preprocessing service and be used for statistical analyses. MethodsWe propose that the binary JavaScript Object Notation format of the PostgreSQL (PSQL) open source database is suitable for not only storing FHIR data, but also extending it with preprocessing and filtering services, which directly transform data stored in FHIR format into prepared data subsets for statistical analysis. We specified an interface for this preprocessor, implemented and deployed it at University Hospital Erlangen-Nürnberg, generated 3 sample data sets, and analyzed the available data. ResultsWe imported real-world patient data from 2016 to 2018 into a standard PSQL database, generating a dataset of approximately 35.5 million FHIR resources, including “Patient,” “Encounter,” “Condition” (diagnoses specified using International Classification of Diseases codes), “Procedure,” and “Observation” (laboratory test results). We then integrated the developed preprocessing service with the PSQL database and the locally installed web-based KETOS analysis platform. Advanced statistical analyses were feasible using the developed framework using 3 clinically relevant scenarios (data-driven establishment of hemoglobin reference intervals, assessment of anemia prevalence in patients with cancer, and investigation of the adverse effects of drugs). ConclusionsThis study shows how the standard open source database PSQL can be used to store FHIR data and be integrated with a specifically developed preprocessing and analysis framework. This enables dataset generation with advanced medical criteria and the integration of subsequent statistical analysis. The web-based preprocessing service can be deployed locally at the hospital level, protecting patients’ privacy while being integrated with existing open source data analysis tools currently being developed across Germany.

BackgroundClinical trial registries increase transparency in medical research by making information and results of planned, ongoing, and completed studies publicly available. However, the registration of clinical trials remains a time-consuming manual task complicated by the fact that the same studies often need to be registered in different registries with different data entry requirements and interfaces. ObjectiveThis study investigates how Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) may be used as a standardized format for exchanging and storing clinical trial records. MethodsWe designed and prototypically implemented an open-source central trial registry containing records from university hospitals, which are automatically exported and updated by local study management systems. ResultsWe provided an architecture and implementation of a multisite clinical trials registry based on HL7 FHIR as a data storage and exchange format. ConclusionsThe results show that FHIR resources establish a harmonized view of study information from heterogeneous sources by enabling automated data exchange between trial centers and central study registries.

Introduction: Human and veterinary medicine are practiced separately, but literature databases such as Pubmed include articles from both fields. This impedes supporting clinical decisions with automated information retrieval, because treatment considerations would not ignore the discipline of mixed sources. Here we investigate data-driven methods from computational linguistics for automatically distinguishing between human and veterinary medical texts. Methods: For our experiments, we selected language models after a literature review of benchmark datasets and reported performances. We generated a dataset of around 48,000 samples for binary text classification, specifically designed to differentiate between human medical and veterinary subjects. Using this dataset, we trained and fine-tuned classifiers based on selected transformer-based models as well as support vector machines (SVM). Results: All trained classifiers achieved more than 99% accuracy, even though the transformer-based classifiers moderately outperformed the SVMbased one. Discussion: Such classifiers could be applicable in clinical decision support functions that build on automated information retrieval. [ABSTRACT FROM AUTHOR]

is an evident secondary use for electronic health records (EHR) data accumulated during routine care. This task requires relevant data elements to be both available in the EHR and in a structured form. This work analyzes these data quality dimensions of EHR data elements corresponding to a selection of frequent eligibility criteria over a total of 436 patient records at 10 university hospitals within the MIRACUM consortium. Data elements from demographics, diagnosis and laboratory findings are typically structured with a completeness of 73 % to 88 % while medication as well as procedures are rather untructured with a completeness of only 44 %. The results can be used to derive suggestions for data quality improvement measures with respect to patient recruitment as well as to serve as a baseline to quantify future developments. [ABSTRACT FROM AUTHOR]

A significant portion of data in Electronic Health Records is only available as unstructured text, such as surgical or finding reports, clinical notes and discharge summaries. To use this data for secondary purposes, natural language processing (NLP) tools are required to extract structured information. Furthermore, for interoperable use, harmonization of the data is necessary. HL7 Fast Healthcare Interoperability Resources (FHIR), an emerging standard for exchanging healthcare data, defines such a structured format. For German-language medical NLP, the tool Averbis Health Discovery (AHD) represents a comprehensive solution. AHD offers a proprietary REST interface for text analysis pipelines. To build a bridge between FHIR and this interface, we created a service that translates the communication around AHD from and to FHIR. The application is available under an open source license. [ABSTRACT FROM AUTHOR]

The MIRACUM consortium is developing a Clinical Trials Recruitment Support System to support the data-driven recruitment of patients for clinical trials.The design of the prototype includes both open source solutions (OMOP CDM,Atlas) and open standards for interoperability (FHIR). The aim of the prototype is to create a patient screening list of potential participants for a clinical study. The paper shows the modular structure and functionality of the prototype building the foundation for the practical implementation of the CTRSS and, at the same time,demonstrating the use of open source solutions and standards for the development of clinical support systems. [ABSTRACT FROM AUTHOR]

Background and objective: To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in this work implements a tool for researchers allowing them to perform statistical analyses and deploy resulting models in a secure environment. Methods: The proposed system uses Docker virtualization to provide researchers with reproducible data analysis and development environments, accessible via Jupyter Notebook, to perform statistical analysis and develop, train and deploy models based on standardized input data. The platform is built in a modular fashion and interfaces with web services using the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard to access patient data. In our prototypical implementation we use an OMOP common data model (OMOP-CDM) database. The architecture supports the entire research lifecycle from creating a data analysis environment, retrieving data, and training to final deployment in a hospital setting. Results: We evaluated the platform by establishing and deploying an analysis and end user application for hemoglobin reference intervals within the University Hospital Erlangen. To demonstrate the potential of the system to deploy arbitrary models, we loaded a colorectal cancer dataset into an OMOP database and built machine learning models to predict patient outcomes and made them available via a web service. We demonstrated both the integration with FHIR as well as an example end user application. Finally, we integrated the platform with the open source DataSHIELD architecture to allow for distributed privacy preserving data analysis and training across networks of hospitals. Conclusion: The KETOS platform takes a novel approach to data analysis, training and deploying decision support models in a hospital or healthcare setting. It does so in a secure and privacy-preserving manner, combining the flexibility of Docker virtualization with the advantages of standardized vocabularies, a widely applied database schema (OMOP-CDM), and a standardized way to exchange medical data (FHIR). [ABSTRACT FROM AUTHOR]

Understanding the prevalence of structured data elements within clinical trial eligibility criteria is a crucial step for prioritizing integration efforts to supported automated patient recruitment into clinical trials based on electronic health record data. In this work, we extract data elements from 50 clinical trials using a collaborative, crowd-sourced, and iterative method. A total of 1.120 criteria were analyzed, and 204 unique data elements were extracted. The most prevalent elements were diagnosis code, procedure code, and medication code, occurring in 414 (37%), 112 (10%), and 91 (8%) of eligibility criteria respectively. The results of this study may aid in optimizing data integration and documentation efforts in the EHR to support clinical trial eligibility determination. [ABSTRACT FROM AUTHOR]

IT-supported patient recruitment, serious adverse event reporting as well as making information about clinical trials accessible to the public on websites, are still major challenges in clinical trials. Too often the distribution of information about trials being performed within a hospital across numerous institutions and IT systems is a barrier to provide efficient IT support for such scenarios. Thus, the essential prerequisite to mastering those challenges is to achieve one single point of truth with adequate, complete, accurate and up-to-date information about all clinical trials running at a hospital. We describe the design and implementation of such a single source clinical trial registry serving multiple purposes at a university hospital. [ABSTRACT FROM AUTHOR]

Clinical trials are the foundation of evidence-based medicine and their computerized support has been a recurring theme in medical informatics. One challenging aspect is the representation of eligibility criteria in a machine-readable format to automate the identification of suitable participants. In this study, we investigate the capabilities for expressing trial eligibility criteria via the search functionality specified in HL7 FHIR, an emerging standard for exchanging healthcare information electronically which also defines a set of operations for searching for health record data. Using a randomly sampled subset of 303 eligibility criteria from ClinicalTrials.gov yielded a 34 % success rate in representing them using the FHIR search semantics. While limitations are present, the FHIR search semantics are a viable tool for supporting preliminary trial eligibility assessment. [ABSTRACT FROM AUTHOR]