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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]

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]

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]

Background: Clinical trials are essential for medical research and medical progress. Nevertheless, trials often fail to reach their recruitment goals. Patient recruitment systems aim to support clinical trials by providing an automated search for eligible patients in the databases of health care institutions like university hospitals. To integrate patient recruitment systems into existing workflows, previous works have assessed user requirements for these tools. In this study, we tested patient recruitment systems KAS+ and recruIT as part of the MIRACUM (Medical Informatics in Research and Care in University Medicine) project., Objective: Our goal was to investigate whether and to what extent the 2 different evaluated tools can meet the requirements resulting from the first requirements analysis, which was performed in 2018-2019. A user survey was conducted to determine whether the tools are usable in practice and helpful for the trial staff. Furthermore, we investigated whether the test phase revealed further requirements for recruitment tools that were not considered in the first place., Methods: We performed semistructured interviews with 10 participants in 3 German university hospitals who used the patient recruitment tools KAS+ or recruIT for at least 1 month with currently recruiting trials. Thereafter, the interviews were transcribed and analyzed by Meyring method. The identified statements of the interviewees were categorized into 5 groups of requirements and sorted by their frequency., Results: The evaluated recruIT and KAS+ tools fulfilled 7 and 11 requirements of the 12 previously identified requirements, respectively. The interviewed participants mentioned the need for different notification schedules, integration into their workflow, different patient characteristics, and pseudonymized screening lists. This resulted in a list of new requirements for the implementation or enhancement of patient recruitment systems., Conclusions: Trial staff report a huge need of support for the identification of eligible trial participants. Moreover, the workflows in patient recruitment differ across trials. For better suitability of the recruitment systems in the workflow of different kinds of trials, we recommend the implementation of an adjustable notification schedule for screening lists, a detailed workflow analysis, broad patient filtering options, and the display of all information needed to identify the persons on the list. Despite criticisms, all participants confirmed to use the patient recruitment systems again., (©Alexandra Stein, Romina Blasini, Cosima Strantz, Kai Fitzer, Christian Gulden, Torsten Leddig, Wolfgang Hoffmann. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 27.09.2024.)

Background: Clinical trials (CTs) are crucial for medical research; however, they frequently fall short of the requisite number of participants who meet all eligibility criteria (EC). A clinical trial recruitment support system (CTRSS) is developed to help identify potential participants by performing a search on a specific data pool. The accuracy of the search results is directly related to the quality of the data used for comparison. Data accessibility can present challenges, making it crucial to identify the necessary data for a CTRSS to query. Prior research has examined the data elements frequently used in CT EC but has not evaluated which criteria are actually used to search for participants. Although all EC must be met to enroll a person in a CT, not all criteria have the same importance when searching for potential participants in an existing data pool, such as an electronic health record, because some of the criteria are only relevant at the time of enrollment., Objective: In this study, we investigated which groups of data elements are relevant in practice for finding suitable participants and whether there are typical elements that are not relevant and can therefore be omitted., Methods: We asked trial experts and CTRSS developers to first categorize the EC of their CTs according to data element groups and then to classify them into 1 of 3 categories: necessary, complementary, and irrelevant. In addition, the experts assessed whether a criterion was documented (on paper or digitally) or whether it was information known only to the treating physicians or patients., Results: We reviewed 82 CTs with 1132 unique EC. Of these 1132 EC, 350 (30.9%) were considered necessary, 224 (19.8%) complementary, and 341 (30.1%) total irrelevant. To identify the most relevant data elements, we introduced the data element relevance index (DERI). This describes the percentage of studies in which the corresponding data element occurs and is also classified as necessary or supplementary. We found that the query of "diagnosis" was relevant for finding participants in 79 (96.3%) of the CTs. This group was followed by "date of birth/age" with a DERI of 85.4% (n=70) and "procedure" with a DERI of 35.4% (n=29)., Conclusions: The distribution of data element groups in CTs has been heterogeneously described in previous works. Therefore, we recommend identifying the percentage of CTs in which data element groups can be found as a more reliable way to determine the relevance of EC. Only necessary and complementary criteria should be included in this DERI., (©Romina Blasini, Cosima Strantz, Christian Gulden, Sven Helfer, Jakub Lidke, Hans-Ulrich Prokosch, Keywan Sohrabi, Henning Schneider. Originally published in JMIR Formative Research (https://formative.jmir.org), 31.01.2024.)

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.