Search

The integration of routine medical care data into research endeavors promises great value. However, access to this extra-domain data is constrained by numerous technical and legal requirements. The German Medical Informatics Initiative (MII) - initiated by the Federal Ministry of Research and Education (BMBF) - is making progress in setting up Medical Data Integration Centers to consolidate data stored in clinical primary information systems. Unfortunately, for many research questions cross-organizational data sources are required, as one organization's data is insufficient, especially in rare disease research. A first step, for research projects exploring possible multi-centric study designs, is to perform a feasibility query, i.e., a cohort size calculation transcending organizational boundaries. Existing solutions for this problem, like the previously introduced feasibility process for the MII's HiGHmed consortium, perform well for most use cases. However, there exist use cases where neither centralized data repositories, nor Trusted Third Parties are acceptable for data aggregation. Based on open standards, such as BPMN 2.0 and HL7 FHIR R4, as well as the cryptographic techniques of secure Multi-Party Computation, we introduce a fully automated, decentral feasibility query process without any central component or Trusted Third Party. The open source implementation of the proposed solution is intended as a plugin process to the HiGHmed Data Sharing Framework. The process's concept and underlying algorithms can also be used independently.

A generic approach for assessment and continuous monitoring of data quality in ODM-based research data has been developed. The focus is on the two data quality indicators completeness and syntactic correctness. The main idea is to enable the generation of a data quality report without additional programming effort.

Pseudonymization plays a vital role in medical research. In Germany, the Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF) has developed guidelines on how to create pseudonyms and how to handle personally identifiable information (PII) during this process. An open-source implementation of a pseudonymization service following these guidelines and therefore recommended by the TMF is the so-called "Mainzelliste". This web application supports a REST-API for (de-) pseudonymization. For security reasons, a complex session and tokening mechanism for each (de-) pseudonymization is required and a careful interaction between front- and backend to ensure a correct handling of PII. The objective of this work is the development of a library to simplify the integration and usage of the Mainzelliste's API in a TMF conform way. The frontend library uses JavaScript while the backend component is based on Java with an optional Spring Boot extension. The library is available under MIT open-source license from https://github.com/DanielPreciado-Marquez/MainzelHandler.

The Operational Data Model (ODM) is a data standard for interchanging clinical trial data. ODM contains the metadata definition of a study, i.e., case report forms, as well as the clinical data, i.e., the answers of the participants. The portal of medical data models is an infrastructure for creation, exchange, and analysis of medical metadata models. There, over 23000 metadata definitions can be downloaded in ODM format. Due to data protection law and privacy issues, clinical data is not contained in these files. Access to exemplary clinical test data in the desired metadata definition is necessary in order to evaluate systems claiming to support ODM or to evaluate if a planned statistical analysis can be performed with the defined data types. In this work, we present a web application, which generates syntactically correct clinical data in ODM format based on an uploaded ODM metadata definition. Data types and range constraints are taken into account. Data for up to one million participants can be generated in a reasonable amount of time. Thus, in combination with the portal of medical data models, a large number of ODM files including metadata definition and clinical data can be provided for testing of any ODM supporting system. The current version of the application can be tested at https://cdgen.uni-muenster.de and source code is available, under MIT license, at https://imigitlab.uni-muenster.de/published/odm-clinical-data-generator.

Reading is an important ability, especially for patients during their medical treatment. It is needed, for instance, to complete administrative forms and patient-reported outcome questionnaires in clinical routine. Unfortunately, not every patient is able to read caused by illiteracy, low vision or simply speaking another language. Thus, a minder is required to support the mentioned reading tasks. Providing patients with the possibility to read and understand texts without additional help is an important factor to improve their self-empowerment. Digital voice pens can be programmed to play prerecorded audio files if tipped onto predefined areas of interactive paper. They can be a tool for impaired patients to read texts aloud in multiple languages. In this work, we wanted to evaluate the possibilities of these digital voice pens. A feasibility study was conducted by using the commercially available tiptoi digital voice pen by Ravensburger AG and the tttool application by Joachim Breitner for the programming of the pen. Focusing on the use case of questionnaires, a schematic questionnaire was implemented which enforced the usage of a digital voice pen. To simulate foreign languages or illiteracy, questions and answers of the document were represented by placeholders and the digital voice pen was required to read aloud the question texts. The correctness of the given answers was documented and the usability of the digital voice pen was measured by the System Usability Scale. The evaluation was performed by 15 volunteers (8 male/7 female) between 24 and 35 years old. The usability and acceptance of digital voice pens were rated as "Good" in our constructed setting.

Data analysis with popular statistical toolchains like R usually needs to be performed on "flat tables" (so-called dataframes). However, data exchange is often done with FHIR, a format that is based on a hierarchical data model. In this paper, we want to present our tool FhirExtinguisher, which tackles the problems of loading FHIR data into statistical tools by extending the FHIRSearch API with an additional projection layer using FHIRPath. This projection layer can be used to select the data elements of interest and create a CSV file, which can be easily read as dataframe by almost any statistical toolchain.

The interaction of multiple computer systems during multi-center randomized controlled trials (RCTs) is a hurdle for IT-specialists as well as medical staff. A common workflow for the initial registration of a patient requires the generation of a pseudonym by a pseudonymization service, a manual transmission of the pseudonym to a randomization service, and a manual transfer of the pseudonym and assigned study arm into an electronic data capture (EDC) system. This interaction is often time consuming and error prone due to multiple system changes. Objective of this work is to enhance a commonly used EDC system, Research Electronic Data Capture (REDCap), as a single source of interaction for multi-center RCTs. This is achieved by providing two modules for a seamless integration of a pseudonymization service, i.e., Mainzelliste, and a randomization service, i.e., RandIMI. Thus, no site-specific system changes are required, which increases time efficiency and reduces errors. From a technical perspective, only authentication credentials and firewall exposure for a single system must be managed. To evaluate the usability of our implementation, the system usability scale was employed. The increase of time efficiency was measured in laboratory conditions by a comparison of the time for patient registrations with and without our modules. An "excellent" usability was shown and an average time reduction by nearly 64 %. Both open-source modules are available from the REDCap Repository of External Modules.

The Portal of Medical Data Models has been developed since 2011 by the University of Münster. Its main goals are transparency, standardization and secondary use of medical metadata. Via two online surveys feedback from stakeholders of German health research was collected regarding the portal's contents. The surveys confirmed great interest in secondary use of medical forms.

Real world data for use in clinical trials is promising. We compared the SDTM for clinical trial data submission with FHIR® for routine documentation. After categorization of variables by relevance, clinically relevant SDTM items were mapped to FHIR®. About 30% in both were seen as clinically relevant. The majority of these SDTM items were mappable to FHIR® Observation resource.

Case report forms (CRF) specify data definitions and encodings for data to be collected in clinical trials. To enable exchange of data definitions and in this way to avoid creation of variants of CRF for similar study designs, the Medical Data Model portal (MDM) has been developed since 2011. This work aims at studying the usability of the MDM portal. We identify issues that hamper its adoption by researchers in order to derive measurements for improving it. We selected relevant tools (e.g. Nibbler, Hotjar, SUPR-Q) for usability testing and generated a structured test protocol. More specifically, the portal was assessed by means of a static analysis, user analysis (n=10), a usability test (n=10) and statistical evaluations. Regarding accessibility and technology, the static code analysis resulted in high scores. Presentation of information and functions as well as interaction with the portal still has to be improved: The results show that only limited functions of the webpage are used regularly and some user navigation errors occur due to the portal's design. In total, six major problems were identified which will be addressed in future. A continuous evaluation using the same structured test protocol allows to continuously measure the website quality, to compare it after changes have been implemented and in this way, to realise a continuous improvement. The effort for a repeated evaluation of the same evaluation with 10 persons is estimated with 10 hours.

We evaluated three repositories implementing the emerging healthcare data standard openEHR for their standard compliance, completeness and vendor lock-in. We found the basic functionality to work consistently across all tested repositories. At the same time, no vendor supports the entire API yet. Some functions like template listing differ slightly in their behavior. Some vendors offer additional custom APIs that are easier to use but lead to vendor lock-in. The openEHR standard itself is designed inconsistently regarding data formats and is missing some basic functionality, for example deletion of templates. With openEHR being a young standard, these issues may be resolved in future releases.

Consumer wearables can provide objective monitoring of movement disorders and may identify new phenotypical biomarkers. We present a novel smartwatch-based prototype, which is implemented as a prospective study in neurology. A full-stack Machine Learning pipeline utilizing Artificial Neural Networks (ANN), Random Forests and Support Vector Machines (SVM) was established and optimized to train for two clinically relevant classification tasks: First, to distinguish neurodegenerative movement disorders such as Parkinson's Disease (PD) or Essential Tremor from healthy subjects. Second, to distinguish specifically PD from other movement disorders or healthy subjects. The system was trained by 318 samples, including 192 PD, 75 other movement disorders and 51 healthy participants. All models were trained and tested with hyperparameter optimization and nested cross-validation. Regarding the more general first task, the ANN succeeded best with precision of 0.94 (SD 0.03) and recall of 0.92 (SD 0.04). Concerning the more specific second task, the SVM performed best with precision of 0.81 (SD 0.01) and recall of 0.89 (SD 0.04). These preliminary results are promising as compared to the literature-reported diagnostic accuracy of neurologists. In addition, a new data foundation with highly structured and clinically annotated acceleration data was established, which enables future biomarker analyses utilizing consumer devices in movement disorders.

Patient Reported Outcomes (PROs) provide essential clinical data for the diagnosis and treatment of patients. Mobile technologies enable rapid and structured collection of PROs with a high usability. MoPat is an electronic PRO system developed at the Münster University that enables patients to complete PROs in multiple languages. This research reports the further development of MoPat and the inclusion of features to document images electronically that will be evaluated in a multi-site clinical research.

Compatible data models are key for data integration. Data transformation after data collection has many limitations. Therefore compatible data structures should be addressed already during the design of information systems. The portal of Medical Data Models (MDM), which contains 20.000+ models and 495.000+ data items, was enhanced with a web service to identify data elements, which are frequently collected together in real information systems. Using Apache Solr, a fast search functionality to identify those elements with semantic annotations was implemented. This service was integrated into the metadata registry (MDR) component of MDM to make it available to the scientific community. It can be used to build intelligent data model editors, which suggest and import frequent data element definitions according to the current medical context.

International trial databases as ClinicalTrials.gov or the EU Clinical Trials Register lack geographic visualization of clinical trials. Utilizing key requirements from patient support groups and clinical researchers, an interactive online platform called StudyPortal was designed. It enables patients, health providers and clinical researchers to find and localize suitable studies or whole research networks for selected diseases in a geographic proximity. A semantic layer enables multilingual disease input and autosuggestion. Trial information is pulled and processed from ClinicalTrials.gov. In addition, author affiliations of disease-related PubMed articles are retrievable in order to boost sensitivity of visualized research networks. The integration of a geodatabase and maps enables access to geospatial study search and visualization. A preliminary implementation of the platform is already accessible on the web: https://studyportal.uni-muenster.de. It showed that over 70% of trials and over 90% of scientific articles are visualized correctly by applying expert review and using Web of Science and the WHO trial database as external sources. Publication and trial-registration bias are significant issues that limit completeness of visualization. ClinicalTrials.gov, MEDLINE and geomaps are well-maintained but disconnected sources. StudyPortal integrates these sources to render a novel geospatial view of regional or global clinical research landscapes of US and European trials in real-time. Future work will focus on extensive search filters for recruitment status and intervention characteristics.

Despite the advances in health information technology and the increasing usage of electronic systems, syntactic and semantic interoperability between different health information systems remains challenging. An emerging standard to tackle interoperability issues is HL7 FHIR, which uses modern web technologies for communication like Representational State Transfer. The electronic patient reported outcome system Mobile Patient Survey (MoPat) was adapted to support metadata import and clinical data export using HL7 FHIR. Thereby, the data models of HL7 FHIR and MoPat were compared and the existing import and export functions of MoPat were extended to support HL7 FHIR. A test protocol including eight test datasets to proof functioning of the new features was successfully conducted. In the near future, a real time searching toolbar of FHIR metadata resources will be integrated within MoPat. MoPat FHIR import and export functions are ready to be used in a clinical setting in combination with a FHIR compliant clinical data server.

In patient care and medical research patient data often has to be transferred between different electronic systems. These systems can be very heterogeneous, sometimes even legacy systems, and thus, often do not support standardized interfaces for data transfer. Since nowadays barcode scanners are commonly used in clinical routine and smartphones are accessible to most patients, we implemented different interfaces based on Data Matrix codes to transfer patient data between several medical applications. Objective of this work is to show different use cases in which Data Matrix codes have been successfully applied and discuss the lessons we have learned during the process of implementation and practical usage.

The Portal of Medical Data Models is an open-access platform for medical forms and data models. Annotation with UMLS codes enables semantic interoperability and secondary use of data. The number of forms and users are growing. The site has been updated and two analyzing tools have been added.

Approximately 300,000 asylum-seeking children arrived in Europe in 2015. The chance of experiencing a traumatic event is very high for fleeing children. Since the origin of the refugees is widespread, the languages spoken are diverse. Multilingual electronic patient-reported outcome systems (ePROs) can be used to gather medical data in a foreign language and display the results in the health professionals' language, which helps overcoming the language barrier. Utilizing such a system, a two-phase study aiming to screen refugee minors for potential mental health issues has started. Potential eligible participants are examined using questionnaires with good psychometric properties and cross-cultural applicability. To date, 75 minors and 21 of their relatives participated in the study, being German and Arabic the most desired languages for the electronic survey. Developing a system that provides multilingual questionnaires entails several drawbacks like a cumbersome translation process and dealing with writing directions. The proposed translation process and the ePRO can be re-used in similar studies.

Annotation with semantic codes helps to overcome interoperability issues for electronic documentation in medicine. However, the manual annotation process is laborious and semantic codes are ambiguous. We developed a publicly accessible web service to alleviate these drawbacks with a sophisticated and fast search mechanism based on more than 330,000 semantic code suggestions. These suggestions are derived from semantically annotated data elements contained in the Portal of Medical Data Models manually curated by medical professionals. Integrating this suggestion service can support the manual annotation process and strengthen uniform coding. Integration is demonstrated for two separate data model editors. Usage statistics show over 5,500 suggestion requests per month for semantic annotation of items. The web service may also prove helpful for automatic semantic coding.

CDISC's Operational Data Model (ODM) is a flexible standard for exchanging and archiving metadata and subject clinical data in clinical trials. The Portal of Medical Data Models (MDM-Portal) uses ODM to store more than 15000 medical forms. As not every electronic health system accepts ODM as input format, there is a need for conversion between ODM and other data standards and formats. This research proposes a standardised template-based process to develop ODM converters. So far, ten converters have been developed and integrated in the MDM-Portal following this process and new ones should be included soon. The template, programming utilities and an ODM test suite have been made online available and can be used to easily develop new converters.

The establishment of a digital healthcare system is a national and community task. The Federal Ministry of Education and Research in Germany is providing funding for consortia consisting of university hospitals among others participating in the "Medical Informatics Initiative". Exchange of medical data between research institutions necessitates a place where meta information for this data is made accessible. Within these consortia different metadata registry solutions were chosen. To promote interoperability between these solutions, we have examined whether the portal of Medical Data Models is eligible for managing and communicating metadata and relevant information across different data integration centres of the Medical Informatics Initiative and beyond. Apart from the MDM-portal, some ISO 11179-based systems such as Samply.MDR as well as openEHR-based solutions are going to be applyed. In this paper, we have focused on the creation of a mapping model between the CDISC ODM standard and the Samply.MDR import format. In summary, it can be stated that the mapping model is feasible and promote the exchangeability between different metadata registry approaches.

Computerized Clinical Decision Support Systems (CDSS) are implemented in hospitals to improve prevention of Venous Thromboembolisms (VTE). A physician-driven review was conducted to assess extent of patient outcome effects of recently published CDSS studies. To facilitate future re-implementations within existing hospital information systems, input variables of included systems were extracted, standardized and annotated with semantic codes. Item category coverages of the different systems were then compared. 73% of studies showed positive medical effect. Of these, 53% showed strong positive medical effect by reducing incidence of deep vein thromboses and pulmonary embolisms. Outcome-improving systems tend to cover more item categories. A broad set of clinically relevant input variables should be taken into account or reused from the electronic health record if considering CDSS implementation. Input data models are provided for download in different standardized formats. Site-specific organizational factors that determine how systems are introduced, implemented and tested are also crucial for success.

Reading centers provide centralized high-quality diagnostics in ophthalmic clinical trials. Since ophthalmic images are captured in electronic format at peripheral clinics, an integrated workflow for image transfer and creation of structured reports is needed, including quality assurance. The image portal and the study database are separate components. We assessed whether this integration is feasible with trial-related IT standards and built a prototype system as a proof-of-concept. CDISC ODM and OAuth authentication were used to integrate the image portal with x4T-EDC, facilitating automatic data transfer and single sign-on.

Data dictionaries provide structural meta-information about data definitions in health information technology (HIT) systems. In this regard, reusing healthcare data for secondary purposes offers several advantages (e.g. reduce documentation times or increased data quality). Prerequisites for data reuse are its quality, availability and identical meaning of data. In diverse projects, research data warehouses serve as core components between heterogeneous clinical databases and various research applications. Given the complexity (high number of data elements) and dynamics (regular updates) of electronic health record (EHR) data structures, we propose a clinical metadata warehouse (CMDW) based on a metadata registry standard. Metadata of two large hospitals were automatically inserted into two CMDWs containing 16,230 forms and 310,519 data elements. Automatic updates of metadata are possible as well as semantic annotations. A CMDW allows metadata discovery, data quality assessment and similarity analyses. Common data models for distributed research networks can be established based on similarity analyses.

The ongoing fragmentation of medical care and mobility of patients severely restrains exchange of lifesaving information about patient's medical history in case of emergencies. Therefore, the objective of this work is to offer a secure technical solution to supply medical professionals with emergency-relevant information concerning the current patient via mobile accessibility. To achieve this goal, the official national emergency data set was extended by additional features to form a patient summary for emergencies, a software architecture was developed and data security and data protection issues were taken into account. The patient has sovereignty over his/her data and can therefore decide who has access to or can change his/her stored data, but the treating physician composes the validated dataset. Building upon the introduced concept, future activities are the development of user-interfaces for the software components of the different user groups as well as functioning prototypes for upcoming field tests.

The increasing use of electronic health information systems brings up an unresolved issue: the lack of interoperability between these systems. The Clinical Data Interchange Standards Consortium's Operational Data Model (ODM) is an xml based standard for the exchange of clinical data and metadata. The University of Münster has been using ODM to store medical forms in a web based metadata registry called Portal of Medical Data Models, which includes a complete set of tools to transform ODM forms into other formats. One kind of medical form is the Patient Reported Outcome, a trending type due to its easy integration with mobile data capture systems. ResearchKit is a development framework that allows the easy creation of these for iOS devices; unfortunately its current interoperability is limited. This research proposes a mapping between ODM and ResearchKit and presents the successful implementation of a converter for ODM into JSON based ResearchKit readable files.

To address current key problems of medical documentation: lack of transparency, overwhelming amount of medical contents to be documented and missing interoperability, the Portal of Medical Data Models (http://medical-data-models.org/) was established in 2012. Constantly evolving, four years later, the portal displays more than 8900 medical data models with more than 250000 items, of which 84 % have been semantically annotated with UMLS codes to support interoperability. Giving an update on new functions and contents of the portal, two additional export formats have been implemented, allowing the reuse of forms such as HL7's framework Fast Health Interoperability Resources (FHIR) Questionnaires, as well as the OpenDataKit format. Future projects include the implementation of an ODMtoOpenClinica converter, as well as supporting the reuse of forms with Apple's ResearchKit and Android's ResearchStack.

Due to the increasing use of electronic data capture systems for clinical research, the interest in saving resources by automatically generating and reusing case report forms in clinical studies is growing. OpenClinica, an open-source electronic data capture system enables the reuse of metadata in its own Excel import template, hampering the reuse of metadata defined in other standard formats. One of these standard formats is the Operational Data Model for metadata, administrative and clinical data in clinical studies. This work suggests a mapping from Operational Data Model to OpenClinica and describes the implementation of a converter to automatically generate OpenClinica conform case report forms based upon metadata in the Operational Data Model.

Electronic collection and high quality analysis of medical data is expected to have a big potential to improve patient care and medical research. However, the integration of data from different stake holders is posing a crucial problem. The exchange and reuse of medical data models as well as annotations with unique semantic identifiers were proposed as a solution.Convert metadata from the Study of Health in Pomerania to the standardized CDISC ODM format.The structure of the two data formats is analyzed and a mapping is suggested and implemented.The metadata from the Study of Health in Pomerania was successfully converted to ODM. All relevant information was included in the resulting forms. Three sample forms were evaluated in-depth, which demonstrates the feasibility of this conversion.Hundreds of data entry forms with more than 15.000 items can be converted into a standardized format with some limitations, e.g. regarding logical constraints. This enables the integration of the Study of Health in Pomerania metadata into various systems, facilitating the implementation and reuse in different study sites.

Interoperability is one of the biggest issues in health informatics despite of the huge effort invested to solve it. Clinical Data Interchange Standards Consortium (CDISC) and Health Level 7 (HL7) are two of the most recognized institutions working on this field. Several systems are becoming compliant with their standards; however, the process to accomplish it is not always straightforward. In this manuscript, we present the successful implementation of the CDISC ODM and HL7 import and export functions for "MoPat", a web-based multi-language electronic patient-reported outcomes system. The system has been evaluated and tested and is currently being used for clinical study and routine data collection, including more than 10.000 patient encounters.

Interoperability between systems and data sharing between domains is becoming more and more important. The portal medical-data-models.org offers more than 5.300 UMLS annotated forms in CDISC ODM format in order to support interoperability, but several additional export formats are available. CDISC's ODM and HL7's framework FHIR Questionnaire resource were analyzed, a mapping between elements created and a converter implemented. The developed converter was integrated into the portal with FHIR Questionnaire XML or JSON download options. New FHIR applications can now use this large library of forms.

Data standards consisting of key data elements for clinical routine and trial documentation harmonize documentation within and across different health care institutions making documentation more efficient and improving scientific data analysis. This work focusses on the field of myeloid leukemia (ML), where a semantic core of common data elements (CDEs) in routine and trial documentation is established by automatic UMLS-based form analysis of existing documentation models. These CDEs (n = 227) were initially reviewed and commented by leukemia experts before they were systematically surveyed by an international voting process through seven hematologists of four countries. The total agreement score was 86%. 116 elements (51%) of these share an agreement score of 100%. This work generated CDEs with language-independent semantic codes and international clinical expert review to build a first approach towards an international data standard for ML. A first version of the CDE list is implemented in the data standard Operational Data Model and additional other data formats for reuse in different medical information systems.

Standardized quality assurance (QA) plays an import role to maintain and develop success of cardiovascular procedures (CP). Well-established QA models from Germany could be shared in a form repository for world-wide reuse and exchange. Therefore, we collected the complete set of all quality QA forms for CP, which is obligatory to be filled out by all German health service providers. Original forms were converted into standardized study forms according to ODM (Operational Data Model) and translated into English. Common medical concepts and clusters of medical concepts were identified based on UMLS coding of form items. All forms are available on the web as multilingual ODM documents. UMLS concept coverage analysis indicates 88% coverage with few but critically important definition gaps, which need to be addressed by UMLS.

With the growing complexity and cost of clinical trials (CTs) over the past few decades, Protocol Feasibility (PF) studies have become one of the most critical CT steps in order to avoid costly protocol amendments and ensure the success of CTs. The PF process includes interaction with clinicians located at targeted clinical sites, which results in slow and cumbersome process steps. These process steps are normally supported by information systems that allow users to create, share and collect responses to feasibility questionnaires. This investigation analyzes the systems and questionnaires utilized at several clinical research companies for PF. In addition, it provides recommendations that could eventually improve current methods and systems in place.

Automatic coding of medical terms is an important, but highly complicated and laborious task.To compare and evaluate different strategies a framework with a standardized web-interface was created. Two UMLS mapping strategies are compared to demonstrate the interface.The framework is a Java Spring application running on a Tomcat application server. It accepts different parameters and returns results in JSON format. To demonstrate the framework, a list of medical data items was mapped by two different methods: similarity search in a large table of terminology codes versus search in a manually curated repository. These mappings were reviewed by a specialist.The evaluation shows that the framework is flexible (due to standardized interfaces like HTTP and JSON), performant and reliable. Accuracy of automatically assigned codes is limited (up to 40%).Combining different semantic mappers into a standardized Web-API is feasible. This framework can be easily enhanced due to its modular design.

The design of clinical trial (CT) study protocols, currently supported by clinicians, is often a slow and cumbersome process. The Electronic Health Records for Clinical Research (EHR4CR) project supports the design of study protocols through a multi-site patient count cohort system. However, there is still a need to improve the process step in which the clinicians are involved. This research aims to enhance the EHR4CR platform with a tool to support the contact of CT sponsors with clinical investigators to obtain their input regarding feasibility data for the CT protocol design. From a list of requirements, a technical architecture that responds to the needs of feasibility assessments was modelled. With this architecture as a basis, a system that allows users to generate, send, fill out and visualise results of feasibility questionnaires across clinical sites was developed and integrated within the EHR4CR platform. The resulting system improves the current methods by providing direct contact to clinical investigators, facilitating the creation and answer of feasibility questionnaires for CTs.

Quality assurance (QA) is a key factor to evaluate success of organ transplantations. In Germany QA documentation is progressively developed and enforced by law. Our objective is to share QA models from Germany in a standardized format within a form repository for world-wide reuse and exchange. Original QA forms were converted into standardized study forms according to the Operational Data Model (ODM) and shared for open access in an international forms repository. Form elements were translated into English and semantically enriched with Concept Unique Identifiers from the Unified Medical Language System (UMLS) based on medical expert decision. All forms are available on the web as multilingual ODM documents. UMLS concept coverage analysis indicates 92% coverage with few but critically important definition gaps. New content and infrastructure for harmonized documentation forms is provided in the domain of organ transplantations enabling world-wide reuse and exchange.

In the last few years much work has been conducted in creating systems that support clinical trials for example by utilizing electronic health record data. One of these endeavours is the Electronic Health Record for Clinical Research project (EHR4CR). An unanswered question that the project aims to answer is which data elements are most commonly required for patient recruitment.Free text eligibility criteria from 40 studies were analysed, simplified and elements were extracted. These elements where then added to an existing inventory of data elements for protocol feasibility.We simplified and extracted data elements from 40 trials, which resulted in 1170 elements. From these we created an inventory of 150 unique data elements relevant for patient identification and recruitment with definitions and referenced codes to standard terminologies.Our list was created with expertise from pharmaceutical companies. Comparisons with related work shows that identified concepts are similar. An evaluation of the availability of these elements in electronic health records is still ongoing. Hospitals that want to engage in re-use of electronic health record data for research purposes, for example by joining networks like EHR4CR, can now prioritize their effort based on this list.

Gathering patient-reported data enables physicians to know a patient and his/her health-related needs in a more comprehensive way. One method to collect these data is to let the patient fill out electronic questionnaires on a mobile device, e.g. a tablet. Although having successfully implemented a prototype, the mobile questionnaire system at this site has been stretched to its limits within both routine care and clinical trials. By collecting user feedback and analysing the capabilities and limits of the underlying framework, we identified key issues of the prototype. A new implementation addresses these obstacles while keeping the overall application logic and usability. This leads to a trial-ready mobile patient questionnaire system.

The Electronic Health Record for Clinical Research (EHR4CR) project aims to improve the current process of clinical trials, providing a technological platform that supports the design and execution of trials. For the protocol feasibility scenario, the system currently allows the user to create a set of in-/exclusion criteria to find patients matching these criteria across sites located in several countries. The automated multi-country patient cohort system developed in EHR4CR implies substantial changes on the current protocol feasibility process, which will be reflected in this study.

Electronic health care records are being used more and more for patient documentation. This electronic data can be used for secondary purposes, for example through systems that support clinical research. Eligibility criteria have to be processable for such systems to work, but criteria published on ClinicalTrials.gov have been shown to be complex, making them challenging to re-use. We analysed the eligibility criteria on ClinicalTrials.gov using automatic methods to determine whether the criteria definition and number changed over time. From 1998 to 2012 the average number of words used to describe eligibility criteria per year increased by 46%, while the average number of lines used per year only slightly increases until 2000 and stabilizes afterwards. Whether the increase of words resulted in increased criteria complexity or whether more data elements are used to describe eligibility needs further investigation.

Real world data is crucial for clinical research. In hospitals this information is more and more stored in electronic health records (EHR) and could be used for clinical trial feasibility and patient recruitment. On the other side eligibility criteria within study protocols are complex and usually not written in a way that is intuitively understandable or electronically usable. As part of overcoming the gap between eligibility criteria and the data that is available in EHRs we simplified eligibility criteria and created a guideline with best practice principles. The guideline explains how understandable criteria should be formulated and illustrates with examples what 'good' and 'bad' criteria are.

In clinical trials (CTs), the process of patient recruitment (PR) is one of the main risk factors, as almost half of all trial delays are caused by problems in PR. To our knowledge, no publication in this field describes the process of PR. Therefore, weak spots and potential benefits cannot be identified. By interviewing six domain experts and modeling the workflow in a standardized way, we describe the actors, tasks and tools within PR. We compare the current workflow with Patient Recruitment System (PRS)-supported PR. The identification of eligible participants is the most complex part, but adding a PRS simplifies it by automating repetitive tasks and taking work off the Investigators' hands. This work contributes to a common understanding of the PR process.

One year ago the portal of Medical Data Models (http://medical-data-models.org) was presented as a resource for the scientific community. As of November 2012 there are approximately 3,300 forms with 102,000 items available in the CDISC ODM format. First descriptive analyses regarding form metadata demonstrate the capability of such a repository to identify commonly used medical concepts. Most common items are administrative attributes which indicates that more clinical information are needed to increase the secondary use of data documented within these forms.

Clinical phenotypes are very complex and not well described. For instance, more than 100.000 biomedical concepts are needed to describe clinical properties of patients. At present, information systems dealing with clinical phenotype data are based on secret, heterogeneous and incompatible data models. This is the root cause for the well-known grand challenge of semantic interoperability in healthcare: data exchange and analysis of medical information systems has major limitations. This problem slows down medical progressand wastes time of health care professionals. A large-scale open metadata initiative can foster exchange, discussion and consensus regarding data models for clinical phenotypes. This would be an important contribution to improve information systems in healthcare and to solve the grand challenge of semantic interoperability.

An essential step when recruiting patients for a Clinical Trial (CT) is to determine the number of patients that satisfy the Eligibility Criteria (ECs) for that trial. An innovative feature of the Electronic Health Records for Clinical Research (EHR4CR) platform is that when automatically determining patient counts, it also allows the user to view counts for subsets of the ECs. This is helpful because some combinations of ECs may be so restrictive that they yield very few or zero patients. If we wanted to show all possible combinations of ECs, the number of queries we would have to execute would be of 2supn/sup, where n is the total number of ECs. Assuming that an average study has between 20 and 30 ECs, the program would have to execute between 2sup20/sup(1,048,576) and 2sup30/sup(1,073,741,824) queries. This is not only computationally expensive but also impractical to visualise. The purpose of our research is to reduce possible combinationsto a manageable number.

In addition to clinical findings, patient reported outcomes (PRO) are a valuable source of information. However, the available time of a physician per patient is limited. Under these constraints, do physicians look into PRO data? By using an, electronic health record (EHR) integrated system for the documentation of PRO the data is directly available during treatment. To evaluate whether this information is used we analyzed access patterns for two types of PRO forms. 56% and 74% of these forms were accessed within routine care while 74% and 100% were analyzed for clinical research.

Planning case report forms for data capture in clinical trials is a labor-insensitive and not formalized process. These CRFs are often neither standardized nor using defined data elements. Metadata registries as the NCI caDSR provide the capability to create forms based on common data elements. However, an exchange of these forms into clinical trial management systems through a standardized format like CDISC ODM is currently not offered. Thus, our objectives were to develop a mapping model between NCI forms and ODM. We analyzed 3012 NCI forms and included common data elements regarding their frequency and uniqueness. In this paper, we have created a mapping model between both formats and identified limitations in the conversion process: Semantic codes requested from the caDSR registry did not allow a proper mapping to ODM items and information like the number of module repetitions got lost. Summarized, it can be stated that our mapping model is feasible. However, mapping of semantic concepts in ODM needs to be specified more precisely.

Semantic interoperability is an unsolved problem which occurs while working with medical forms from different information systems or institutions. Standards like ODM or CDA assure structural homogenization but in order to compare elements from different data models it is necessary to use semantic concepts and codes on an item level of those structures. We developed and implemented a web-based tool which enables a domain expert to perform semi-automated coding of ODM-files. For each item it is possible to inquire web services which result in unique concept codes without leaving the context of the document. Although it was not feasible to perform a totally automated coding we have implemented a dialog based method to perform an efficient coding of all data elements in the context of the whole document. The proportion of codable items was comparable to results from previous studies.