Comparison of Contemporary Risk Scores in All Groups of Pulmonary Hypertension: A Pulmonary Vascular Research Institute GoDeep Meta-Registry Analysis

BACKGROUND

Pulmonary hypertension (PH) is a heterogeneous disease with a poor prognosis. Accurate risk stratification is essential for guiding treatment decisions in pulmonary arterial hypertension (PAH). Although various risk models have been developed for PAH, their comparative prognostic potential requires further exploration. Additionally, the applicability of risk scores in PH groups beyond group 1 remains to be investigated.

RESEARCH QUESTION

Are risk scores originally developed for PAH predictive in PH groups 1 through 4?

STUDY DESIGN AND METHODS

We conducted a comprehensive analysis of outcomes among patients with incident PH enrolled in the multicenter worldwide Pulmonary Vascular Research Institute GoDeep meta-registry. Analyses were performed across PH groups 1 through 4 and further subgroups to evaluate the predictive value of PAH risk scores, including REVEAL Lite 2, REVEAL 2.0, ESC/ERS 2022, COMPERA 3-strata, and COMPERA 4-strata.

RESULTS

Eight thousand five hundred sixty-five patients were included in the study, of whom 3,537 patients were assigned to group 1 PH, whereas 1,807 patients, 1,635 patients, and 1,586 patients were assigned to group 2 PH, group 3 PH, and group 4 PH, respectively. Pulmonary hemodynamics were impaired with median mean pulmonary arterial pressure of 42 mm Hg (33-52 mm Hg) and pulmonary vascular resistance of 7 WU (4-11 WU). All risk scores were prognostic in the entire PH population and in each of the PH groups 1 through 4. The REVEAL scores, when used as continuous prediction models, demonstrated the highest statistical prognostic power and granularity; the COMPERA 4-strata risk score provided subdifferentiation of the intermediate-risk group. Similar results were obtained when separately analyzing various subgroups (PH subgroups 1.1, 1.4.1, and 1.4.4; PH subgroups 3.1 and 3.2; group 2 with isolated postcapillary PH vs combined precapillary and postcapillary PH; patients of all groups with concomitant cardiac comorbidities; and severe [> 5 WU] vs nonsevere PH).

INTERPRETATION

This comprehensive study with real-world data from 15 PH centers showed that PAH-designed risk scores possess predictive power in a large PH cohort, whether considered as common to the group or calculated separately for each PH group (1-4) and various subgroups.

Definition, Composition, and Harmonization of Core Datasets Within the German Center for Lung Research

Core datasets are the composition of essential data items for a certain research scope. As they state commonalities between heterogeneous data collections, they serve as a basis for cross-site and cross-disease research. Therefore, researchers at the national and international levels have addressed the problem of missing core datasets. The German Center for Lung Research (DZL) comprises five sites and eight disease areas and aims to gain further scientific knowledge by continuously promoting collaborations. In this study, we elaborated a methodology for defining core datasets in the field of lung health science. Additionally, through support of domain experts, we have utilized our method and compiled core datasets for each DZL disease area and a general core dataset for lung research. All included data items were annotated with metadata and where possible they were assigned references to international classification systems. Our findings will support future scientific collaborations and meaningful data collections.

Modeling Clinical Guidelines for an Epilepsy-CDSS: The EDiTh Project

The knowledge transformation process involves the guideline for the diagnosis and therapy of epilepsy to an executable and computable knowledge base that serves as the basis for a decision-support system. We present a transparent knowledge representation model which facilitates technical implementation and verification. Knowledge is represented in a plain table, used in the frontend code of the software where simple reasoning is performed. The simple structure is sufficient and comprehensible also for non-technical persons (i.e., clinicians).

Local Approval Processes in a Federated and Distributed Research Infrastructure - Lessons Learned from the AKTIN-Project

The AKTIN-Emergency Department Registry is a federated and distributed health data network which uses a two-step process for local approval of received data queries and result transmission. For currently establishing distributed research infrastructures, we present our lessons learned from 5 years of established operations.

Comparison of the sensitivity of different criteria to select lung cancer patients for screening in a cohort of German patients

INTRODUCTION

Trials of CT-based screening for lung cancer have shown a mortality advantage for screening in North America and Europe. Before introducing a nationwide lung cancer screening program in Germany, it is important to assess the criteria used in international trials in the German population.

METHODS

We used data from 3623 lung cancer patients from the data warehouse of the German Center for Lung Research (DZL). We compared the sensitivity of the following lung cancer screening criteria overall and stratified by age and histology: the National Lung Screening Trial (NLST), the Danish Lung Cancer Screening Trial (DLCST), the 2013 and 2021 US Preventive Services Task Force (USPSTF), and an adapted version of the Prostate, Lung, Colorectal, and Ovarian no race model (adapted PLCOm2012) with 6-year risk thresholds of 1.0%/6 year and 1.7%/6 year.

RESULTS

Overall, the adapted PLCOm2012 model (1%/6 years), selected the highest proportion of lung cancer patients for screening (72.4%), followed by the 2021 USPSTF (70.0%), the adapted PLCOm2012 (1.7%/6 year) (57.4%), the 2013 USPTF (57.0%), DLCST criteria (48.7%), and the NLST (48.5%). The adapted PLCOm2012 risk model (1.0%/6 year) had the highest sensitivity for all histological types except for small-cell and large-cell carcinomas (non-significant), whereas the 2021 USPTF selected a higher proportion of patients. The sensitivity levels were higher in males than in females.

CONCLUSION

Using a risk-based selection score resulted in higher sensitivities compared to criteria using dichotomized age and smoking history. However, gender disparities were apparent in all studied eligibility criteria. In light of increasing lung cancer incidences in women, all selection criteria should be reviewed for ways to close this gender gap, especially when implementing a large-scale lung cancer screening program.

Pulmonary Vascular Research Institute GoDeep: A meta-registry merging deep phenotyping datafrom international PH reference centers

The Pulmonary Vascular Research Institute GoDeep meta-registry is a collaboration of pulmonary hypertension (PH) reference centers across the globe. Merging worldwide PH data in a central meta-registry to allow advanced analysis of the heterogeneity of PH and its groups/subgroups on a worldwide geographical, ethnical, and etiological landscape (ClinTrial. gov NCT05329714). Retrospective and prospective PH patient data (diagnosis based on catheterization; individuals with exclusion of PH are included as a comparator group) are mapped to a common clinical parameter set of more than 350 items, anonymized and electronically exported to a central server. Use and access is decided by the GoDeep steering board, where each center has one vote. As of April 2022, GoDeep comprised 15,742 individuals with 1.9 million data points from eight PH centers. Geographic distribution comprises 3990 enrollees (25%) from America and 11,752 (75%) from Europe. Eighty-nine perecent were diagnosed with PH and 11% were classified as not PH and provided a comparator group. The retrospective observation period is an average of 3.5 years (standard error of the mean 0.04), with 1159 PH patients followed for over 10 years. Pulmonary arterial hypertension represents the largest PH group (42.6%), followed by Group 2 (21.7%), Group 3 (17.3%), Group 4 (15.2%), and Group 5 (3.3%). The age distribution spans several decades, with patients 60 years or older comprising 60%. The majority of patients met an intermediate risk profile upon diagnosis. Data entry from a further six centers is ongoing, and negotiations with >10 centers worldwide have commenced. Using electronic interface-based automated retrospective and prospective data transfer, GoDeep aims to provide in-depth epidemiological and etiological understanding of PH and its various groups/subgroups on a global scale, offering insights for improved management.

Establishing a Data Quality Baseline in the AKTIN Emergency Department Data Registry - A Secondary Use Perspective

Secondary use of clinical data is an increasing application that is affected by the data quality (DQ) of its source systems. Techniques such as audits and risk-based monitoring for controlling DQ often rely on source data verification (SDV). SDV requires access to data generating systems. We present an approach to a targeted SDV based on manual input and synthetic data that is applicable in low resource settings with restricted system access. We deployed the protocol in the DQ management of the AKTIN Emergency Department Data Registry. Our targeted approach has shown to be feasible to form a DQ baseline that can be used for different DQ monitoring processes such as the identification of different error sources.

A Federated and Distributed Data Management Infrastructure to Enable Public Health Surveillance from Intensive Care Unit Data

The Robert Koch Institute (RKI) monitors the actual number of COVID-19 patients requiring intensive care from aggregated data reported by hospitals in Germany. So far, there is no infrastructure to make use of individual patient-level data from intensive care units for public health surveillance. Adopting concepts and components of the already established AKTIN Emergency Department Data registry, we implemented the prototype of a federated and distributed research infrastructure giving the RKI access to patient-level intensive care data.

Architecture for a feasibility query portal for distributed COVID-19 Fast Healthcare Interoperability Resources (FHIR) patient data repositories: Design and Implementation Study (Preprint)

Background

An essential step in any medical research project after identifying the research question is to determine if there are sufficient patients available for a study and where to find them. Pursuing digital feasibility queries on available patient data registries has proven to be an excellent way of reusing existing real-world data sources. To support multicentric research, these feasibility queries should be designed and implemented to run across multiple sites and securely access local data. Working across hospitals usually involves working with different data formats and vocabularies. Recently, the Fast Healthcare Interoperability Resources (FHIR) standard was developed by Health Level Seven to address this concern and describe patient data in a standardized format. The Medical Informatics Initiative in Germany has committed to this standard and created data integration centers, which convert existing data into the FHIR format at each hospital. This partially solves the interoperability problem; however, a distributed feasibility query platform for the FHIR standard is still missing.

Objective

This study described the design and implementation of the components involved in creating a cross-hospital feasibility query platform for researchers based on FHIR resources. This effort was part of a large COVID-19 data exchange platform and was designed to be scalable for a broad range of patient data.

Methods

We analyzed and designed the abstract components necessary for a distributed feasibility query. This included a user interface for creating the query, backend with an ontology and terminology service, middleware for query distribution, and FHIR feasibility query execution service.

Results

We implemented the components described in the Methods section. The resulting solution was distributed to 33 German university hospitals. The functionality of the comprehensive network infrastructure was demonstrated using a test data set based on the German Corona Consensus Data Set. A performance test using specifically created synthetic data revealed the applicability of our solution to data sets containing millions of FHIR resources. The solution can be easily deployed across hospitals and supports feasibility queries, combining multiple inclusion and exclusion criteria using standard Health Level Seven query languages such as Clinical Quality Language and FHIR Search. Developing a platform based on multiple microservices allowed us to create an extendable platform and support multiple Health Level Seven query languages and middleware components to allow integration with future directions of the Medical Informatics Initiative.

Conclusions

We designed and implemented a feasibility platform for distributed feasibility queries, which works directly on FHIR-formatted data and distributed it across 33 university hospitals in Germany. We showed that developing a feasibility platform directly on the FHIR standard is feasible.

Understanding the Nature of Metadata: Systematic Review

Background

Metadata are created to describe the corresponding data in a detailed and unambiguous way and is used for various applications in different research areas, for example, data identification and classification. However, a clear definition of metadata is crucial for further use. Unfortunately, extensive experience with the processing and management of metadata has shown that the term “metadata” and its use is not always unambiguous.

Objective

This study aimed to understand the definition of metadata and the challenges resulting from metadata reuse.

Methods

A systematic literature search was performed in this study following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting on systematic reviews. Five research questions were identified to streamline the review process, addressing metadata characteristics, metadata standards, use cases, and problems encountered. This review was preceded by a harmonization process to achieve a general understanding of the terms used.

Results

The harmonization process resulted in a clear set of definitions for metadata processing focusing on data integration. The following literature review was conducted by 10 reviewers with different backgrounds and using the harmonized definitions. This study included 81 peer-reviewed papers from the last decade after applying various filtering steps to identify the most relevant papers. The 5 research questions could be answered, resulting in a broad overview of the standards, use cases, problems, and corresponding solutions for the application of metadata in different research areas.

Conclusions

Metadata can be a powerful tool for identifying, describing, and processing information, but its meaningful creation is costly and challenging. This review process uncovered many standards, use cases, problems, and solutions for dealing with metadata. The presented harmonized definitions and the new schema have the potential to improve the classification and generation of metadata by creating a shared understanding of metadata and its context.

Generation of a Fast Healthcare Interoperability Resources (FHIR)-based Ontology for federated Feasibility Queries in the context of COVID-19: An automated approach (Preprint)

Background

The COVID-19 pandemic highlighted the importance of making research data from all German hospitals available to scientists to respond to current and future pandemics promptly. The heterogeneous data originating from proprietary systems at hospitals' sites must be harmonized and accessible. The German Corona Consensus Dataset (GECCO) specifies how data for COVID-19 patients will be standardized in Fast Healthcare Interoperability Resources (FHIR) profiles across German hospitals. However, given the complexity of the FHIR standard, the data harmonization is not sufficient to make the data accessible. A simplified visual representation is needed to reduce the technical burden, while allowing feasibility queries.

Objective

This study investigates how a search ontology can be automatically generated using FHIR profiles and a terminology server. Furthermore, it describes how this ontology can be used in a user interface (UI) and how a mapping and a terminology tree created together with the ontology can translate user input into FHIR queries.

Methods

We used the FHIR profiles from the GECCO data set combined with a terminology server to generate an ontology and the required mapping files for the translation. We analyzed the profiles and identified search criteria for the visual representation. In this process, we reduced the complex profiles to code value pairs for improved usability. We enriched our ontology with the necessary information to display it in a UI. We also developed an intermediate query language to transform the queries from the UI to federated FHIR requests. Separation of concerns resulted in discrepancies between the criteria used in the intermediate query format and the target query language. Therefore, a mapping was created to reintroduce all information relevant for creating the query in its target language. Further, we generated a tree representation of the ontology hierarchy, which allows resolving child concepts in the process.

Results

In the scope of this project, 82 (99%) of 83 elements defined in the GECCO profile were successfully implemented. We verified our solution based on an independently developed test patient. A discrepancy between the test data and the criteria was found in 6 cases due to different versions used to generate the test data and the UI profiles, the support for specific code systems, and the evaluation of postcoordinated Systematized Nomenclature of Medicine (SNOMED) codes. Our results highlight the need for governance mechanisms for version changes, concept mapping between values from different code systems encoding the same concept, and support for different unit dimensions.

Conclusions

We developed an automatic process to generate ontology and mapping files for FHIR-formatted data. Our tests found that this process works for most of our chosen FHIR profile criteria. The process established here works directly with FHIR profiles and a terminology server, making it extendable to other FHIR profiles and demonstrating that automatic ontology generation on FHIR profiles is feasible.

The Collaborative Metadata Repository (CoMetaR) Web App: Quantitative and Qualitative Usability Evaluation

Background

In the field of medicine and medical informatics, the importance of comprehensive metadata has long been recognized, and the composition of metadata has become its own field of profession and research. To ensure sustainable and meaningful metadata are maintained, standards and guidelines such as the FAIR (Findability, Accessibility, Interoperability, Reusability) principles have been published. The compilation and maintenance of metadata is performed by field experts supported by metadata management apps. The usability of these apps, for example, in terms of ease of use, efficiency, and error tolerance, crucially determines their benefit to those interested in the data.

Objective

This study aims to provide a metadata management app with high usability that assists scientists in compiling and using rich metadata. We aim to evaluate our recently developed interactive web app for our collaborative metadata repository (CoMetaR). This study reflects how real users perceive the app by assessing usability scores and explicit usability issues.

Methods

We evaluated the CoMetaR web app by measuring the usability of 3 modules: core module, provenance module, and data integration module. We defined 10 tasks in which users must acquire information specific to their user role. The participants were asked to complete the tasks in a live web meeting. We used the System Usability Scale questionnaire to measure the usability of the app. For qualitative analysis, we applied a modified think aloud method with the following thematic analysis and categorization into the ISO 9241-110 usability categories.

Results

A total of 12 individuals participated in the study. We found that over 97% (85/88) of all the tasks were completed successfully. We measured usability scores of 81, 81, and 72 for the 3 evaluated modules. The qualitative analysis resulted in 24 issues with the app.

Conclusions

A usability score of 81 implies very good usability for the 2 modules, whereas a usability score of 72 still indicates acceptable usability for the third module. We identified 24 issues that serve as starting points for further development. Our method proved to be effective and efficient in terms of effort and outcome. It can be adapted to evaluate apps within the medical informatics field and potentially beyond.

Der AKTIN-Monatsbericht: Plädoyer für ein standardisiertes Reporting in der Notaufnahme

Hintergrund

Vor dem Hintergrund der steigenden Inanspruchnahme und aktuellen Veränderungen in der Notfallversorgung ist eine standardisierte Erfassung relevanter Kennzahlen in Notaufnahmen zwingend erforderlich.

Ziel der Arbeit

Es werden die Konsentierung von Inhalten und technische Umsetzung eines automatisierten Reportings für Notaufnahmen des AKTIN-Notaufnahmeregisters beschrieben. Ziel war ein aussagefähiger Monatsbericht zur Prozesssteuerung und Qualitätssicherung.

Material und Methoden

Datengrundlage ist der Datensatz Notaufnahme V2015.1 der Deutschen Interdisziplinären Vereinigung für Intensiv- und Notfallmedizin e. V. (DIVI). Die Konsentierung der Inhalte erfolgte auf Basis von externen Referenzen und fachlicher Expertise; die technische Entwicklung erfolgte anhand eines Testdatensatzes. Mit der Software R und Apache Formatting Objects Processor (FOP) wird der finale Bericht im PDF-Format automatisiert erstellt.

Ergebnisse

Der Bericht enthält unter anderem Angaben zu Fallzahlen, Demografie der Patienten, Vorstellungsgründen, Ersteinschätzung, Verbleib und ausgewählten Prozesszeiten in Form von Tabellen und Grafiken. Er wird monatsweise automatisch oder auf Anforderung aus den Routinedaten generiert. Fehlende Werte und Ausreißer werden zur Abschätzung der Datenqualität separat ausgewiesen.

Diskussion

Beim AKTIN-Monatsbericht handelt es sich um ein Instrument, welches das Versorgungsgeschehen aufbereitet und visualisiert. Die konsentierten Kennzahlen sind praxistauglich und bilden auch die Vorgaben des Gemeinsamen Bundesausschusses zur Ersteinschätzung ab. Die Nutzung von Interoperabilitätsstandards erlaubt eine automatische Erfassung im Alltag, gewährleistet eine Unabhängigkeit von einzelnen IT-Systemen und kann als Grundlage für ein klinikübergreifendes Benchmarking dienen.

HIStream-Import: A Generic ETL Framework for Processing Arbitrary Patient Data Collections or Hospital Information Systems into HL7 FHIR Bundles

Data integration is a necessary and important step to perform translational research and improve the sample size beyond single data collections. For health information, the most recent established communication standards is HL7 FHIR. To bridge the concepts of "minimal invasive" data integration and open standards, we propose a generic ETL framework to process arbitrary patient related data collections into HL7 FHIR - which in turn can then be used for loading into target data warehouses. The proposed algorithm is able to read any relational delimited text exports and produce a standard HL7 FHIR bundle collection. We evaluated an implementation of the algorithm using different lung research registries and used the resulting FHIR resources to fill our i2b2 based data warehouse as well an OMOP common data model repository.

[Cooperative approach of pathology and neuropathology in the COVID-19 pandemic : German registry for COVID-19 autopsies (DeRegCOVID) and German network for autopsies in pandemics (DEFEAT PANDEMIcs)]

Hintergrund

Die Obduktion ist ein wichtiges Instrument zum Verständnis der Pathogenese von Krankheiten, inklusive COVID-19.

Material und Methoden

Am 15.04.2020 wurde zusammen mit der Deutschen Gesellschaft für Pathologie und dem Bundesverband der deutschen Pathologen das Deutsche Register von COVID-19-Obduktionen (DeRegCOVID) gestartet (www.DeRegCOVID.ukaachen.de) und darauf aufbauend seit 01.09.2020 das Deutsche Netzwerk für Autopsien in Pandemien (DEFEAT PANDEMIcs) etabliert.

Ergebnisse

Hauptziel des DeRegCOVID ist es, faktisch anonymisierte Daten über idealerweise alle Obduktionen von COVID-19-Verstorbenen in Deutschland zu sammeln und zur Verfügung zu stellen, um dem Bedarf an zentralisierter, koordinierter und strukturierter Datenerhebung und Berichterstattung in der Pandemie gerecht zu werden. Der Erfolg des Registers hängt von der Bereitschaft der jeweiligen Zentren zur Meldung der Daten ab. Diese hat sich bislang sehr positiv entwickelt und wir danken allen beteiligten Zentren. Dabei verbleiben die Rechte an eigenen Daten (und dezentral verbleibenden Biomaterialen) bei den jeweiligen Institutionen. Das Netzwerk DEFEAT PANDEMIcs zielt auf eine Verstärkung der Harmonisierung und Standardisierung sowie die bundesweite Implementation und Kooperation im Bereich von Pandemieobduktionen.

Schlussfolgerungen

Die außerordentliche Kooperation in Deutschland im Bereich der Obduktionen während der COVID-19-Pandemie ist durch den Aufbau des DeRegCOVID, dessen Fusionierung mit dem Register der Neuropathologie (CNS-COVID19) und dem Aufbau des Netzwerks DEFEAT PANDEMIcs eindrücklich belegt. Es ist ein starkes Signal für die Notwendigkeit, Bereitschaft und Expertise, mit durch Autopsie gewonnenen Erkenntnissen zur gemeinsamen Bewältigung gegenwärtiger und künftiger Pandemien beizutragen.

[AKTIN - The German Emergency Department Data Registry - real-time data from emergency medicine : Implementation and first results from 15 emergency departments with focus on Federal Joint Committee's guidelines on acuity assessment]

Hintergrund

Die Notfallversorgung befindet sich im Umbruch. In Notaufnahmen werden Patienten ausgehend von Symptomen und Dringlichkeit versorgt; dies bildet sich jedoch in den etablierten Routinedaten der gesetzlichen Sozialversicherung nicht ab. Ziel des AKTIN-Projekts war der Aufbau einer datenschutzkonformen Registerinfrastruktur zur Nutzung von klinischen Routinedaten aus Notaufnahmen.

Methoden

Über eine standardisierte Schnittstelle werden aus den verschiedenen Dokumentationssystemen kontinuierlich Daten der Notaufnahmebehandlung in ein lokales Data Warehouse exportiert. Dort stehen sie sowohl für lokale Nutzungen, wie interne Berichte und Qualitätsmanagement, als auch gleichzeitig datenschutzkonform für multizentrische Auswertungen zur Verfügung.

Anhand der Registerpopulation wird die Ersteinschätzung sowie die Erhebung von Vitalparametern in Abhängigkeit von Vorstellungsgründen für einen 12-Monats-Zeitraum analysiert.

Ergebnisse

Für den Zeitraum 04/2018 bis 03/2019 wurden 436.149 gültige Fälle aus 15 Notaufnahmen übermittelt. In 86,0 % der Fälle ist eine Ersteinschätzung dokumentiert.

Diese fand in 70,5 % innerhalb von 10 min nach Ankunft des Patienten statt. In 10 Kliniken wird regelhaft (82,3 %) ein Vorstellungsgrund erfasst. Die Erfassung von Vitalparametern variiert plausibel zwischen den Vorstellungsgründen.

Schlussfolgerung

Das AKTIN-Notaufnahmeregister bietet einen zeitnahen Einblick in das Versorgungsgeschehen der Notaufnahmen ohne zusätzlichen Dokumentationsaufwand und unabhängig vom primären IT-System, Kostenträger, Fallart und Abrechnungsmodus. Die Vorgaben des Gemeinsamen Bundesausschusses zur Ersteinschätzung werden weitgehend umgesetzt. Durch die Etablierung von standardisierten Vorstellungsgründen werden symptombasierte Analysen und Gesundheitssurveillance ermöglicht.

Zusatzmaterial online

Die Onlineversion dieses Beitrags (10.1007/s00063-020-00764-2) enthält die Abb. S1 und S2. Beitrag und Zusatzmaterial stehen Ihnen auf www.springermedizin.de zur Verfügung. Bitte geben Sie dort den Beitragstitel in die Suche ein, das Zusatzmaterial finden Sie beim Beitrag unter „Ergänzende Inhalte“.

Clinical and Functional Characteristics of Patients with Unclassifiable Interstitial Lung Disease (uILD): Long-Term Follow-Up Data from European IPF Registry (eurIPFreg)

(1) Aim of the study: In spite of extensive research, up to 20% of interstitial lung diseases (ILD) patients cannot be safely classified. We analyzed clinical features, progression factors, and outcomes of unclassifiable ILD (uILD). (2) Methods: A total of 140 uILD subjects from the University of Giessen and Marburg Lung Center (UGMLC) were recruited between 11/2009 and 01/2019 into the European Registry for idiopathic pulmonary fibrosis (eurIPFreg) and followed until 01/2020. The diagnosis of uILD was applied only when a conclusive diagnosis could not be reached with certainty. (3) Results: In 46.4% of the patients, the uILD diagnosis was due to conflicting clinical, radiological, and pathological data. By applying the diagnostic criteria of usual interstitial pneumonia (UIP) based on computed tomography (CT), published by the Fleischner Society, 22.2% of the patients displayed a typical UIP pattern. We also showed that forced vital capacity (FVC) at baseline (p = 0.008), annual FVC decline ≥10% (p < 0.0001), smoking (p = 0.033), and a diffusing capacity of the lung for carbon monoxide (DLco) ≤55% of predicted value at baseline (p < 0.0001) were significantly associated with progressive disease. (4) Conclusions: The most important prognostic factors in uILD are baseline level and decline in lung function and smoking. The use of Fleischner diagnostic criteria allows further differentiation and accurate diagnosis.

Data Integration into OMOP CDM for Heterogeneous Clinical Data Collections via HL7 FHIR Bundles and XSLT

Data integration is an important task in medical informatics and highly impacts the gain out of existing health information data. These tasks are using implemented as extract transform and load processes. By introducing HL7 FHIR as an intermediate format, our aim was to integrate heterogeneous data from a German pulmonary hypertension registry into an OMOP Common Data Model. First, domain knowledge experts defined a common parameter set, which was subsequently mapped to standardized terminologies like LOINC or SNOMED-CT. Data was extracted as HL7 FHIR Bundle to be transformed to OMOP CDM by using XSLT. We successfully transformed the majority of data elements to the OMOP CDM in a feasible time.

Provenance for Biomedical Ontologies with RDF and Git

The German Center for Lung Research (DZL) is a research network with the aim of researching respiratory diseases. In order to enable consortium-wide retrospective research and prospective patient recruitment, we perform data integration into a central data warehouse. The enhancements of the underlying ontology is an ongoing process for which we developed the Collaborative Metadata Repository (CoMetaR) tool. Its technical infrastructure is based on the Resource Description Framework (RDF) for ontology representation and the distributed version control system Git for storage and versioning. Ontology development involves a considerable amount of data curation. Data provenance improves its feasibility and quality. Especially in collaborative metadata development, a comprehensive annotation about "who contributed what, when and why" is essential. Although RDF and Git versioning repositories are commonly used, no existing solution captures metadata provenance information in sufficient detail. We propose an enhanced composition of standardized RDF statements for detailed provenance representation. Additionally, we developed an algorithm that extracts and translates provenance data from the repository into the proposed RDF statements.

Verifying Data Integration Configurations for Semantical Correctness and Completeness

Data integration is the problem of combining data residing at different sources and providing the user with a unified view of these data. In medical informatics, such a unified view enables retrospective analyses based on more facts and prospective recruitment of more patients than any single data collection by itself. The technical part of data integration is based on rules interpreted by software. These rules define how to perform the translation of source database schemata into the target database schema. Translation rules are formulated by data managers who usually do not have the knowledge about meaning and acquisition methods of the data they handle. The professionals (data providers) collecting the source data who have the respective knowledge again usually have no sufficient technical background. Since data providers are neither able to formulate the transformation rules themselves nor able to validate them, the whole process is fault-prone. Additionally, in continuous development and maintenance of (meta-) data repositories, data structures underlie changes, which may lead to outdated transformation rules. We did not find any technical solution, which enables data providers to formulate transformation rules themselves or which provides an understandable reflection of given rules. Our approach is to enable data providers understand the rules regarding their own data by presenting rules and available context visually. Context information is fetched from a metadata repository. In this paper, we propose a software tool that builds on existing data integration infrastructures. The tool provides a visually supported validation routine for data integration rules. In a first step towards its evaluation, we implement the tool into the DZL data integration process and verify the correct presentation of transformation rules.

Implementation of Emergency Department Performance Benchmarking Using R and LaTeX

The German Emergency Department Data Registry (GEDD-registry, AKTIN) provides an infrastructure for collecting and querying up-to-date medical records in a distributed manner. Within this framework, a benchmark report on cross-institutional comparison using the program R is prepared using routine data of participating hospitals. Currently, 16 emergency departments (EDs) routinely transfer data of 1,200 to approximately 5,000 patients per month to a federated GEDD-registry datawarehouse. Using various packages in the R environment, hospitals receive a monthly visual report on their data among all participating hospitals. Graphical representations are implemented using column diagrams and box plots. Reports currently contain 25 tables and 40 graphs. Benchmark reports are created in R-Studio and exported using Portable Document Format, PDF. Quarterly expert meetings with the heads of participating EDs are currently performed for further improvements. Preparation of external benchmarking reports with R enables a detailed data presentation for participating hospitals and ED managers.

Data Discovery for Integration of Heterogeneous Medical Datasets in the German Center for Lung Research (DZL)

The German Centre for Lung Research (DZL) is an association of Germany's leading university and non-university institutions dedicated to lung research. Institutes and disease areas within the DZL manage their own data in several databases and registers using different software tools. Aim of our data integration effort is to provide a single central data warehouse frontend, where all patient related data is combined and made accessible. A two-stage survey was used to determine the data collections suitable for data integration. Integration was performed via extract-transform-load (ETL) steps using custom software. Original software (e.g. eCRF) used by the data collections did not need any modifications. The survey yielded 68 data collections. Until Jan 2018, 20 collections were successfully integrated. 10 collections were withdrawn by their owners while the integration of 38 was delayed. Data discovery, the process of finding existing data collections in a large research network, proved to be the step most underestimated. From technical point of view, data integration proved to be of minor complexity in comparison to the effort required for harmonization/mapping of data elements and management of common terminology.

Metadata Import from RDF to i2b2

Metadata management is an important task in medical informatics and highly affects the gain out of existing health information data. Data Warehouse solutions like Informatics for Integrating Biology and the Bedside (i2b2) are common tools for identifying patient cohorts and analyzing collected clinical data while respecting patient privacy. The Resource Description Framework (RDF) is designed for highly interoperable ontology representation in various formats, facilitating ontology and metadata management. Our approach is to combine i2b2's and RDF's benefits by importing the easy-to-edit RDF ontology into the extensive-research-enabling i2b2 software. We do so by using a SPARQL Protocol and RDF Query Language (SPARQL) interface, that enables RDF data queries, and developing a java program, which then generates i2b2-specific SQL insert statements. To demonstrate our solution's feasibility, we transcribe our lung disease specific ontology to RDF and import it into our i2b2 data warehouse.

Using RDF and Git to Realize a Collaborative Metadata Repository

The German Center for Lung Research (DZL) is a research network with the aim of researching respiratory diseases. The participating study sites' register data differs in terms of software and coding system as well as data field coverage. To perform meaningful consortium-wide queries through one single interface, a uniform conceptual structure is required covering the DZL common data elements. No single existing terminology includes all our concepts. Potential candidates such as LOINC and SNOMED only cover specific subject areas or are not granular enough for our needs. To achieve a broadly accepted and complete ontology, we developed a platform for collaborative metadata management. The DZL data management group formulated detailed requirements regarding the metadata repository and the user interfaces for metadata editing. Our solution builds upon existing standard technologies allowing us to meet those requirements. Its key parts are RDF and the distributed version control system Git. We developed a software system to publish updated metadata automatically and immediately after performing validation tests for completeness and consistency.

Li2b2-Façade: Simulation of i2b2 Data Warehouse Server and Client for Interaction with Other Systems

Since its release in 2004, the i2b2 data warehouse software has become a valuable tool for clinical researchers. Physicians can use its browser-based query frontend intuitively without additional training or reading through documentation. While the i2b2 software describes it's API as "REST", it is neither stateless nor does it follow the common guidelines for RESTful APIs. Thus, interfacing other software with i2b2's custom RPC-style XML-API is a very cumbersome process. To overcome these issues, we developed a lightweight software abstraction layer "lightweight i2b2 façade" (li2b2-façade).

Mapping Equivalence of German Emergency Department Medical Record Concepts with SNOMED CT After Implementation with HL7 CDA

INTRODUCTION

The German Emergency Department Medical Record (GEDMR) was created by medical domain experts and healthcare providers providing a dataset as well as a form. The trauma module of GEDMR was syntactically standardized using HL7 CDA and semantically standardized using different terminologies including SNOMED CT, LOINC and proprietary coding systems. This study depicts the mapping accuracy with aforementioned syntactical and semantical standards in general and especially the content coverage of SNOMED CT.

METHODS

The specification of GEDMR (V2015.1) concepts with eHealth-standards HL7-CDA, LOINC, SNOMED CT was analyzed. A content coverage assessment was made using the ISO TR 12300 rating scheme, following descriptive analysis.

RESULTS

The trauma module of GEDMR contains 489 concepts, with 202 concepts expressed via HL7 CDA structure. It is possible to code 89 % of the remaining concepts via SNOMED CT. 79 % provide an advanced level of semantic interoperability, as they represent the source information either lexically or as an approved synonym.

DISCUSSION

The terminology binding problem is relevant when combining different standards for syntactic and semantic interoperability with best practice documents and reference specifications providing guidance. A national license and extension for SNOMED CT in Germany as well as an ongoing effort in contributing to the International Version of SNOMED CT would be necessary to gain full coverage for concepts in German Emergency Medicine and to leverage the associated standardization process.

Secure Electronical Communications and Data Transfers in a Clinical Environment

As part of the German Project AKTIN, data security arises as an important issue. The context of this issue was detailed and the requirements were determined, with special focus on the hospital's point of view. The solution is illustrated in this poster. Further use cases in other medical or study context are also discussed.

CoMetaR: A Collaborative Metadata Repository for Biomedical Research Networks

The German Center for Lung Research (DZL) is a research network with the aim of researching respiratory diseases. To perform consortium-wide queries through one single interface, it requires a uniform conceptual structure. No single terminology covers all our concepts. To achieve a broadly accepted and complete ontology, we developed a platform for collaborative metadata management "CoMetaR". Anyone can browse and discuss the ontology while editing can be performed by authenticated users.

On-The-Fly Query Translation Between i2b2 and Samply in the German Biobank Node (GBN) Prototypes

Information retrieval is a major challenge in medical informatics. Various research projects have worked on this task in recent years on an institutional level by developing tools to integrate and retrieve information. However, when it comes down to querying such data across institutions, the challenge persists due to the high heterogeneity of data and differences in software systems. The German Biobank Node (GBN) project faced this challenge when trying to interconnect four biobanks to enable distributed queries for biospecimens. All biobanks had already established integrated data repositories, and some of them were already part of research networks. Instead of developing another software platform, GBN decided to form a bridge between these. This paper describes and discusses a core component from the GBN project, the OmniQuery library, which was implemented to enable on-the-fly query translation between heterogeneous research infrastructures.

Factors Influencing the Implementation and Distribution of Clinical Decision Support Systems (CDSS)

Clinical Decision Support Systems (CDSS) can have positive effects on quality of care measures, yet have not gained widespread traction in healthcare. This study sought to determine and evaluate barriers and facilitators to CDSS implementation and distribution. Based on 768 systems identified in a literature review we conducted semi-structured telephone interviews with 54 system developers in 16 countries. Qualitative analysis led to the identification of 66 key factors influencing implementation. Central issues evolved around CDSS properties, quality and integration, as well as usability, user related factors, internal marketing, resource issues and collaborations with emphasis partly on topics differing from existing research. Additionally, evidence pointed to regional differences regarding implementation hurdles. Recent regulatory requirements were deemed less of a barrier to system adoption than expected, even though lacking expertise in this area was surprisingly common among interview partners.

Proof-of-Concept Integration of Heterogeneous Biobank IT Infrastructures into a Hybrid Biobanking Network

Cross-institutional biobank networks hold the promise of supporting medicine by enabling the exchange of associated samples for research purposes. Various initiatives, such as BBMRI-ERIC and German Biobank Node (GBN), aim to interconnect biobanks for enabling the compilation of joint biomaterial collections. However, building software platforms to facilitate such collaboration is challenging due to the heterogeneity of existing biobank IT infrastructures and the necessary efforts for installing and maintaining additional software components. As a remedy, this paper presents the concept of a hybrid network for interconnecting already existing software components commonly found in biobanks and a proof-of-concept implementation of two prototypes involving four biobanks of the German Biobank Node. Here we demonstrate the successful bridging of two IT systems found in many German biobanks - Samply and i2b2.

Asynchronous Query Distribution Between Multiple i2b2 Research Data Warehouses: Li2b2-SHRINE

Clinical Data Ware Houses are established sources for research and quality management. The open source data warehouse software i2b2 enjoys good reputation and wide-spread use in the international medical informatics community. We developed a novel infrastructure to allow queries to be distributed asynchronously between i2b2 data warehouses.

Automated Report Generation for Research Data Repositories: From i2b2 to PDF

We developed an automated toolchain to generate reports of i2b2 data. It is based on free open source software and runs on a Java Application Server. It is sucessfully used in an ED registry project. The solution is highly configurable and portable to other projects based on i2b2 or compatible factual data sources.

Disseminating a Standard for Medical Records in Emergency Departments Among Different Software Vendors Using HL7 CDA

A standardized medical record for the emergency department (GEDMR) was released in Germany, but only sparsely and randomly implemented by emergency department (ED) electronic health record (EHR) vendors. A reason for this may be a lacking common language between the medical and the Health Information Technology (HIT) domain. HL7 clinical document architecture (CDA) may leverage this communication gap. This paper reports on the effects of a professional medical association record standard on EHR vendors and the German ED-EHR market. Standard records and data standards are developed and published by different institutions either on governmental, healthcare agency or medical association level. There are some standard records, especially by US cardiology associations, transformed into HL7 C-CDA. GEDMR was modeled as HL7 CDA with the use of interoperable terminologies like LOINC and SNOMED CT. Being part of an emergency department data registry development project, local deployment at 15 project hospitals receiving sufficient funding was performed. Two major ED-EHR vendors adapted GEDMR within their product including CDA export. 106,868 CDAs were produced in six hospitals until now. Four local implementations with four different ED-EHRs were developed, producing 42,256 CDAs. Five additional vendors are adapting or developing an ED-EHR. The GEDMR-CDA implementation guide with funding for implementation in project hospitals had a significant impact on the German ED-EHR market. Within two years after release, a broadening and increasingly self-enforcing support by German ED-EHR vendors is notable.

Version changes in medical software: proposing minimal requirements for release notes and a version number convention - an operators' point of view

Medical software--like any other software--is susceptible to errors. To avoid false system behaviour or attenuate its consequences, system operators need to know about changes in the software. The goal of this proposal is to define terms and minimum requirements regarding documentation for a version change from the operator's point of view, especially in the domain of medical software or software as a medical device (SaMD). The results are a classification of version changes (Upgrade: breaks support for a rollback to a prior version, Major Update: either substantial configuration or user education needed, Minor Update: minor configuration or user information needed, Patch: collection of (small) changes that require neither configuration nor user information.). Additionally, minimal requirements for release notes are determined and a document structure recommended.

Comparison of electronic health record system functionalities to support the patient recruitment process in clinical trials

OBJECTIVES

Reusing data from electronic health records for clinical and translational research and especially for patient recruitment has been tackled in a broader manner since about a decade. Most projects found in the literature however focus on standalone systems and proprietary implementations at one particular institution often for only one singular trial and no generic evaluation of EHR systems for their applicability to support the patient recruitment process does yet exist. Thus we sought to assess whether the current generation of EHR systems in Germany provides modules/tools, which can readily be applied for IT-supported patient recruitment scenarios.

METHODS

We first analysed the EHR portfolio implemented at German University Hospitals and then selected 5 sites with five different EHR implementations covering all major commercial systems applied in German University Hospitals. Further, major functionalities required for patient recruitment support have been defined and the five sample EHRs and their standard tools have been compared to the major functionalities.

RESULTS

In our analysis of the site's hospital information system environments (with four commercial EHR systems and one self-developed system) we found that - even though no dedicated module for patient recruitment has been provided - most EHR products comprise generic tools such as workflow engines, querying capabilities, report generators and direct SQL-based database access which can be applied as query modules, screening lists and notification components for patient recruitment support. A major limitation of all current EHR products however is that they provide no dedicated data structures and functionalities for implementing and maintaining a local trial registry.

CONCLUSIONS

At the five sites with standard EHR tools the typical functionalities of the patient recruitment process could be mostly implemented. However, no EHR component is yet directly dedicated to support research requirements such as patient recruitment. We recommend for future developments that EHR customers and vendors focus much more on the provision of dedicated patient recruitment modules.

Design and multicentric implementation of a generic software architecture for patient recruitment systems re-using existing HIS tools and routine patient data

OBJECTIVE

(1) To define features and data items of a Patient Recruitment System (PRS); (2) to design a generic software architecture of such a system covering the requirements; (3) to identify implementation options available within different Hospital Information System (HIS) environments; (4) to implement five PRS following the architecture and utilizing the implementation options as proof of concept.

METHODS

Existing PRS were reviewed and interviews with users and developers conducted. All reported PRS features were collected and prioritized according to their published success and user's request. Common feature sets were combined into software modules of a generic software architecture. Data items to process and transfer were identified for each of the modules. Each site collected implementation options available within their respective HIS environment for each module, provided a prototypical implementation based on available implementation possibilities and supported the patient recruitment of a clinical trial as a proof of concept.

RESULTS

24 commonly reported and requested features of a PRS were identified, 13 of them prioritized as being mandatory. A UML version 2 based software architecture containing 5 software modules covering these features was developed. 13 data item groups processed by the modules, thus required to be available electronically, have been identified. Several implementation options could be identified for each module, most of them being available at multiple sites. Utilizing available tools, a PRS could be implemented in each of the five participating German university hospitals.

CONCLUSION

A set of required features and data items of a PRS has been described for the first time. The software architecture covers all features in a clear, well-defined way. The variety of implementation options and the prototypes show that it is possible to implement the given architecture in different HIS environments, thus enabling more sites to successfully support patient recruitment in clinical trials.

ChronoQuery: visual modelling of temporal queries for real-time decision support

Clinical decision support systems are an important aspect of medical informatics. The increasing amount of available patient data requires physicians to rely on information technology for research and during their day by day work. In intensive care medicine, fast actions are especially important. One major step towards enabling direct interaction of medical staff with patient data was the development of clinical data repositories with easy query frontends. While clinical data repositories can be extended for the use of real-time data, the corresponding query frontends do not support the time concepts necessary for real-time queries and decision support. Aim of this project is the development of a user interface to give physicians visual understanding of propositional logic combined with time concepts. Thus, physicians should be able formulate simple time based queries on their own--and validate and quality check complex queries created by medical informatics experts.

Balancing the need for big data and patient data privacy--an IT infrastructure for a decentralized emergency care research database

Emergency rooms of hospitals provide care to a lot of patients and have great impact on their outcome, so researching the quality of care seems important. Research using registries has yielded impressive results in other areas of medicine. However centralized data-storage has its pitfalls, especially regarding data privacy. We therefore drafted an IT infrastructure that uses decentralized storage to ensure data privacy, but still enables data transfer between participating hospitals. It implements an independent information broker to ensure anonymity of patients. Still it provides a way for researchers to request data and hospitals to contribute data on an opt-in basis. Although not an entirely new approach, the emphasis on data privacy throughout the design is a novel aspect providing a better balance between the need for big sample sizes and patient privacy.

LOINC in prehospital emergency medicine in Germany - experience of the `DIRK´-project

BACKGROUND

Treatment of patients picked up by emergency services can be improved by data transfer ahead of arrival. Care given to emergency patients can be assessed and improved through data analysis. Both goals require electronic data transfer from the emergency medical services (EMS) to the hospital information system. Therefore a generic semantic standard is needed.

OBJECTIVES

Objective of this paper is to test the suitability of the international nomenclature Logical Observation Identifiers Names and Codes (LOINC) to encode the core data-sets for rescue service protocols (MIND 2 and MIND 3). Encoding diagnosis and medication categories using ICD-10 and ATC were also assessed.

METHODS

Protocols were broken down into concepts, assigned to categories, translated and manually mapped to LOINC codes. Each protocol was independently encoded by two healthcare professionals and in case of discrepancies a third expert was consulted to reach a consensus.

RESULTS

Currently 39% of parameters could be mapped to LOINC. Additional use of other coding systems such as International Statistical Classification of Diseases and Related Health Problems (ICD-10) for diagnoses and Anatomical Therapeutic Chemical Classification System (ATC) for medications increases the rate of 'mappable' parameters to 56%.

CONCLUSIONS

Although the coverage is low, mapping has shown that LOINC is suitable to encode concepts of the rescue services. In order to create a generic semantic model to be applied in the field our next step is to request new LOINC codes for the missing concepts.

Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence

BACKGROUND

Computerized clinical trial recruitment support is one promising field for the application of routine care data for clinical research. The primary task here is to compare the eligibility criteria defined in trial protocols with patient data contained in the electronic health record (EHR). To avoid the implementation of different patient definitions in multi-site trials, all participating research sites should use similar patient data from the EHR. Knowledge of the EHR data elements which are commonly available from most EHRs is required to be able to define a common set of criteria. The objective of this research is to determine for five tertiary care providers the extent of available data compared with the eligibility criteria of randomly selected clinical trials.

METHODS

Each participating study site selected three clinical trials at random. All eligibility criteria sentences were broken up into independent patient characteristics, which were then assigned to one of the 27 semantic categories for eligibility criteria developed by Luo et al. We report on the fraction of patient characteristics with corresponding structured data elements in the EHR and on the fraction of patients with available data for these elements. The completeness of EHR data for the purpose of patient recruitment is calculated for each semantic group.

RESULTS

351 eligibility criteria from 15 clinical trials contained 706 patient characteristics. In average, 55% of these characteristics could be documented in the EHR. Clinical data was available for 64% of all patients, if corresponding data elements were available. The total completeness of EHR data for recruitment purposes is 35%. The best performing semantic groups were 'age' (89%), 'gender' (89%), 'addictive behaviour' (74%), 'disease, symptom and sign' (64%) and 'organ or tissue status' (61%). No data was available for 6 semantic groups.

CONCLUSIONS

There exists a significant gap in structure and content between data documented during patient care and data required for patient eligibility assessment. Nevertheless, EHR data on age and gender of the patient, as well as selected information on his disease can be complete enough to allow for an effective support of the manual screening process with an intelligent preselection of patients and patient data.

Architecture of a prehospital emergency patient care report system (PEPRS)

In recent years, prehospital emergency care adapted to the technology shift towards tablet computers and mobile computing. In particular, electronic patient care report (e-PCR) systems gained considerable attention and adoption in prehospital emergency medicine [1]. On the other hand, hospital information systems are already widely adopted. Yet, there is no universal solution for integrating prehospital emergency reports into electronic medical records of hospital information systems. Previous projects either relied on proprietary viewing workstations or examined and transferred only data for specific diseases (e.g. stroke patients[2]). Using requirements engineering and a three step software engineering approach, this project presents a generic architecture for integrating prehospital emergency care reports into hospital information systems. Aim of this project is to describe a generic architecture which can be used to implement data transfer and integration of pre hospital emergency care reports to hospital information systems. In summary, the prototype was able to integrate data in a standardized manner. The devised methods can be used design generic software for prehospital to hospital data integration.

Proactive authenticated notifications for health practitioners: two way human computer interaction through phone

Notifications and alerts play an important role in clinical daily routine. Rising prevalence of clinical decision support systems and electronic health records also result in increasing demands on notification systems. Failure adequately to communicate a critical value is a potential cause of adverse events. Critical laboratory values and changing vital data depend on timely notifications of medical staff. Vital monitors and medical devices rely on acoustic signals for alerting which are prone to "alert fatigue" and require medical staff to be present within audible range. Personal computers are unsuitable to display time critical notification messages, since the targeted medical staff are not always operating or watching the computer. On the other hand, mobile phones and smart devices enjoy increasing popularity. Previous notification systems sending text messages to mobile phones depend on asynchronous confirmations. By utilizing an automated telephony server, we provide a method to deliver notifications quickly and independently of the recipients' whereabouts while allowing immediate feedback and confirmations. Evaluation results suggest the feasibility of the proposed notification system for real-time notifications.

Automated realtime data import for the i2b2 clinical data warehouse: introducing the HL7 ETL cell

Clinical data warehouses are used to consolidate all available clinical data from one or multiple organizations. They represent an important source for clinical research, quality management and controlling. Since its introduction, the data warehouse i2b2 gathered a large user base in the research community. Yet, little work has been done on the process of importing clinical data into data warehouses using existing standards. In this article, we present a novel approach of utilizing the clinical integration server as data source, commonly available in most hospitals. As information is transmitted through the integration server, the standardized HL7 message is immediately parsed and inserted into the data warehouse. Evaluation of import speeds suggest feasibility of the provided solution for real-time processing of HL7 messages. By using the presented approach of standardized data import, i2b2 can be used as a plug and play data warehouse, without the hurdle of customized import for every clinical information system or electronic medical record. The provided solution is available for download at http://sourceforge.net/projects/histream/.

Quality of human-computer interaction--results of a national usability survey of hospital-IT in Germany

Background

Due to the increasing functionality of medical information systems, it is hard to imagine day to day work in hospitals without IT support. Therefore, the design of dialogues between humans and information systems is one of the most important issues to be addressed in health care. This survey presents an analysis of the current quality level of human-computer interaction of healthcare-IT in German hospitals, focused on the users' point of view.

Methods

To evaluate the usability of clinical-IT according to the design principles of EN ISO 9241-10 the IsoMetrics Inventory, an assessment tool, was used. The focus of this paper has been put on suitability for task, training effort and conformity with user expectations, differentiated by information systems. Effectiveness has been evaluated with the focus on interoperability and functionality of different IT systems.

Results

4521 persons from 371 hospitals visited the start page of the study, while 1003 persons from 158 hospitals completed the questionnaire. The results show relevant variations between different information systems.

Conclusions

Specialised information systems with defined functionality received better assessments than clinical information systems in general. This could be attributed to the improved customisation of these specialised systems for specific working environments. The results can be used as reference data for evaluation and benchmarking of human computer engineering in clinical health IT context for future studies.

Identifying patients for clinical trials using fuzzy ternary logic expressions on HL7 messages

Identifying eligible patients is one of the most critical parts of any clinical trial. The process of recruiting patients for the third phase of any clinical trial is usually done manually, informing relevant physicians or putting notes on bulletin boards. While most necessary information is already available in electronic hospital information systems, required data still has to be looked up individually. Most university hospitals make use of a dedicated communication server to distribute information from independent information systems, e.g. laboratory information systems, electronic health records, surgery planning systems. Thus, a theoretical model is developed to formally describe inclusion and exclusion criteria for each clinical trial using a fuzzy ternary logic expression. These expressions will then be used to process HL7 messages from a communication server in order to identify eligible patients.