Federated electronic data capture (fEDC): Architecture and prototype.

In clinical research as well as patient care, structured documentation of findings is an important task. In many cases, this is achieved by means of electronic case report forms (eCRF) using corresponding information technology systems. To avoid double data entry, eCRF systems can be integrated with electronic health records (EHR). However, when researchers from different institutions collaborate in collecting data, they often use a single joint eCRF system on the Internet. In this case, integration with EHR systems is not possible in most cases due to information security and data protection restrictions. To overcome this shortcoming, we propose a novel architecture for a federated electronic data capture system (fEDC). Four key requirements were identified for fEDC: Definitions of forms have to be available in a reliable and controlled fashion, integration with electronic health record systems must be possible, patient data should be under full local control until they are explicitly transferred for joint analysis, and the system must support data sharing principles accepted by the scientific community for both data model and data captured. With our approach, sites participating in a joint study can run their own instance of an fEDC system that complies with local standards (such as being behind a network firewall) while also being able to benefit from using identical form definitions by sharing metadata in the Operational Data Model (ODM) format published by the Clinical Data Interchange Standards Consortium (CDISC) throughout the collaboration. The fEDC architecture was validated with a working open-source prototype at five German university hospitals. The fEDC architecture provides a novel approach with the potential to significantly improve collaborative data capture: Efforts for data entry are reduced and at the same time, data quality is increased since barriers for integrating with local electronic health record systems are lowered. Further, metadata are shared and patient privacy is ensured at a high level.

A data driven learning approach for the assessment of data quality.

BACKGROUND: Data quality assessment is important but complex and task dependent. Identifying suitable measurement methods and reference ranges for assessing their results is challenging. Manually inspecting the measurement results and current data driven approaches for learning which results indicate data quality issues have considerable limitations, e.g. to identify task dependent thresholds for measurement results that indicate data quality issues. OBJECTIVES: To explore the applicability and potential benefits of a data driven approach to learn task dependent knowledge about suitable measurement methods and assessment of their results. Such knowledge could be useful for others to determine whether a local data stock is suitable for a given task. METHODS: We started by creating artificial data with previously defined data quality issues and applied a set of generic measurement methods on this data (e.g. a method to count the number of values in a certain variable or the mean value of the values). We trained decision trees on exported measurement methods' results and corresponding outcome data (data that indicated the data's suitability for a use case). For evaluation, we derived rules for potential measurement methods and reference values from the decision trees and compared these regarding their coverage of the true data quality issues artificially created in the dataset. Three researchers independently derived these rules. One with knowledge about present data quality issues and two without. RESULTS: Our self-trained decision trees were able to indicate rules for 12 of 19 previously defined data quality issues. Learned knowledge about measurement methods and their assessment was complementary to manual interpretation of measurement methods' results. CONCLUSIONS: Our data driven approach derives sensible knowledge for task dependent data quality assessment and complements other current approaches. Based on labeled measurement methods' results as training data, our approach successfully suggested applicable rules for checking data quality characteristics that determine whether a dataset is suitable for a given task.

A method for interoperable knowledge-based data quality assessment.

BACKGROUND: Assessing the quality of healthcare data is a complex task including the selection of suitable measurement methods (MM) and adequately assessing their results. OBJECTIVES: To present an interoperable data quality (DQ) assessment method that formalizes MMs based on standardized data definitions and intends to support collaborative governance of DQ-assessment knowledge, e.g. which MMs to apply and how to assess their results in different situations. METHODS: We describe and explain central concepts of our method using the example of its first real world application in a study on predictive biomarkers for rejection and other injuries of kidney transplants. We applied our open source tool-openCQA-that implements our method utilizing the openEHR specifications. Means to support collaborative governance of DQ-assessment knowledge are the version-control system git and openEHR clinical information models. RESULTS: Applying the method on the study's dataset showed satisfactory practicability of the described concepts and produced useful results for DQ-assessment. CONCLUSIONS: The main contribution of our work is to provide applicable concepts and a tested exemplary open source implementation for interoperable and knowledge-based DQ-assessment in healthcare that considers the need for flexible task and domain specific requirements.

Automated population of an i2b2 clinical data warehouse from an openEHR-based data repository.

BACKGROUND: Detailed Clinical Model (DCM) approaches have recently seen wider adoption. More specifically, openEHR-based application systems are now used in production in several countries, serving diverse fields of application such as health information exchange, clinical registries and electronic medical record systems. However, approaches to efficiently provide openEHR data to researchers for secondary use have not yet been investigated or established. METHODS: We developed an approach to automatically load openEHR data instances into the open source clinical data warehouse i2b2. We evaluated query capabilities and the performance of this approach in the context of the Hanover Medical School Translational Research Framework (HaMSTR), an openEHR-based data repository. RESULTS: Automated creation of i2b2 ontologies from archetypes and templates and the integration of openEHR data instances from 903 patients of a paediatric intensive care unit has been achieved. In total, it took an average of ∼2527s to create 2.311.624 facts from 141.917 XML documents. Using the imported data, we conducted sample queries to compare the performance with two openEHR systems and to investigate if this representation of data is feasible to support cohort identification and record level data extraction. DISCUSSION: We found the automated population of an i2b2 clinical data warehouse to be a feasible approach to make openEHR data instances available for secondary use. Such an approach can facilitate timely provision of clinical data to researchers. It complements analytics based on the Archetype Query Language by allowing querying on both, legacy clinical data sources and openEHR data instances at the same time and by providing an easy-to-use query interface. However, due to different levels of expressiveness in the data models, not all semantics could be preserved during the ETL process.

Targeted Data Quality Analysis for a Clinical Decision Support System for SIRS Detection in Critically Ill Pediatric Patients.

BACKGROUND: Data quality issues can cause false decisions of clinical decision support systems (CDSSs). Analyzing local data quality has the potential to prevent data quality-related failure of CDSS adoption. OBJECTIVES: To define a shareable set of applicable measurement methods (MMs) for a targeted data quality assessment determining the suitability of local data for our CDSS. METHODS: We derived task-specific MMs using four approaches: (1) a GUI-based data quality analysis using the open source tool openCQA. (2) Analyzing cases of known false CDSS decisions. (3) Data-driven learning on MM-results. (4) A systematic check to find blind spots in our set of MMs based on the HIDQF data quality framework. We expressed the derived data quality-related knowledge about the CDSS using the 5-tuple-formalization for MMs. RESULTS: We identified some task-specific dataset characteristics that a targeted data quality assessment for our use case should inspect. Altogether, we defined 394 MMs organized in 13 data quality knowledge bases. CONCLUSIONS: We have created a set of shareable, applicable MMs that can support targeted data quality assessment for CDSS-based systemic inflammatory response syndrome (SIRS) detection in critically ill, pediatric patients. With the demonstrated approaches for deriving and expressing task-specific MMs, we intend to help promoting targeted data quality assessment as a commonly recognized usual part of research on data-consuming application systems in health care.

Identifying Relevant FHIR Elements for Data Quality Assessment in the German Core Data Set.

The German Medical Informatics Initiative makes clinical routine data available for biomedical research. In total, 37 university hospitals have set up so-called data integration centers to facilitate this data reuse. A standardized set of HL7 FHIR profiles ("MII Core Data Set") defines the common data model across all centers. Regular Projectathons ensure continuous evaluation of the implemented data sharing processes on artificial and real-world clinical use cases. In this context, FHIR continues to rise in popularity for exchanging patient care data. As reusing data from patient care in clinical research requires high trust in the data, data quality assessments are a key point of concern in the data sharing process. To support the setup of data quality assessments within data integration centers, we suggest a process for finding elements of interest from FHIR profiles. We focus on the specific data quality measures defined by Kahn et al.

Towards Harmonized Data Quality in the Medical Informatics Initiative - Current State and Future Directions.

Health data from hospital information systems are valuable sources for medical research but have known issues in terms of data quality. In a nationwide data integration project in Germany, health care data from all participating university hospitals are being pooled and refined in local centers. As there is currently no overarching agreement on how to deal with errors and implausibilities, meetings were held to discuss the current status and the need to develop consensual measures at the organizational and technical levels. This paper analyzes the discovered similarities and differences. The result shows that although data quality checks are carried out at all sites, there is a lack of both centrally coordinated data quality indicators and a formalization of plausibility rules as well as a repository for automatic querying of the rules, for example in ETL processes.

Preliminary Analysis of Structured Reporting in the HiGHmed Use Case Cardiology: Challenges and Measures.

The HiGHmed consortium aims to create a shared information governance framework to integrate clinical routine data. One challenge is the replacement of unstructured reporting (e.g. doctoral letters) with structured reporting in clinical routine. The Heidelberg cardiology department evaluates dynamic PDF forms for structured data reporting of heart failure (HF) patients. In this use case, we aim to identify potential caveats or shortcomings in data processing at an early stage. We employed data mining strategies to detect patterns related to incomplete or false data, which we found to be present among all data types. We then discuss the characteristics of the baseline patient cohort in Heidelberg to find out about specific peculiarities and potential biases, which may be site-specific. Briefly, our patient population is predominantly male (67%), NYHA I & II are the most common severity classes, NYHA IV is missing entirely. Most patients have a dilated cardiomyopathy (DCM) or coronary heart disease (CHD) diagnosed as their cause of HF. Finally, we also analyzed how comorbidities and risk factors relate to specific disease entities of heart failure patients. Family anamnesis was more frequent among cardiomyopathy patients than among CHD patients, who show a more dominating presence of dyslipidemia instead. Generally, the most dominant risk factor was arterial hypertension, while at the other end of the scale alcoholism appears to be underreported.

Transformation of microbiology data into a standardised data representation using OpenEHR.

The spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue. To allow application reusability across institutions, the various heterogeneous microbiology data representations needs to be transformed into standardised, unambiguous data models. In this work, we present a multi-centric standardisation approach by using openEHR as modelling standard. Data models have been consented in a multicentre and international approach. Participating sites integrated microbiology reports from primary source systems into an openEHR-based data platform. For evaluation, we implemented a prototypical application, compared the transformed data with original reports and conducted automated data quality checks. We were able to develop standardised and interoperable microbiology data models. The publicly available data models can be used across institutions to transform real-life microbiology reports into standardised representations. The implementation of a proof-of-principle and quality control application demonstrated that the new formats as well as the integration processes are feasible. Holistic transformation of microbiological data into standardised openEHR based formats is feasible in a real-life multicentre setting and lays the foundation for developing cross-institutional, automated outbreak detection systems.

Clinical Information Model Based Data Quality Checks: Theory and Example.

INTRODUCTION: We describe principles of leveraging clinical information models (CIMs) for data quality (DQ) checks and present the exemplary application of these principles. METHODS: openEHR compliant CIMs are used to express DQ-checks as constraints. Test setting is the process of extracting, transforming and loading (ETL) assisted ventilation data from two patient data management systems (PDMS) of a pediatric intensive care unit into a local openEHR-based data repository. RESULTS: A generic component logs aggregated DQ-check results for ~28 million entries. DQ-issue types in the presented results are range-, format- and value set violations. DISCUSSION: CIMs are suitable means to define DQ-checks for range-, format-, value set and cardinality constraints. However, they cannot constitute a complete solution for standardized DQ-assessment.

Provenance Solutions for Medical Research in Heterogeneous IT-Infrastructure: An Implementation Roadmap.

Research data generated in large projects raise challenges about not only data analytics but also data quality assessments and data governance. The provenance of a data set - that is the history of data sets - holds information relevant to technicians and non-technicians and is able to answer questions regarding data quality, transparency, and more. We propose an implementation roadmap to extract, store, and utilize provenance records in order to make provenance available to data analysts, research subjects, privacy officers, and machines (machine readability). Each aspect is tackled separately, resulting in the implementation of a provenance toolbox. We aim to do so within the context of HiGHmed, a research consortium established within the medical informatics initiative in Germany. In this testbed of federated IT-infrastructures, the toolbox shall assist each stakeholder in answering domain-specific and domain-agnostic questions regarding the provenance of data sets. This way, we will improve data re-use, transparency, and reproducibility.

Striving for Use Case Specific Optimization of Data Quality Assessment for Health Data.

Data quality (DQ) assessment is advisable before (re)using datasets. Besides supporting DQ-assessment, DQ-tools can indicate data integration issues. The objective of this contribution is to put up for discussion the identified current state of scientific knowledge in DQ-assessment for health data and the planned work resulting from that state of knowledge. The state of scientific knowledge bases on a continuous literature survey and tracking of related working groups' activities. 95 full text publications constitute the considered state of scientific knowledge of which a representative selection of six DQ-tools and -frameworks is presented. The delineated future work explores multi-institutional machine learning on the DQ-measurement results of an interoperable DQ-tool, with the goal to optimize DQ-measurement method combinations and reference values for DQ-issue recognition.

Modeling of ETL-Processes and Processed Information in Clinical Data Warehousing.

BACKGROUND: Literature describes a big potential for reuse of clinical patient data. A clinical data warehouse (CDWH) is a means for that. OBJECTIVES: To support management and maintenance of processes extracting, transforming and loading (ETL) data into CDWHs as well as to ease reuse of metadata between regular IT-management, CDWH and secondary data users by providing a modeling approach. METHODS: Expert survey and literature review to find requirements and existing modeling techniques. An ETL-modeling-technique was developed extending existing modeling techniques. Evaluation by exemplarily modeling existing ETL-process and a second expert survey. RESULTS: Nine experts participated in the first survey. Literature review yielded 15 included publications. Six existing modeling techniques were identified. A modeling technique extending 3LGM2 and combining it with openEHR information models was developed and evaluated. Seven experts participated in the evaluation. CONCLUSION: The developed approach can help in management and maintenance of ETL-processes and could serve as interface between regular IT-management, CDWH and secondary data users.

An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR.

BACKGROUND: Clinical decision-support systems (CDSS) are designed to solve knowledge-intensive tasks for supporting decision-making processes. Although many approaches for designing CDSS have been proposed, due to high implementation costs, as well as the lack of interoperability features, current solutions are not well-established across different institutions. Recently, the use of standardized formalisms for knowledge representation as terminologies as well as the integration of semantically enriched clinical information models, as openEHR Archetypes, and their reuse within CDSS are theoretically considered as key factors for reusable CDSS. OBJECTIVE: We aim at developing and evaluating an openEHR based approach to achieve interoperability in CDSS by designing and implementing an exemplary system for automated systemic inflammatory response syndrome (SIRS) detection in pediatric intensive care. METHODS: We designed an interoperable concept, which enables an easy integration of the CDSS across different institutions, by using openEHR Archetypes, terminology bindings and the Archetype Query Language (AQL). The practicability of the approach was tested by (1) implementing a prototype, which is based on an openEHR based data repository of the Hannover Medical School (HaMSTR), and (2) conducting a first pilot study. RESULTS: We successfully designed and implemented a CDSS with interoperable knowledge bases and interfaces by reusing internationally agreed-upon Archetypes, incorporating LOINC terminology and creating AQL queries, which allowed retrieving dynamic facts in a standardized and unambiguous form. The technical capabilities of the system were evaluated by testing the prototype on 16 randomly selected patients with 129 days of stay, and comparing the results with the assessment of clinical experts (leading to a sensitivity of 1.00, a specificity of 0.94 and a Cohen's kappa of 0.92). CONCLUSIONS: We found the use of openEHR Archetypes and AQL a feasible approach to bridge the interoperability gap between local infrastructures and CDSS. The designed concept was successfully transferred into a clinically evaluated openEHR based CDSS. To the authors' knowledge, this is the first openEHR based CDSS, which is technically reliable and capable in a real context, and facilitates clinical decision-support for a complex task. Further activities will comprise enrichments of the knowledge base, the reasoning processes and cross-institutional evaluations.

Introducing a Method for Transformation of Paper-Based Research Data into Concept-Based Representation with openEHR.

Combining research data and clinical routine data is a chance for medical research. We present our method for the transformation of paper-based research data into a concept-based representation. With this representation the study data from research projects can be combined with data from clinical tools with less integration effort. We applied and verified our method using data from a current research study. In this paper we also show our main challenges and lessons learned. Clinical assessment data and study diaries from a long term study (n=24, 3 months observation time each, 17 different clinical assessments) stored on paper were used as the data set. An openEHR-based electronical health record platform was adapted for acquisition and representation of the research data. To avoid transcription errors, the data was entered twice by different student assistants. A third compared and corrected both data sets. Content models (17 archetypes and five templates from openEHR concept) based on clinical assessments were created manually. Web forms for data entry were created automatically on the basis of this concept-based content models. Additionally, form functionalities to support data entry and comparison were implemented. In total, 829 compositions were entered by the student assistants. With our developed method, we are able to represent the study data in a clinical concept-based platform, which means less integration effort for access and processing of research and clinical data. Some minor difficulties occurred during the process. All in all, adapting routine tools, like the EHR platform, seems to be convenient to deal with research data.

Low acyl-CoA synthetase 5 expression in colorectal carcinomas is prognostic for early tumour recurrence.

It has been shown that the metabolism of long chain fatty acids is involved in colorectal carcinogenesis. Acyl-CoA synthetases (ACSL) activate free fatty acids by synthesis of acyl-CoA thioesters. ACSL isoform 5 (ACSL5) is involved in enterocytic differentiation and maturation by regulating both pro-apoptotic and anti-proliferative effects. Whilst impaired expression of ACSL5 has been associated with sporadic colorectal carcinogenesis, little is known about ACSL5 as a prognostic factor. Aim of this retrospective study was to characterize the prognostic impact of ACSL5 expression levels in sporadic colorectal adenocarcinomas. A total of 72 patients with a median follow-up of 54 months was included. Using a standardized immunohistochemical approach, colorectal adenocarcinomas with low (n=41; group 1) or high (n=31; group 2) ACSL5 levels were identified. In a one-year follow-up, tumour recurrence was significantly increased in group 1 (p=0.0279). The finding was independent of the TNM- and UICC-stage in the surgical resections. Frequency of lymph node metastasis and mortality was not different between the groups. In a long-time follow-up no differences were found between the ACSL5 groups. The data indicate that ACSL5 could be an independent prognostic factor for early recurrence of sporadic colorectal adenocarcinoma.

[An urban EMS at the start of a cross-sectoral quality management system: prioritized implementation of the 2010 ERC recommendations and long-term survival after cardiac arrest]. / Ein städtischer Rettungsdienstbereich am Beginn eines sektorenübergreifenden Qualitätsmanagementsystems: Eine priorisierte Umsetzung der ERC Empfehlungen von 2010 und das Langzeitüberleben nach Herzkreislaufstillstand.

INTRODUCTION: Due to limited resources, the 2010 European Resuscitation Council (ERC) guidelines could not be fully implemented in the Emergency Medical Services (EMS) of Brunswick, Germany. This is why implementation was prioritized according to local conditions. Thus, prehospital therapeutic hypothermia, mechanical chest compression and feedback systems were not established. Clinical data and long-term results were assessed by a QM system and room for improvement was identified. METHODS: All attempted resuscitations from 2011 until 2014 were recorded and compared against the German Resuscitation Registry. Outcomes of adult patients following non-traumatic cardiac arrest were analyzed by year. RESULTS: 812 resuscitations were attempted (incidence 81.2/100,000 inhabitants/year). In the two years following full implementation since 2013 the discharge rate from hospital was 16.4 %, the discharge rate with a favorable neurologic outcome was 14.1 %, the 1-year survival rate was 14.4 % in 2013. A significant improvement of risk-adjusted ROSC rate during the investigation period was demonstrated. The discharge rates remained unchanged; the increase in the discharge rates paralleled the increase in CPR incidence. EMS response times were remarkably shorter. CONCLUSION: The implementation of the ERC guidelines chosen appears to be generally safe. Fast EMS response contributed to superior results. All links of the chain of survival showed room for improvement, especially the proportion of lay rescuer CPR and telephone-assisted CPR. The high CPR incidence might indicate room for improvement in prevention. Access to resuscitation care can hardly be evaluated. Age-related access to pre-hospital resuscitation seems to be appropriate.

Using ICT to Support Individual Guidance in Health Promotion Programs for Increased Physical Activity.

INTRODUCTION: We report on experiences in implementing a system to support the individual guidance of training in health promotion programs aiming to increase participants' regular level of physical activity. METHODS: We used an iterative development approach considering data privacy and security aspects, followed by a phase of field testing and continuous further development. RESULTS: Our preliminary results comprise identified clinically relevant parameters, suitable data collection methods, experienced privacy and security challenges and a glance on our developed prototype system. DISCUSSION: We consider our results to be of interest for others doing related research. The most important requirements for a simple supporting system can be fulfilled with established solutions in the short run. A more adaptable and flexible system with an increased level of support in analysing the data, which we aim to achieve, leads to currently open research challenges.