Local Data Quality Assessments on EHR-Based Real-World Data for Rare Diseases.

The project "Collaboration on Rare Diseases" CORD-MI connects various university hospitals in Germany to collect sufficient harmonized electronic health record (EHR) data for supporting clinical research in the field of rare diseases (RDs). However, the integration and transformation of heterogeneous data into an interoperable standard through Extract-Transform-Load (ETL) processes is a complex task that may influence the data quality (DQ). Local DQ assessments and control processes are needed to ensure and improve the quality of RD data. We therefore aim to investigate the impact of ETL processes on the quality of transformed RD data. Seven DQ indicators for three independent DQ dimensions were evaluated. The resulting reports show the correctness of calculated DQ metrics and detected DQ issues. Our study provides the first comparison results between the DQ of RD data before and after ETL processes. We found that ETL processes are challenging tasks that influence the quality of RD data. We have demonstrated that our methodology is useful and capable of evaluating the quality of real-world data stored in different formats and structures. Our methodology can therefore be used to improve the quality of RD documentation and to support clinical research.

Rare Diseases in Hospital Information Systems-An Interoperable Methodology for Distributed Data Quality Assessments.

BACKGROUND: Multisite research networks such as the project "Collaboration on Rare Diseases" connect various hospitals to obtain sufficient data for clinical research. However, data quality (DQ) remains a challenge for the secondary use of data recorded in different health information systems. High levels of DQ as well as appropriate quality assessment methods are needed to support the reuse of such distributed data. OBJECTIVES: The aim of this work is the development of an interoperable methodology for assessing the quality of data recorded in heterogeneous sources to improve the quality of rare disease (RD) documentation and support clinical research. METHODS: We first developed a conceptual framework for DQ assessment. Using this theoretical guidance, we implemented a software framework that provides appropriate tools for calculating DQ metrics and for generating local as well as cross-institutional reports. We further applied our methodology on synthetic data distributed across multiple hospitals using Personal Health Train. Finally, we used precision and recall as metrics to validate our implementation. RESULTS: Four DQ dimensions were defined and represented as disjunct ontological categories. Based on these top dimensions, 9 DQ concepts, 10 DQ indicators, and 25 DQ parameters were developed and applied to different data sets. Randomly introduced DQ issues were all identified and reported automatically. The generated reports show the resulting DQ indicators and detected DQ issues. CONCLUSION: We have shown that our approach yields promising results, which can be used for local and cross-institutional DQ assessments. The developed frameworks provide useful methods for interoperable and privacy-preserving assessments of DQ that meet the specified requirements. This study has demonstrated that our methodology is capable of detecting DQ issues such as ambiguity or implausibility of coded diagnoses. It can be used for DQ benchmarking to improve the quality of RD documentation and to support clinical research on distributed data.

Assessment of the Consistency of Categorical Features Within the DZHK Biobanking Basic Set.

Data quality in health research encompasses a broad range of aspects and indicators. While some indicators are generic and can be calculated without domain knowledge, others require information about a specific data element. Even more complex are indicators addressing contradictions, that stem from implausible combinations of multiple data elements. In this paper, we investigate how contradictions within interdependent categorical data can be identified and if they give additional information about possible quality issues, their cause, and mitigation options. The 19 data elements that represent four biosample types including their pre-analytic states within the DZHK Biobanking basic set are exported to the CDISC Operational Data Model (ODM), transformed and loaded into a tranSMART instance. Through the implementation of a data quality assessment workflow as a SmartR plug-in, statistical information about the domain-specific consistency of interdependent values are retrieved, assessed, and visualized. Data quality indicators have been selected for the assessment according to common recommendations found in the literature. Different contradictions could be discovered in the dataset including mismatch of interdependent values in the pre-analytic states of blood and urine samples, as well as primary and aliquoted samples. The overall assessment rating shows that 99.61% of the interdependent values are free of contradictions. However, measures within the EDC design to avoid contradictions may result in overestimated missing rates in automatic, item-based quality assessment checks. Through consistency checks on interdependent categorical features, we demonstrated that consistency flaws can be found in the categorical data of biobanking metadata and that they can help to detect issues in the data entry process. Our approach underscores the importance of domain knowledge in the definition of the consistency rules but also knowledge about the EDC implementation of such consistency rules to consider the impact on item-based quality indicators.

Integrating Heterogeneous Data Sources for Cross-Institutional Data Sharing: Requirements Elicitation and Management in SMITH.

The digitization of health records and cross-institutional data sharing is a necessary precondition to improve clinical research and patient care. The SMITH project unites several university hospitals and medical faculties in order to provide medical informatics solutions for health data integration and cross-institutional communication. In this paper, we focus on requirements elicitation and management for extracting clinical data from heterogeneous subsystems and data integration based on eHealth standards such as HL7 FHIR and IHE profiles.

Ontology-Guided Markerless Navigation and Situational Awareness for Endoscopic Surgery.

Optical navigation systems help surgeons find their way through the complex anatomy of a patient. However, such systems are accident-sensitive, time-consuming and difficult to use because of their complicated technical requirements such as the setting of optical markers and their intraoperative registration. The BIOPASS project, therefore, provides an innovative localisation system for markerless navigation in endoscopic surgery to support medical decision making. This system comprises several machine learning classifiers to recognise anatomical structures visible in the endoscopic images. To verify the data provided by these classifiers and to alert medical staff about surgical risk situations, we developed a new ontology-based software called OntoSun. Our software improves the precision and the sustainable traceability of the classifiers' results and also provides warning messages that increase situational awareness during surgical interventions.

Ontology-based specification, identification and analysis of perioperative risks.

BACKGROUND: Medical personnel in hospitals often works under great physical and mental strain. In medical decision-making, errors can never be completely ruled out. Several studies have shown that between 50 and 60% of adverse events could have been avoided through better organization, more attention or more effective security procedures. Critical situations especially arise during interdisciplinary collaboration and the use of complex medical technology, for example during surgical interventions and in perioperative settings (the period of time before, during and after surgical intervention). METHODS: In this paper, we present an ontology and an ontology-based software system, which can identify risks across medical processes and supports the avoidance of errors in particular in the perioperative setting. We developed a practicable definition of the risk notion, which is easily understandable by the medical staff and is usable for the software tools. Based on this definition, we developed a Risk Identification Ontology (RIO) and used it for the specification and the identification of perioperative risks. RESULTS: An agent system was developed, which gathers risk-relevant data during the whole perioperative treatment process from various sources and provides it for risk identification and analysis in a centralized fashion. The results of such an analysis are provided to the medical personnel in form of context-sensitive hints and alerts. For the identification of the ontologically specified risks, we developed an ontology-based software module, called Ontology-based Risk Detector (OntoRiDe). CONCLUSIONS: About 20 risks relating to cochlear implantation (CI) have already been implemented. Comprehensive testing has indicated the correctness of the data acquisition, risk identification and analysis components, as well as the web-based visualization of results.

Expert2OWL: A Methodology for Pattern-Based Ontology Development.

The formalization of expert knowledge enables a broad spectrum of applications employing ontologies as underlying technology. These include eLearning, Semantic Web and expert systems. However, the manual construction of such ontologies is time-consuming and thus expensive. Moreover, experts are often unfamiliar with the syntax and semantics of formal ontology languages such as OWL and usually have no experience in developing formal ontologies. To overcome these barriers, we developed a new method and tool, called Expert2OWL that provides efficient features to support the construction of OWL ontologies using GFO (General Formal Ontology) as a top-level ontology. This method allows a close and effective collaboration between ontologists and domain experts. Essentially, this tool integrates Excel spreadsheets as part of a pattern-based ontology development and refinement process. Expert2OWL enables us to expedite the development process and modularize the resulting ontologies. We applied this method in the field of Chinese Herbal Medicine (CHM) and used Expert2OWL to automatically generate an accurate Chinese Herbology ontology (CHO). The expressivity of CHO was tested and evaluated using ontology query languages SPARQL and DL. CHO shows promising results and can generate answers to important scientific questions such as which Chinese herbal formulas contain which substances, which substances treat which diseases, and which ones are the most frequently used in CHM.

Using Fast Healthcare Interoperability Resources (FHIR) for the Integration of Risk Minimization Systems in Hospitals.

With the growing strain of medical staff and complexity of patient care, the risk of medical errors increases. In this work we present the use of Fast Healthcare Interoperability Resources (FHIR) as communication standard for the integration of an ontology- and agent-based system to identify risks across medical processes in a clinical environment.

Visualization of Large Ontologies in University Education from a Tool Point of View.

The realization of ontology visualization requirements in university education is a challenging task and should be supported by appropriate tools. This applies in particular, if the ontology to be visualized is based on a large text corpus that comprises a huge number of concepts, relations and annotations. In SNIK, we developed such an ontology of information management in hospitals in order to support the transfer of knowledge in the context of the university education. The challenge is to identify tools and methods, which are capable to support ontology visualization and usage as efficiently as possible. Related research fields (e.g. bioinformatics) are confronted with similar visualization problems. These tools and methods used could provide a suitable solution in our research field. In total, we assessed eight tools concerning the visualization of large ontologies to evaluate their suitability representing knowledge in the field of medical informatics.

An Ontology-Based Scenario for Teaching the Management of Health Information Systems.

The terminology for the management of health information systems is characterized by complexity and polysemy which is both challenging for medical informatics students and practitioners. SNIK, an ontology of information management (IMI) in hospitals, brings together IM concepts from different literature sources. Based on SNIK, we developed a blended learning scenario to teach medical informatics students IM concepts and their relationships. In proof-of-concept teaching units, students found the use of SNIK in teaching and learning motivating and useful. In the next step, the blended learning scenario will be rolled out to an international course for medical informatics students.

An Approach to Support Collaborative Ontology Construction.

The increasing number of terms used in textbooks for information management (IM) in hospitals makes it difficult for medical informatics students to grasp IM concepts and their interrelations. Formal ontologies which comprehend and represent the essential content of textbooks can facilitate the learning process in IM education. The manual construction of such ontologies is time-consuming and thus very expensive [3]. Moreover, most domain experts lack skills in using a formal language like OWL [2] and usually have no experience with standard editing tools like Protégé http://protege.stanford.edu [4,5]. This paper presents an ontology modeling approach based on Excel2OWL, a self-developed tool which efficiently supports domain experts in collaboratively constructing ontologies from textbooks. This approach was applied to classic IM textbooks, resulting in an ontology called SNIK. Our method facilitates the collaboration between domain experts and ontologists in the development process. Furthermore, the proposed approach enables ontologists to detect modeling errors and also to evaluate and improve the quality of the resulting ontology rapidly. This approach allows us to visualize the modeled textbooks and to analyze their semantics automatically. Hence, it can be used for e-learning purposes, particularly in the field of IM in hospitals.