BackgroundMedical 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).MethodsIn 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.ResultsAn 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).ConclusionsAbout 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.
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.
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.
The constant upward movement of data-driven medicine as a valuable option to enhance daily clinical practice has brought new challenges for data analysts to get access to valuable but sensitive data due to privacy considerations. One solution for most of these challenges are Distributed Analytics (DA) infrastructures, which are technologies fostering collaborations between healthcare institutions by establishing a privacy-preserving network for data sharing. However, in order to participate in such a network, a lot of technical and administrative prerequisites have to be made, which could pose bottlenecks and new obstacles for non-technical personnel during their deployment. We have identified three major problems in the current state-of-the-art. Namely, the missing compliance with FAIR data principles, the automation of processes, and the installation. In this work, we present a seamless on-boarding workflow based on a DA reference architecture for data sharing institutions to address these problems. The on-boarding service manages all technical configurations and necessities to reduce the deployment time. Our aim is to use well-established and conventional technologies to gain acceptance through enhanced ease of use. We evaluate our development with six institutions across Germany by conducting a DA study with open-source breast cancer data, which represents the second contribution of this work. We find that our on-boarding solution lowers technical barriers and efficiently deploys all necessary components and is, therefore, indeed an enabler for collaborative data sharing.
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.
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