[Automated surveillance and risk prediction with the aim of risk-stratified infection control and prevention (RISK PRINCIPE)]. / Automatisierte Surveillance und Risikovorhersage mit dem Ziel einer risikostratifizierten Infektionskontrolle und -prävention (RISK Prediction for Risk-stratified Infection Control and Prevention).

Healthcare-associated infections (HCAIs) represent an enormous burden for patients, healthcare workers, relatives and society worldwide, including Germany. The central tasks of infection prevention are recording and evaluating infections with the aim of identifying prevention potential and risk factors, taking appropriate measures and finally evaluating them. From an infection prevention perspective, it would be of great value if (i) the recording of infection cases was automated and (ii) if it were possible to identify particularly vulnerable patients and patient groups in advance, who would benefit from specific and/or additional interventions.To achieve this risk-adapted, individualized infection prevention, the RISK PRINCIPE research project develops algorithms and computer-based applications based on standardised, large datasets and incorporates expertise in the field of infection prevention.The project has two objectives: a) to develop and validate a semi-automated surveillance system for hospital-acquired bloodstream infections, prototypically for HCAI, and b) to use comprehensive patient data from different sources to create an individual or group-specific infection risk profile.RISK PRINCIPE is based on bringing together the expertise of medical informatics and infection medicine with a focus on hygiene and draws on information and experience from two consortia (HiGHmed and SMITH) of the German Medical Informatics Initiative (MII), which have been working on use cases in infection medicine for more than five years.

Achieving Interoperable Datasets in Pediatrics: A Data Integration Approach.

Despite their increased secondary value for developing applications and knowledge gain, routine, harmonized and standardized datasets are often not available in Pediatrics. We propose a data integration pipeline towards an interoperable routine dataset in pediatric intensive care medicine. Our three-level approach involves identifying relevant data from primary source systems, developing local data integration processes, and converting data into a standardized, interoperable format using openEHR. We modeled 15 openEHR templates and established 31 interoperable ETL processes, resulting in anonymized, standardized data of about 4,200 pediatric patients that were loaded into a harmonized database. Based on our pipeline and templates, we successfully integrated the first part of this data in our openEHR data repository. We seek to inspire other pediatric intensive care units to adopt similar approaches, with the aim of breaking down heterogenous data silos and promoting secondary use of routine data.

Improving Emergency Service Quality and Patient Safety Through the Use of Cross-Sectoral Digital Feedback Systems - An Overview.

BACKGROUND: Feedback is essential for personal and professional development, also in emergency services. However, EMS usually ends at the interface with the emergency department, thus, the long-term effect of initiated emergency measures often remains unclear for emergency personnel. Digital, data-driven tools providing systematic feedback on patients' outcome may be valuable to improve emergency service quality and patient safety. OBJECTIVES: To provide an overview about current approaches for cross-sectoral digital feedback systems in EMS. METHODS: Literature review in PubMed/MEDLINE and Google Scholar in accordance with the PRISMA statement. RESULTS: The search resulted in 567 articles out of which only three were identified as eligible. In only one study cross-sectoral feedback was used to improve quality. CONCLUSION: Although feedback is described as a method for improving the quality of the rescue service and feedback is also considered in the description of the requirements for digitization, there seems to be no technical implementation of a cross-sectoral feedback system so far.

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.

Tracing the Progression of Sepsis in Critically Ill Children: Clinical Decision Support for Detection of Hematologic Dysfunction.

BACKGROUND: One of the major challenges in pediatric intensive care is the detection of life-threatening health conditions under acute time constraints and performance pressure. This includes the assessment of pediatric organ dysfunction (OD) that demands extraordinary clinical expertise and the clinician's ability to derive a decision based on multiple information and data sources. Clinical decision support systems (CDSS) offer a solution to support medical staff in stressful routine work. Simultaneously, detection of OD by using computerized decision support approaches has been scarcely investigated, especially not in pediatrics. OBJECTIVES: The aim of the study is to enhance an existing, interoperable, and rule-based CDSS prototype for tracing the progression of sepsis in critically ill children by augmenting it with the capability to detect SIRS/sepsis-associated hematologic OD, and to determine its diagnostic accuracy. METHODS: We reproduced an interoperable CDSS approach previously introduced by our working group: (1) a knowledge model was designed by following the commonKADS methodology, (2) routine care data was semantically standardized and harmonized using openEHR as clinical information standard, (3) rules were formulated and implemented in a business rule management system. Data from a prospective diagnostic study, including 168 patients, was used to estimate the diagnostic accuracy of the rule-based CDSS using the clinicians' diagnoses as reference. RESULTS: We successfully enhanced an existing interoperable CDSS concept with the new task of detecting SIRS/sepsis-associated hematologic OD. We modeled openEHR templates, integrated and standardized routine data, developed a rule-based, interoperable model, and demonstrated its accuracy. The CDSS detected hematologic OD with a sensitivity of 0.821 (95% CI: 0.708-0.904) and a specificity of 0.970 (95% CI: 0.942-0.987). CONCLUSION: We could confirm our approach for designing an interoperable CDSS as reproducible and transferable to other critical diseases. Our findings are of direct practical relevance, as they present one of the first interoperable CDSS modules that detect pediatric SIRS/sepsis-associated hematologic OD.

Towards an Evolutionary Open Pediatric Intensive Care Dataset in the ELISE Project.

BACKGROUND: To embrace the need for freely accessible training data sets originating from the real world, in the ELISE project, we integrate source data from a pediatric intensive care unit and provide it to researchers. OBJECTIVE: We present our vision, initial results and steps on a trail towards an evolutionary open pediatric intensive care data set. METHODS: Our evolution plan for the data set comprises three steps. The final data set will include raw clinical data and labels on critical outcomes such as organ dysfunction and sepsis, generated automatically by computerized and well-evaluated methods. RESULTS: First step resulted in an initial version data set available in a central repository. CONCLUSIONS: Our approach has great potential to provide a comprehensive open intensive care data set labeled for critical pediatric outcomes and, thus, contributing to overcome the current lack of real-world pediatric intensive care data usable for training data-driven algorithms.

Tracing COVID-19 Infection Chains Within Healthcare Institutions - Another Brick in the Wall Against SARS-CoV-2.

Early anticipation of COVID-19 infection chains within hospitals is of high importance for initiating suitable measures at the right time. Infection control specialists can be supported by application systems able of consolidating and analyzing heterogeneous, up-to-now non-standardized and distributed data needed for tracking COVID-19 infections and infected patients' hospital contacts. We developed a system, Co-Surv-SmICS, assisting in infection chain detection, in an open and standards-based way to ensure reusability of the system across institutions. Data is modelled in alignment to various national modelling initiatives and consensus data definitions, queried in a standardized way by the use of OpenEHR as information modelling standard and its associated model-based query language, analyzed and interactively visualized in the application. A first version has been published and will be enhanced with further features and evaluated in detail with regard to its potentials to support specialists during their work against SARS-CoV-2.

Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data.

Critical care can benefit from analyzing data by machine learning approaches for supporting clinical routine and guiding clinical decision-making. Developing data-driven approaches for an early detection of systemic inflammatory response syndrome (SIRS) in patients of pediatric intensive care and exploring the possibility of an approach using training data sets labeled automatically beforehand by knowledge-based approaches rather than clinical experts. Using naïve Bayes classifier and an artificial neuronal network (ANN), trained with real data labeled by (1) domain experts ad (2) a knowledge-based decision support system (CDSS). Accuracies were evaluated by the data set labeled by domain experts using a 10-fold cross validation. The ANN approach trained with data labeled by domain experts yielded a specificity of 0.9139 and sensitivity of 0.8979, whereas the approach trained with a data set labeled by a knowledge-based CDSS achieves a specificity of 0.9220 and a sensitivity of 0.8887. ANN yielded promising results for data-driven detection of pediatric SIRS with real data. Our comparison shows the feasibility of using training data labeled automatically by knowledge-based approaches rather than manually allocated by experts.

Prediction models for SIRS, sepsis and associated organ dysfunctions in paediatric intensive care: study protocol for a diagnostic test accuracy study.

INTRODUCTION: Systemic inflammatory response syndrome (SIRS), sepsis and associated organ dysfunctions are life-threating conditions occurring at paediatric intensive care units (PICUs). Early recognition and treatment within the first hours of onset are critical. However, time pressure, lack of personnel resources, and the need for complex age-dependent diagnoses impede an accurate and timely diagnosis by PICU physicians. Data-driven prediction models integrated in clinical decision support systems (CDSS) could facilitate early recognition of disease onset. OBJECTIVES: To estimate the sensitivity and specificity of previously developed prediction models (index tests) for the detection of SIRS, sepsis and associated organ dysfunctions in critically ill children up to 12 hours before reference standard diagnosis is possible. METHODS AND ANALYSIS: We conduct a monocentre, prospective diagnostic test accuracy study. Clinicians in the PICU of the tertiary care centre Hannover Medical School, Germany, continuously screen and recruit patients until the adaptive sample size (originally intended sample size of 500 patients) is enrolled. Eligible are children (0-17 years, all sexes) who stay in the PICU for ≥12 hours and for whom an informed consent is given. All eligible patients are independently assessed for SIRS, sepsis and organ dysfunctions using corresponding predictive and knowledge-based CDSS models. The knowledge-based CDSS models serve as imperfect reference standards. The assessments are used to estimate the sensitivities and specificities of each predictive model using a clustered nonparametric approach (main analysis). Subgroup analyses ('age groups', 'sex' and 'age groups by sex') are predefined. ETHICS AND DISSEMINATION: This study obtained ethics approval from the Hannover Medical School Ethics Committee (No. 10188_BO_SK_2022). Results will be disseminated as peer-reviewed publications, at scientific conferences, and to patients in an appropriate dissemination approach. TRIAL REGISTRATION NUMBER: This study was registered with the German Clinical Trial Register (DRKS00029071) on 2022-05-23. PROTOCOL VERSION: 10188_BO_SK_2022_V.2.0-20220330_4_Studienprotokoll.

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.

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 evaluation of an interoperable clinical decision-support system for the detection of systemic inflammatory response syndrome in critically ill children.

BACKGROUND: Systemic inflammatory response syndrome (SIRS) is defined as a non-specific inflammatory process in the absence of infection. SIRS increases susceptibility for organ dysfunction, and frequently affects the clinical outcome of affected patients. We evaluated a knowledge-based, interoperable clinical decision-support system (CDSS) for SIRS detection on a pediatric intensive care unit (PICU). METHODS: The CDSS developed retrieves routine data, previously transformed into an interoperable format, by using model-based queries and guideline- and knowledge-based rules. We evaluated the CDSS in a prospective diagnostic study from 08/2018-03/2019. 168 patients from a pediatric intensive care unit of a tertiary university hospital, aged 0 to 18 years, were assessed for SIRS by the CDSS and by physicians during clinical routine. Sensitivity and specificity (when compared to the reference standard) with 95% Wald confidence intervals (CI) were estimated on the level of patients and patient-days. RESULTS: Sensitivity and specificity was 91.7% (95% CI 85.5-95.4%) and 54.1% (95% CI 45.4-62.5%) on patient level, and 97.5% (95% CI 95.1-98.7%) and 91.5% (95% CI 89.3-93.3%) on the level of patient-days. Physicians' SIRS recognition during clinical routine was considerably less accurate (sensitivity of 62.0% (95% CI 56.8-66.9%)/specificity of 83.3% (95% CI 80.4-85.9%)) when measurd on the level of patient-days. Evaluation revealed valuable insights for the general design of the CDSS as well as specific rule modifications. Despite a lower than expected specificity, diagnostic accuracy was higher than the one in daily routine ratings, thus, demonstrating high potentials of using our CDSS to help to detect SIRS in clinical routine. CONCLUSIONS: We successfully evaluated an interoperable CDSS for SIRS detection in PICU. Our study demonstrated the general feasibility and potentials of the implemented algorithms but also some limitations. In the next step, the CDSS will be optimized to overcome these limitations and will be evaluated in a multi-center study. TRIAL REGISTRATION: NCT03661450 (ClinicalTrials.gov); registered September 7, 2018.

Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing.

BACKGROUND: Merging disparate and heterogeneous datasets from clinical routine in a standardized and semantically enriched format to enable a multiple use of data also means incorporating unstructured data such as medical free texts. Although the extraction of structured data from texts, known as natural language processing (NLP), has been researched at least for the English language extensively, it is not enough to get a structured output in any format. NLP techniques need to be used together with clinical information standards such as openEHR to be able to reuse and exchange still unstructured data sensibly. OBJECTIVES: The aim of the study is to automatically extract crucial information from medical free texts and to transform this unstructured clinical data into a standardized and structured representation by designing and implementing an exemplary pipeline for the processing of pediatric medical histories. METHODS: We constructed a pipeline that allows reusing medical free texts such as pediatric medical histories in a structured and standardized way by (1) selecting and modeling appropriate openEHR archetypes as standard clinical information models, (2) defining a German dictionary with crucial text markers serving as expert knowledge base for a NLP pipeline, and (3) creating mapping rules between the NLP output and the archetypes. The approach was evaluated in a first pilot study by using 50 manually annotated medical histories from the pediatric intensive care unit of the Hannover Medical School. RESULTS: We successfully reused 24 existing international archetypes to represent the most crucial elements of unstructured pediatric medical histories in a standardized form. The self-developed NLP pipeline was constructed by defining 3.055 text marker entries, 132 text events, 66 regular expressions, and a text corpus consisting of 776 entries for automatic correction of spelling mistakes. A total of 123 mapping rules were implemented to transform the extracted snippets to an openEHR-based representation to be able to store them together with other structured data in an existing openEHR-based data repository. In the first evaluation, the NLP pipeline yielded 97% precision and 94% recall. CONCLUSION: The use of NLP and openEHR archetypes was demonstrated as a viable approach for extracting and representing important information from pediatric medical histories in a structured and semantically enriched format. We designed a promising approach with potential to be generalized, and implemented a prototype that is extensible and reusable for other use cases concerning German medical free texts. In a long term, this will harness unstructured clinical data for further research purposes such as the design of clinical decision support systems. Together with structured data already integrated in openEHR-based representations, we aim at developing an interoperable openEHR-based application that is capable of automatically assessing a patient's risk status based on the patient's medical history at time of admission.

Clinical Decision-Support Systems for Detection of Systemic Inflammatory Response Syndrome, Sepsis, and Septic Shock in Critically Ill Patients: A Systematic Review.

BACKGROUND: The design of computerized systems able to support automated detection of threatening conditions in critically ill patients such as systemic inflammatory response syndrome (SIRS) and sepsis has been fostered recently. The increase of research work in this area is due to both the growing digitalization in health care and the increased appreciation of the importance of early sepsis detection and intervention. To be able to understand the variety of systems and their characteristics as well as performances, a systematic literature review is required. Existing reviews on this topic follow a rather restrictive searching methodology or they are outdated. As much progress has been made during the last 5 years, an updated review is needed to be able to keep track of current developments in this area of research. OBJECTIVES: To provide an overview about current approaches for the design of clinical decision-support systems (CDSS) in the context of SIRS, sepsis, and septic shock, and to categorize and compare existing approaches. METHODS: A systematic literature review was performed in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. Searches for eligible articles were conducted on five electronic bibliographic databases, including PubMed/MEDLINE, IEEE Xplore, Embase, Scopus, and ScienceDirect. Initial results were screened independently by two reviewers based on clearly defined eligibility criteria. A backward as well as an updated search enriched the initial results. Data were extracted from included articles and presented in a standardized way. Articles were classified into predefined categories according to characteristics extracted previously. The classification was performed according to the following categories: clinical setting including patient population and mono- or multicentric study, support type of the system such as prediction or detection, systems characteristics such as knowledge- or data-driven algorithms used, evaluation of methodology, and results including ground truth definition, sensitivity, and specificity. All results were assessed qualitatively by two reviewers. RESULTS: The search resulted in 2,373 articles out of which 55 results were identified as eligible. Over 80% of the articles describe monocentric studies. More than 50% include adult patients, and only four articles explicitly report the inclusion of pediatric patients. Patient recruitment often is very selective, which can be observed from highly varying inclusion and exclusion criteria. The task of disease detection is covered in 62% of the articles; prediction of upcoming conditions in 33%. Sepsis is covered in 67% of the articles, SIRS as sole entity in only 4%, whereas 27% focus on severe sepsis and/or septic shock. The most common combinations of categories "algorithm used" and "support type" are knowledge-based detection of sepsis and data-driven prediction of sepsis. In evaluations, manual chart review (38%) and diagnosis coding (29%) represent the most frequently used ground truth definitions; most studies present a sample size between 10,001 and 100,000 cases (31%) and performances highly differ with only five articles presenting sensitivities and specificities above 90%; four of them using knowledge-based rather than machine learning algorithms. The presentations of holistic CDSS approaches, including technical implementation details, system interfaces, and data and interoperability aspects enabling the use of CDSS in routine settings are missing in nearly all articles. CONCLUSIONS: The review demonstrated the high variety of research in this context successfully. A clear trend is observable toward the use of data-driven algorithms, and a lack of research could be identified in covering the pediatric population as well as acknowledging SIRS as an independent and threatening condition. The quality as well as the significance of the presented evaluations for assessing the performances of the algorithms in clinical routine settings are often not meeting the current standard of scientific work. Our future interest will be concentrated on these realistic settings by implementing and evaluating SIRS detection approaches as well as considering factors to make the CDSS useable in clinical routine from both technical and medical perspectives.

CADDIE2-evaluation of a clinical decision-support system for early detection of systemic inflammatory response syndrome in paediatric intensive care: study protocol for a diagnostic study.

INTRODUCTION: Systemic inflammatory response syndrome (SIRS) is one of the most critical indicators determining the clinical outcome of paediatric intensive care patients. Clinical decision support systems (CDSS) can be designed to support clinicians in detection and treatment. However, the use of such systems is highly discussed as they are often associated with accuracy problems and 'alert fatigue'. We designed a CDSS for detection of paediatric SIRS and hypothesise that a high diagnostic accuracy together with an adequate alerting will accelerate the use. Our study will (1) determine the diagnostic accuracy of the CDSS compared with gold standard decisions created by two blinded, experienced paediatricians, and (2) compare the system's diagnostic accuracy with that of routine clinical care decisions compared with the same gold standard. METHODS AND ANALYSIS: CADDIE2 is a prospective diagnostic accuracy study taking place at the Department of Pediatric Cardiology and Intensive Care Medicine at the Hannover Medical School; it represents the second step towards our vision of cross-institutional and data-driven decision-support for intensive care environments (CADDIE). The study comprises (1) recruitment of up to 300 patients (start date 1 August 2018), (2) creation of gold standard decisions (start date 1 May 2019), (3) routine SIRS assessments by physicians (starts with recruitment), (4) SIRS assessments by a CDSS (start date 1 May 2019), and (5) statistical analysis with a modified approach for determining sensitivity and specificity and comparing the accuracy results of the different diagnostic approaches (planned start date 1 July 2019). ETHICS AND DISSEMINATION: Ethics approval was obtained at the study centre (Ethics Committee of Hannover Medical School). Results of the main study will be communicated via publication in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov NCT03661450; Pre-results.

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.

A Report on Archetype Modelling in a Nationwide Data Infrastructure Project.

BACKGROUND: The nationwide data infrastructure project HiGHmed strives for achieving semantic interoperability through the use of openEHR archetypes. Therefore, a knowledge governance framework defining collaborative modelling processes has been established. For long-sustained success and the creation of high-quality archetypes, continuous monitoring is vital. OBJECTIVES: To present an update on archetype modelling and governance framework establishment in HiGHmed. METHODS: Qualitative and quantitative analyses of the progress in establishing modelling groups, roles and users, realizing modelling workflows, and modelling archetypes. RESULTS: Currently, 25 modellers and 17 domain experts are participating. 79 archetypes have been identified, from which 69 are pre-existing and internationally published; completion rates of review rounds are satisfying but improvable. CONCLUSIONS: The governance framework is valuable to make the activities manageable and to accelerate modelling. Combined with highly engaged data stewards and clinicians, a reasonable number of archetypes have already been developed.

Assessment of Decision Models for Hybrid Approaches.

A wide range of Clinical Decision Support Systems (CDSS) have been developed. These CDSS are based on decision models, which normally have a knowledge- or data-driven approach. In this work a structured development of potential hybrid approaches was realized by the assessment of decision models and identification of their advantages and disadvantages. For the assessment of different decision models, eight criteria were identified and three of them were chosen as main criteria for CDSS: transparency, learning aptitude and handling of uncertain and vague knowledge. The comparison of decision models in regard to the developed main criteria resulted in an identification of three groups of models with similar characteristics. Based on these groups hybrid approaches had been developed, so that different decision models could be combined in a beneficial way. Thereby this work provides an instrument for a structured development of hybrid decision models.

Learning Healthcare Systems in Pediatrics: Cross-Institutional and Data-Driven Decision-Support for Intensive Care Environments (CADDIE).

BACKGROUND: The vast amount of data generated in healthcare can be reused to support decision-making by developing clinical decision-support systems. Since evidence is lacking in Pediatrics, it seems to be beneficial to design future systems towards the vision of generating evidence through cross-institutional data analysis and continuous learning cycles. OBJECTIVES: Presentation of an approach for cross-institutional and data-driven decision support in pediatric intensive care units (PICU), and the long-term vision of Learning Healthcare Systems in Pediatrics. METHODS: Using a four-step approach, including the design of interoperable decision-support systems and data-driven algorithms, for establishing a Learning Health Cycle. RESULTS: We developed and started to follow that approach on exemplary of systemic inflammatory response syndrome (SIRS) detection in PICU. CONCLUSIONS: Our approach has great potential to establish our vision of learning systems, which support decision-making in PICU by analyzing cross-institutional data and giving insights back to both, their own knowledge base and clinical care, to continuously learn about practices and evidence in Pediatrics.

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.