The Effects of Displaying the Time Targets of the Manchester Triage System to Emergency Department Personnel: Prospective Crossover Study.

BACKGROUND: The use of triage systems such as the Manchester Triage System (MTS) is a standard procedure to determine the sequence of treatment in emergency departments (EDs). When using the MTS, time targets for treatment are determined. These are commonly displayed in the ED information system (EDIS) to ED staff. Using measurements as targets has been associated with a decline in meeting those targets. OBJECTIVE: This study investigated the impact of displaying time targets for treatment to physicians on processing times in the ED. METHODS: We analyzed the effects of displaying time targets to ED staff on waiting times in a prospective crossover study, during the introduction of a new EDIS in a large regional hospital in Germany. The old information system version used a module that showed the time target determined by the MTS, while the new system version used a priority list instead. Evaluation was based on 35,167 routinely collected electronic health records from the preintervention period and 10,655 records from the postintervention period. Electronic health records were extracted from the EDIS, and data were analyzed using descriptive statistics and generalized additive models. We evaluated the effects of the intervention on waiting times and the odds of achieving timely treatment according to the time targets set by the MTS. RESULTS: The average ED length of stay and waiting times increased when the EDIS that did not display time targets was used (average time from admission to treatment: preintervention phase=median 15, IQR 6-39 min; postintervention phase=median 11, IQR 5-23 min). However, severe cases with high acuity (as indicated by the triage score) benefited from lower waiting times (0.15 times as high as in the preintervention period for MTS1, only 0.49 as high for MTS2). Furthermore, these patients were less likely to receive delayed treatment, and we observed reduced odds of late treatment when crowding occurred. CONCLUSIONS: Our results suggest that it is beneficial to use a priority list instead of displaying time targets to ED personnel. These time targets may lead to false incentives. Our work highlights that working better is not the same as working faster.

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.

[Use of emergency services for suicide attempts during the SARS-CoV-2 pandemic-a prevalence study]. / Inanspruchnahme des Rettungsdiensts bei Suizidversuchen im Verlauf der SARS-CoV-2-Pandemie.

Background: The pandemic has caused several changes in the emergency care system. The deployment figures in emergency medical services have shown atypical fluctuations. This has also been explained by changes in behavior and an increased sense of stress among the population. Existing research provides hints for the increased incidence of mental health symptoms in emergency care during ongoing pandemics. Objective: In this context, this paper examines the occurrence of emergency medical services calls related to the keyword suicide in relation to total calls. Methods: This is a retrospective cross-sectional study based on routine documentation from a fire and rescue dispatch center with descriptive and exploratory data analyses. The data are divided by settlement-structural county types and compared with incidences and pandemic phases. Results: Phase 1 and 2a show a decrease in the number of dispatches during the pandemic. In addition, there is a shift in the number of dispatch cases with a context of suicide by structure types in phase 3. A decreased dispatch rate in the sparsely populated rural county is offset by an increase in the metropolitan area. Changes made to the control center system resulted in an increase in the number of dispatch cases in the context of suicide. Conclusion: Continuous mental health surveillance, including data collected by emergency medical services, can provide valuable insight. The study also highlights the need for standardization of emergency dispatch center data.

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).

BACKGROUND: Autopsy is an important tool for understanding the pathogenesis of diseases, including COVID-19. MATERIAL AND METHODS: On 15 April 2020, together with the German Society of Pathology and the Federal Association of German Pathologists, the German Registry of COVID-19 Autopsies (DeRegCOVID) was launched ( www.DeRegCOVID.ukaachen.de ). Building on this, the German Network for Autopsies in Pandemics (DEFEAT PANDEMIcs) was established on 1 September 2020. RESULTS: The main goal of DeRegCOVID is to collect and distribute de facto anonymized data on potentially all autopsies of people who have died from COVID-19 in Germany in order to meet the need for centralized, coordinated, and structured data collection and reporting during the pandemic. The success of the registry strongly depends on the willingness of the respective centers to report the data, which has developed very positively so far and requires special thanks to all participating centers. The rights to own data and biomaterials (stored decentrally) remain with each respective center. The DEFEAT PANDEMIcs network expands on this and aims to strengthen harmonization and standardization as well as nationwide implementation and cooperation in the field of pandemic autopsies. CONCLUSIONS: The extraordinary cooperation in the field of autopsies in Germany during the COVID-19 pandemic is impressively demonstrated by the establishment of DeRegCOVID, the merger of the registry of neuropathology (CNS-COVID19) with DeRegCOVID and the establishment of the autopsy network DEFEAT PANDEMIcs. It gives a strong signal for the necessity, readiness, and expertise to jointly help manage current and future pandemics by autopsy-derived knowledge.

[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)]. / Kooperatives Vorgehen der Pathologie und Neuropathologie in der COVID-19-Pandemie : Deutsches Register für COVID-19-Obduktionen (DeRegCOVID) und Deutsches Netzwerk für Autopsien in Pandemien (DEFEAT PANDEMIcs).

BACKGROUND: Autopsy is an important tool for understanding the pathogenesis of diseases, including COVID-19. MATERIAL AND METHODS: On 15 April 2020, together with the German Society of Pathology and the Federal Association of German Pathologists, the German Registry of COVID-19 Autopsies (DeRegCOVID) was launched ( www.DeRegCOVID.ukaachen.de ). Building on this, the German Network for Autopsies in Pandemics (DEFEAT PANDEMIcs) was established on 1 September 2020. RESULTS: The main goal of DeRegCOVID is to collect and distribute de facto anonymized data on potentially all autopsies of people who have died from COVID-19 in Germany in order to meet the need for centralized, coordinated, and structured data collection and reporting during the pandemic. The success of the registry strongly depends on the willingness of the respective centers to report the data, which has developed very positively so far and requires special thanks to all participating centers. The rights to own data and biomaterials (stored decentrally) remain with each respective center. The DEFEAT PANDEMIcs network expands on this and aims to strengthen harmonization and standardization as well as nationwide implementation and cooperation in the field of pandemic autopsies. CONCLUSIONS: The extraordinary cooperation in the field of autopsies in Germany during the COVID-19 pandemic is impressively demonstrated by the establishment of DeRegCOVID, the merger of the registry of neuropathology (CNS-COVID19) with DeRegCOVID and the establishment of the autopsy network DEFEAT PANDEMIcs. It gives a strong signal for the necessity, readiness, and expertise to jointly help manage current and future pandemics by autopsy-derived knowledge.

Data Integration for a Global Deep-Phenotyping Registry for Pulmonary Hypertension - Lessons Learned.

The integration of data from various healthcare centers into disease registries is pivotal for facilitating collaborative research and enhancing clinical insights. In this study, we investigate the integration process of existing registries into the PVRI GoDeep meta-registry, focusing on the complexities and challenges encountered. We detail the integration process, including data transformation, mapping updates, and feedback mechanisms. Our findings underscore the importance of standardized processes and proactive communication in addressing data quality issues, ultimately enhancing the reliability and trustworthiness of meta-registry data. Through careful harmonization of the data and transparent documentation of data processing, we pave the way for leveraging registry data to drive advancements in pulmonary hypertension research and patient care.

Beating the Clock: A Prediction Model for Timely Care in Emergency Departments.

We developed and validated a statistical prediction model using 2.5 electronic health records from 24 German emergency departments (EDs) to estimate treatment timeliness at triage. The model's moderate fit and reliance on interoperable, routine data suggest its potential for implementation in ED crowding management.

A Pipeline for the Usage of the Core Data Set of the Medical Informatics Initiative for Process Mining - A Technical Case Report.

INTRODUCTION: Process Mining (PM) has emerged as a transformative tool in healthcare, facilitating the enhancement of process models and predicting potential anomalies. However, the widespread application of PM in healthcare is hindered by the lack of structured event logs and specific data privacy regulations. CONCEPT: This paper introduces a pipeline that converts routine healthcare data into PM-compatible event logs, leveraging the newly available permissions under the Health Data Utilization Act to use healthcare data. IMPLEMENTATION: Our system exploits the Core Data Sets (CDS) provided by Data Integration Centers (DICs). It involves converting routine data into Fast Healthcare Interoperable Resources (FHIR), storing it locally, and subsequently transforming it into standardized PM event logs through FHIR queries applicable on any DIC. This facilitates the extraction of detailed, actionable insights across various healthcare settings without altering existing DIC infrastructures. LESSONS LEARNED: Challenges encountered include handling the variability and quality of data, and overcoming network and computational constraints. Our pipeline demonstrates how PM can be applied even in complex systems like healthcare, by allowing for a standardized yet flexible analysis pipeline which is widely applicable.The successful application emphasize the critical role of tailored event log generation and data querying capabilities in enabling effective PM applications, thus enabling evidence-based improvements in healthcare processes.

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 the Registry to Evaluate Early and Long-Term PAH Disease Mangement (REVEAL) Lite 2, REVEAL 2.0, European Society of Cardiology/European Respiratory Society 2022, Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (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 (interquartile range, 33-52 mm Hg) and pulmonary vascular resistance of 7 Wood units (WU) (interquartile range, 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. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT05329714; URL: www. CLINICALTRIALS: gov.

Phosphodiesterase-5 Inhibitor Treatment is Associated with Improved Survival in Pulmonary Hypertension Associated with COPD in the PVRI GoDeep Meta-Registry.

BACKGROUND: Patients with COPD frequently develop pulmonary hypertension (PH-COPD). Severe PH-COPD, identified by a pulmonary vascular resistance (PVR) >5 Wood Units (WU), is closely linked to impaired transplant-free survival. The impact of PH-targeting pharmacotherapy in this context remains unclear. RESEARCH QUESTION: Is PH-targeted therapy associated with improved transplant-free survival in PH-COPD? STUDY DESIGN AND METHODS: This study included PVRI GoDeep meta-registry patients with PH-COPD and available right heart catheterization at diagnosis. We investigated PH-targeted therapy prevalence and its association with transplant-free survival using diverse statistical methods, including Cox regression and subgroup analyses based on PH severity, comorbidities, and pulmonary function tests. Immortal time bias was addressed through a landmark approach. RESULTS: As of December 2023, the GoDeep meta-registry included 26981 patients (28% PH-Group 1, 13% PH-Group 2, 12% PH-Group 3, 10% PH-Group 4, 2% PH-Group 5, 26% undefined and 9% control). Out of these, 836 patients were diagnosed as PH-COPD and included in this analysis, with median age 66 [59,73]years, FEV1 51 [34,69]%predicted, mPAP 35 [28,44]mmHg, PVR 5 [4,8]WU, cardiac index 2.5 [2.0,2.9]L/min.m2, and mostly WHO functional class III were included. 5-year transplant-free survival was 42%, significantly worse than group 1 PH. A multivariable Cox proportional hazards model identified PVR, but not FEV1 as a major predictor of outcome. 418 patients (50%) received phosphodiesterase-5 inhibitor (PDE5i) therapy, which was associated with significantly reduced mortality: hazard ratio 0.65 [0.57,0.75] for the entire PH-COPD cohort and 0.83 [0.74,0.94] when performing landmark analysis. This PDE5i effect was robustly reproduced when performing subgroup analyses for patients with moderate/severe PH, various comorbidities, and supplemental oxygen requirement, and when assessing the impact of unobserved confounders. INTERPRETATION: PH-COPD patients exhibit poor transplant-free survival, with PVR being a predictor of mortality. In this meta-registry, PDE5i therapy was associated with a significant reduction in mortality across all tested models.

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.

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.

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.

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).

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.

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/.

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.

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.

Generation of a Fast Healthcare Interoperability Resources (FHIR)-based Ontology for Federated Feasibility Queries in the Context of COVID-19: Feasibility Study.

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.

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.