Using the data quality dashboard to improve the ehden network

Federated networks of observational health databases have the potential to be a rich resource to inform clinical practice and regulatory decision making. However, the lack of standard data quality processes makes it difficult to know if these data are research ready. The EHDEN COVID-19 Rapid Collaboration Call presented the opportunity to assess how the newly developed open-source tool Data Quality Dashboard (DQD) informs the quality of data in a federated network. Fifteen Data Partners (DPs) from 10 different countries worked with the EHDEN taskforce to map their data to the OMOP CDM. Throughout the process at least two DQD results were collected and compared for each DP. All DPs showed an improvement in their data quality between the first and last run of the DQD. The DQD excelled at helping DPs identify and fix conformance issues but showed less of an impact on completeness and plausibility checks. This is the first study to apply the DQD on multiple, disparate databases across a network. While study-specific checks should still be run, we recommend that all data holders converting their data to the OMOP CDM use the DQD as it ensures conformance to the model specifications and that a database meets a baseline level of completeness and plausibility for use in research.</p

European Health Data and Evidence Network—learnings from building out a standardized international health data network

Objective: Health data standardized to a common data model (CDM) simplifies and facilitates research. This study examines the factors that make standardizing observational health data to the Observational Medical Outcomes Partnership (OMOP) CDM successful. Materials and methods: Twenty-five data partners (DPs) from 11 countries received funding from the European Health Data Evidence Network (EHDEN) to standardize their data. Three surveys, DataQualityDashboard results, and statistics from the conversion process were analyzed qualitatively and quantitatively. Our measures of success were the total number of days to transform source data into the OMOP CDM and participation in network research. Results: The health data converted to CDM represented more than 133 million patients. 100%, 88%, and 84% of DPs took Surveys 1, 2, and 3. The median duration of the 6 key extract, transform, and load (ETL) processes ranged from 4 to 115 days. Of the 25 DPs, 21 DPs were considered applicable for analysis of which 52% standardized their data on time, and 48% participated in an international collaborative study. Discussion: This study shows that the consistent workflow used by EHDEN proves appropriate to support the successful standardization of observational data across Europe. Over the 25 successful transformations, we confirmed that getting the right people for the ETL is critical and vocabulary mapping requires specific expertise and support of tools. Additionally, we learned that teams that proactively prepared for data governance issues were able to avoid considerable delays improving their ability to finish on time. Conclusion: This study provides guidance for future DPs to standardize to the OMOP CDM and participate in distributed networks. We demonstrate that the Observational Health Data Sciences and Informatics community must continue to evaluate and provide guidance and support for what ultimately develops the backbone of how community members generate evidence

RADAR-base/radar-android-faros: Release 0.1.1

Changes from 0.1.0 Updates to radar-commons-android 0.7.0 Improvements on artifact publishin

Stochastic simulation of prokaryotic two-component signalling indicates stochasticity-induced active-state locking and growth-rate dependent bistability

Signal transduction by prokaryotes almost exclusively relies on two-component systems for sensing and responding to (extracellular) signals. Here, we use stochastic models of two-component systems to better understand the impact of stochasticity on the fidelity and robustness of signal transmission, the outcome of autoregulatory gene expression and the influence of cell growth and division. We report that two-component systems are remarkably robust against copy number fluctuations of the signalling proteins they are composed of, which enhances signal transmission fidelity. Furthermore, we find that due to stochasticity these systems can get locked in an active state for extended time periods when (initially high) signal levels drop to zero. This behaviour can contribute to a bet-hedging adaptation strategy, aiding survival in fluctuating environments. Additionally, autoregulatory gene expression can cause two-component systems to become bistable at realistic parameter values. As a result, two sub-populations of cells can co-exist—active and inactive cells, which contributes to fitness in unpredictable environments. Bistability also proved robust with respect to cell growth and division, and is tunable by the growth rate. In conclusion, our results indicate how single cells can cope with the inevitable stochasticity occurring in the activity of their two-component systems. They are robust to disadvantageous fluctuations that scramble signal transduction and they exploit beneficial stochasticity that generates fitness-enhancing heterogeneity across an isogenic population of cells

Transforming and evaluating electronic health record disease phenotyping algorithms using the OMOP common data model: a case study in heart failure

OBJECTIVE: The aim of the study was to transform a resource of linked electronic health records (EHR) to the OMOP common data model (CDM) and evaluate the process in terms of syntactic and semantic consistency and quality when implementing disease and risk factor phenotyping algorithms. MATERIALS AND METHODS: Using heart failure (HF) as an exemplar, we represented three national EHR sources (Clinical Practice Research Datalink, Hospital Episode Statistics Admitted Patient Care, Office for National Statistics) into the OMOP CDM 5.2. We compared the original and CDM HF patient population by calculating and presenting descriptive statistics of demographics, related comorbidities, and relevant clinical biomarkers. RESULTS: We identified a cohort of 502 536 patients with the incident and prevalent HF and converted 1 099 195 384 rows of data from 216 581 914 encounters across three EHR sources to the OMOP CDM. The largest percentage (65%) of unmapped events was related to medication prescriptions in primary care. The average coverage of source vocabularies was >98% with the exception of laboratory tests recorded in primary care. The raw and transformed data were similar in terms of demographics and comorbidities with the largest difference observed being 3.78% in the prevalence of chronic obstructive pulmonary disease (COPD). CONCLUSION: Our study demonstrated that the OMOP CDM can successfully be applied to convert EHR linked across multiple healthcare settings and represent phenotyping algorithms spanning multiple sources. Similar to previous research, challenges mapping primary care prescriptions and laboratory measurements still persist and require further work. The use of OMOP CDM in national UK EHR is a valuable research tool that can enable large-scale reproducible observational research

RADAR-base/radar-android-phone: Release 0.5.0

Changes from 0.1.5 Poll battery on set interval dependencies to latest version

RADAR-base/RADAR-Schemas: radar-schemas 0.3.5

&lt;p&gt;Changes since version 0.3.4:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Added question ID for questionnaires&lt;/li&gt; &lt;li&gt;Upgraded Gradle&lt;/li&gt; &lt;li&gt;Upgraded dependencies&lt;/li&gt; &lt;li&gt;Wait for zookeeper and Kafka before registering topics&lt;/li&gt; &lt;li&gt;Register all topics simultaneously&lt;/li&gt; &lt;li&gt;Enable connector sources in the Catalog server&lt;/li&gt; &lt;li&gt;Add vender, model and version in connectors&lt;/li&gt; &lt;li&gt;Fixed Bittium Faros temperature topic name&lt;/li&gt; &lt;/ul&gt

RADAR-base/RADAR-Schemas: RADAR-Schemas release version 0.5.2

Changes since version 0.5.1 &lt;ul&gt; &lt;li&gt;Adds Active app interaction events schema and specifications&lt;/li&gt; &lt;li&gt;More explicit bootstrap server logging&lt;/li&gt; &lt;li&gt;Update dependencies&lt;/li&gt; &lt;/ul&gt

RADAR-base/RADAR-Schemas: radar-schemas version 0.4.3

&lt;p&gt;Changes since version 0.4.2:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Deprecated &lt;code&gt;active/questionniare/notification&lt;/code&gt; schema&lt;/li&gt; &lt;li&gt;Added &lt;code&gt;ApplicationDeviceInfo&lt;/code&gt; to &lt;code&gt;radar-android-application-status&lt;/code&gt;&lt;/li&gt; &lt;li&gt;Added &lt;code&gt;timeNotification&lt;/code&gt; field to active app schemas&lt;/li&gt; &lt;li&gt;Updated tool dependencies&lt;/li&gt; &lt;/ul&gt