Data Quality Assessment Tools Research Articles

Automatic transparency evaluation for open knowledge extraction systems

BackgroundThis paper proposes Cyrus, a new transparency evaluation framework, for Open Knowledge Extraction (OKE) systems. Cyrus is based on the state-of-the-art transparency models and linked data quality assessment dimensions. It brings together a comprehensive view of transparency dimensions for OKE systems. The Cyrus framework is used to evaluate the transparency of three linked datasets, which are built from the same corpus by three state-of-the-art OKE systems. The evaluation is automatically performed using a combination of three state-of-the-art FAIRness (Findability, Accessibility, Interoperability, Reusability) assessment tools and a linked data quality evaluation framework, called Luzzu. This evaluation includes six Cyrus data transparency dimensions for which existing assessment tools could be identified. OKE systems extract structured knowledge from unstructured or semi-structured text in the form of linked data. These systems are fundamental components of advanced knowledge services. However, due to the lack of a transparency framework for OKE, most OKE systems are not transparent. This means that their processes and outcomes are not understandable and interpretable. A comprehensive framework sheds light on different aspects of transparency, allows comparison between the transparency of different systems by supporting the development of transparency scores, gives insight into the transparency weaknesses of the system, and ways to improve them. Automatic transparency evaluation helps with scalability and facilitates transparency assessment. The transparency problem has been identified as critical by the European Union Trustworthy Artificial Intelligence (AI) guidelines. In this paper, Cyrus provides the first comprehensive view of transparency dimensions for OKE systems by merging the perspectives of the FAccT (Fairness, Accountability, and Transparency), FAIR, and linked data quality research communities.ResultsIn Cyrus, data transparency includes ten dimensions which are grouped in two categories. In this paper, six of these dimensions, i.e., provenance, interpretability, understandability, licensing, availability, interlinking have been evaluated automatically for three state-of-the-art OKE systems, using the state-of-the-art metrics and tools. Covid-on-the-Web is identified to have the highest mean transparency.ConclusionsThis is the first research to study the transparency of OKE systems that provides a comprehensive set of transparency dimensions spanning ethics, trustworthy AI, and data quality approaches to transparency. It also demonstrates how to perform automated transparency evaluation that combines existing FAIRness and linked data quality assessment tools for the first time. We show that state-of-the-art OKE systems vary in the transparency of the linked data generated and that these differences can be automatically quantified leading to potential applications in trustworthy AI, compliance, data protection, data governance, and future OKE system design and testing.

Childhood Routine Immunization Data in Enugu: Findings from a Quality Assessment Survey

ABSTRACT Background: Immunization data are critical for the effective delivery of immunization programs, health planning, and-decision-making. It can also help to identify gaps in vaccine coverage and service delivery. Immunization data quality is dependent on the quality of the collection process at individual health facilities and its transmission to an integrated system. Aim: This study set out to appraise the quality of childhood routine immunization data across the different levels of transmission in selected health facilities in Enugu, Nigeria. Materials and Methods: This was a cross-sectional, analytical study of the immunization data of two vaccine antigens in 103 health facilities across 10 local council areas offering routine immunization services in Enugu state. Accuracy, discrepancy consistency, completeness, and timeliness of the data for the year 2020, the study period, were determined using WHO data quality assessment tools. Results: This study noted that childhood routine immunization data from health facilities to the state are inconsistent, incomplete, and untimely in the transmission. Accuracy ratios were 110 and 108, discrepancy -10 and -8 for BCG and Penta III, respectively, at the health facility level. These signify the underreporting of the two vaccine antigens. This pattern was observed to be reversed as the data were transmitted from the local councils to DHIS 2. Timeliness of reporting did not go beyond 65% for the entire year 2020, while the completeness of reporting was below 80% for the same period. Conclusion: There were significant changes in childhood routine immunization data as it were transmitted across the different levels of the data management processes in the state. Routine immunization data from health facilities in the state are inconsistent, incomplete, and untimely in their transmission. This calls for interventions to improve data quality and make use of it in decision-making.

Data quality evaluation in wearable monitoring.

Wearable recordings of neurophysiological signals captured from the wrist offer enormous potential for seizure monitoring. Yet, data quality remains one of the most challenging factors that impact data reliability. We suggest a combined data quality assessment tool for the evaluation of multimodal wearable data. We analyzed data from patients with epilepsy from four epilepsy centers. Patients wore wristbands recording accelerometry, electrodermal activity, blood volume pulse, and skin temperature. We calculated data completeness and assessed the time the device was worn (on-body), and modality-specific signal quality scores. We included 37,166h from 632 patients in the inpatient and 90,776h from 39 patients in the outpatient setting. All modalities were affected by artifacts. Data loss was higher when using data streaming (up to 49% among inpatient cohorts, averaged across respective recordings) as compared to onboard device recording and storage (up to 9%). On-body scores, estimating the percentage of time a device was worn on the body, were consistently high across cohorts (more than 80%). Signal quality of some modalities, based on established indices, was higher at night than during the day. A uniformly reported data quality and multimodal signal quality index is feasible, makes study results more comparable, and contributes to the development of devices and evaluation routines necessary for seizure monitoring.

Quality of Maternal & Newborns Health indicators in Western Province of Rwanda

Data quality is defined as a measure of data status that fulfills the following elements: accuracy, completeness, consistency, reliability, and if the data is current. The World Health Organization (WHO) reported that only 40% of all countries have an adequate system to collect information on birth and deaths. Even though the system is there, vital registration systems are inaccurate and incomplete in developing countries. In Rwanda, maternal health related data was over-reported more than other indicators. These are the main reasons for conducting the study to investigate the data quality of four maternal and newborn health indicators reported by Rwandan Western Province health centers. This concurrent-mixed method study included 61 data managers and 12 key informants. Routine data quality assessment tool and structured interview guide were used to collect data. Descriptive statistics were used to get proportion of respondents’ socio-demographic characteristics. The analysis was done for assessing median of data quality index. The results show that 55.7% of data managers were male while 58.3% of responsible of maternity were female. Majority (58.9%) of participants was in age’s category from 33-42, 61.6% have A1 education level and 53.4% have experience less than five years. Data quality index of one out of four (25%) MNH indicators was found below 95% accepted by WHO. The main reasons for insufiscient quality of data are lack of data validation meetings (57.5%) and incompleteness of reporting tools (36.4%). Monthly data validation meetings chaired by HC leaders are important to contribute to high-quality data in healthcare settings. Supportive supervisions done in data quality and management have to be organized in a supportive, and educative way.

Data cleaning and restoring method for vehicle battery big data platform

Battery is one of the most important and costly devices in electric vehicles (EVs). Developing an efficient battery management method is of great significance to enhancing vehicle safety and economy. Recently developed big-data and cloud platform computing technologies bring a bright perspective for efficient utilization and protection of vehicle batteries. However, a reliable data transmission network and a high-quality cloud battery dataset are indispensable to enable this benefit.This paper makes the first effort to systematically solve data quality problems in cloud-based vehicle battery monitoring and management by developing a novel integrated battery data cleaning framework. In the first stage, the outlier samples are detected by analyzing the temporal features in the battery data time series. The outlier data in the dataset can be accurately detected to avoid their impacts on battery monitoring and management. Then, the abnormal samples, including the noise polluted data and missing value, are restored by a novel future fusion data restoring model. The real electric bus operation data collected by a cloud-based battery monitoring and management platform are used to verify the performance of the developed data cleaning method. More than 93.3% of outlier samples can be detected, and the data restoring error can be limited to 2.11%, which validates the effectiveness of the developed methods. The proposed data cleaning method provides an effective data quality assessment tool in cloud-based vehicle battery management, which can further boost the practical application of the vehicle big data platform and Internet of vehicle.

Assessment of neonatal mortalities and stillbirths data quality in Offinso North District of Ghana

Background: Stillbirths and neonatal deaths when poorly documented or collated, negatively affect the quality of decision and interventions. This study sought to assess the quality of routine neonatal mortalities and stillbirth records in health facilities and propose interventions to improve the data quality gaps.
 Method: Descriptive cross-sectional study was employed. This study was carried out at three (3) purposively selected health facilities in Offinso North district. Stillbirths and neonatal deaths recorded in registers from 2015 to 2017, were recounted and compared with monthly aggregated data and District Health Information Management System 2 (DHIMS 2) data using a self-developed Excel Data Quality Assessment Tool (DQS). An observational checklist was used to collect primary data on completeness and availability. Accuracy ratio (verification factor), discrepancy rate, percentage availability and completeness of stillbirths and neonatal mortality data were computed using the DQS tool.
 Findings: The results showed high discrepancy rate of stillbirth data recorded in registers compared with monthly aggregated reports (12.5%), and monthly aggregated reports compared with DHIMS 2 (13.5%). Neonatal mortalities data were under-reported in monthly aggregated reports, but over-reported in DHIMS 2. Overall data completeness was about 84.6%, but only 68.5% of submitted reports were supervised by facility in-charges. Delivery and admission registers availability were 100% and 83.3% respectively.
 Conclusion: Quality of stillbirths and neonatal mortality data in the district is generally encouraging, but are not reliable for decision-making. Routine data quality audit is needed to reduce high discrepancies in stillbirth and neonatal mortality data in the district.

Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository.

Background Many research initiatives aim at using data from electronic health records (EHRs) in observational studies. Participating sites of the German Medical Informatics Initiative (MII) established data integration centers to integrate EHR data within research data repositories to support local and federated analyses. To address concerns regarding possible data quality (DQ) issues of hospital routine data compared with data specifically collected for scientific purposes, we have previously presented a data quality assessment (DQA) tool providing a standardized approach to assess DQ of the research data repositories at the MIRACUM consortium's partner sites. Objectives Major limitations of the former approach included manual interpretation of the results and hard coding of analyses, making their expansion to new data elements and databases time-consuming and error prone. We here present an enhanced version of the DQA tool by linking it to common data element definitions stored in a metadata repository (MDR), adopting the harmonized DQA framework from Kahn et al and its application within the MIRACUM consortium. Methods Data quality checks were consequently aligned to a harmonized DQA terminology. Database-specific information were systematically identified and represented in an MDR. Furthermore, a structured representation of logical relations between data elements was developed to model plausibility-statements in the MDR. Results The MIRACUM DQA tool was linked to data element definitions stored in a consortium-wide MDR. Additional databases used within MIRACUM were linked to the DQ checks by extending the respective data elements in the MDR with the required information. The evaluation of DQ checks was automated. An adaptable software implementation is provided with the R package DQAstats . Conclusion The enhancements of the DQA tool facilitate the future integration of new data elements and make the tool scalable to other databases and data models. It has been provided to all ten MIRACUM partners and was successfully deployed and integrated into their respective data integration center infrastructure.

The ATLAS Tile Calorimeter Tools for Data Quality Assessment

The ATLAS Tile Calorimeter (TileCal) is the central part of the hadronic calorimeter of the ATLAS experiment and provides important information for reconstruction of hadrons, jets, hadronic decays of tau leptons and missing transverse energy. The readout is segmented into nearly 10000 channels that are calibrated by means of Cesium source, laser, charge injection, and integratorbased systems. The data quality (DQ) relies on extensive monitoring of both collision and calibration data. Automated checks are performed on a set of pre-defined histograms and results are summarized in dedicated web pages. A set of tools is then used by the operators for further inspection of the acquired data with the goal of spotting the origins of problems or other irregularities. Consequently, the TileCal conditions data (calibration constants, channel statuses etc) are updated in databases that are used for the data-reprocessing, or serve as an important input for the maintenance works during the shutdown periods. This talk reviews the software tools used for the DQ monitoring with emphasis on recent developments aiming to integrate all tools into a single platform.

Assessing completeness of patient medical records of surgical and obstetric patients in Northern Tanzania

ABSTRACT Background Strengthening surgical services in resource-constrained settings is contingent on using high-quality data to inform decision making at clinical, facility, and policy levels. However, the evidence is sparse on gaps in paper-based medical record quality for surgical and obstetric patients in low-resource settings. Objective We aim to examine surgical and obstetric patient medical record data quality in health facilities as part of a surgical system strengthening initiative in northern Tanzania. Methods To measure the incidence of Surgical Site Infections (SSIs), sepsis and maternal sepsis surgical and obstetric inpatients were followed prospectively, over three months in ten primary, district, and regional health facilities in northern Tanzania. Between April 22nd to May 1st, 2018, we retrospectively reviewed paper-based medical records of surgical and obstetric patients diagnosed with SSIs, post-operative sepsis, and maternal sepsis in the three-month follow-up period. A data quality assessment tool with18 data elements related to documentation of SSIs and sepsis diagnosis, their respective symptoms and vital signs, inpatient daily monitoring indicators, and demographic information was developed and used to assess the completeness of patient medical records. Results Among the 157 patients diagnosed with SSI and sepsis, we found and reviewed 68% of all medical records. Among records reviewed, approximately one third (34%) and one quarter (23%) included documentation of SSI and sepsis diagnoses, respectively. 6% of reviewed records included documentation of all SSI and sepsis diagnoses, symptoms and vital signs, inpatient daily monitoring indicators, and demographic data. Conclusions Strengthening data quality and record-keeping is essential for surgical team communication, continuity of care, and patient safety, especially in low resource settings where paper-based records are the primary means of data collection. High-quality primary health information provides facilities with actionable data for improving surgical and obstetric care quality at the facility level.

Transforming French Electronic Health Records into the Observational Medical Outcome Partnership's Common Data Model: A Feasibility Study.

Common data models (CDMs) enable data to be standardized, and facilitate data exchange, sharing, and storage, particularly when the data have been collected via distinct, heterogeneous systems. Moreover, CDMs provide tools for data quality assessment, integration into models, visualization, and analysis. The observational medical outcome partnership (OMOP) provides a CDM for organizing and standardizing databases. Common data models not only facilitate data integration but also (and especially for the OMOP model) extends the range of available statistical analyses. This study aimed to evaluate the feasibility of implementing French national electronic health records in the OMOP CDM. The OMOP's specifications were used to audit the source data, specify the transformation into the OMOP CDM, implement an extract-transform-load process to feed data from the French health care system into the OMOP CDM, and evaluate the final database. Seventeen vocabularies corresponding to the French context were added to the OMOP CDM's concepts. Three French terminologies were automatically mapped to standardized vocabularies. We loaded nine tables from the OMOP CDM's "standardized clinical data" section, and three tables from the "standardized health system data" section. Outpatient and inpatient data from 38,730 individuals were integrated. The median (interquartile range) number of outpatient and inpatient stays per patient was 160 (19-364). Our results demonstrated that data from the French national health care system can be integrated into the OMOP CDM. One of the main challenges was the use of international OMOP concepts to annotate data recorded in a French context. The use of local terminologies was an obstacle to conceptual mapping; with the exception of an adaptation of the International Classification of Diseases 10th Revision, the French health care system does not use international terminologies. It would be interesting to extend our present findings to the 65 million people registered in the French health care system.

Erratum: Transforming French Electronic Health Records into the Observational Medical Outcome Partnership's Common Data Model: A Feasibility Study.

Background Common data models (CDMs) enable data to be standardized, and facilitate data exchange, sharing, and storage, particularly when the data have been collected via distinct, heterogeneous systems. Moreover, CDMs provide tools for data quality assessment, integration into models, visualization, and analysis. The observational medical outcome partnership (OMOP) provides a CDM for organizing and standardizing databases. Common data models not only facilitate data integration but also (and especially for the OMOP model) extends the range of available statistical analyses. Objective This study aimed to evaluate the feasibility of implementing French national electronic health records in the OMOP CDM. Methods The OMOP's specifications were used to audit the source data, specify the transformation into the OMOP CDM, implement an extract–transform–load process to feed data from the French health care system into the OMOP CDM, and evaluate the final database. Results Seventeen vocabularies corresponding to the French context were added to the OMOP CDM's concepts. Three French terminologies were automatically mapped to standardized vocabularies. We loaded nine tables from the OMOP CDM's “standardized clinical data” section, and three tables from the “standardized health system data” section. Outpatient and inpatient data from 38,730 individuals were integrated. The median (interquartile range) number of outpatient and inpatient stays per patient was 160 (19–364). Conclusion Our results demonstrated that data from the French national health care system can be integrated into the OMOP CDM. One of the main challenges was the use of international OMOP concepts to annotate data recorded in a French context. The use of local terminologies was an obstacle to conceptual mapping; with the exception of an adaptation of the International Classification of Diseases 10th Revision, the French health care system does not use international terminologies. It would be interesting to extend our present findings to the 65 million people registered in the French health care system.

QUANTIS: Data quality assessment tool by clustering analysis

AbstractAutomatically generated kinetic networks are ideally validated against a large set of accurate, reproducible, and easy‐to‐model experimental data. However, although this might seem simple, it proves to be quite challenging. QUANTIS, a publicly available Python package, is specifically developed to evaluate both the precision and accuracy of experimental data and to ensure a uniform, quick processing, and storage strategy that enables automated comparison of developed kinetic models. The precision is investigated with two clustering techniques, PCA and t‐SNE, whereas the accuracy is probed with checks for the conservation laws. First, the developed tool processes, evaluates, and stores experimental yield data automatically. All data belonging to a given experiment, both unprocessed and processed, are stored in the form of an HDF5 container. The demonstration of QUANTIS on three different pyrolysis cases showed that it can help in identifying and overcoming instabilities in experimental datasets, reduce mass and molar balance closure discrepancies, and, by evaluating the visualized correlation matrices, increase understanding in the underlying reaction pathways. Inclusion of all experimental data in the HDF5 file makes it possible to automate simulating the experiment with CHEMKIN. Because of the employed InChI string identifiers for molecules, it is possible to automate the comparison experiment/simulation. QUANTIS and the concepts demonstrated therein is a potentially useful tool for data quality assessment, kinetic model validation, and refinement.

Clinical Research Informatics: Contributions from 2018.

Summary Objectives : To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2018. Method : A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting of the editorial team was organized to conclude on the selection of best papers. Results : Among the 1,469 retrieved papers published in 2018 in the various areas of CRI, the full review process selected four best papers. The first best paper describes a simple algorithm detecting co-morbidities in Electronic Healthcare Records (EHRs) using a clinical data warehouse and a knowledge base. The authors of the second best paper present a federated algorithm for predicting heart failure hospital admissions based on patients' medical history described in their distributed EHRs. The third best paper reports the evaluation of an open source, interoperable, and scalable data quality assessment tool measuring completeness of data items, which can be run on different architectures (EHRs and Clinical Data Warehouses (CDWs) based on PCORnet or OMOP data models). The fourth best paper reports a data quality program conducted across 37 hospitals addressing data quality Issues through the whole data life cycle from patient to researcher. Conclusions : Research efforts in the CRI field currently focus on consolidating promises of early Distributed Research Networks aimed at maximizing the potential of large-scale, harmonized data from diverse, quickly developing digital sources. Data quality assessment methods and tools as well as privacy-enhancing techniques are major concerns. It is also notable that, following examples in the US and Asia, ambitious regional or national plans in Europe are launched that aim at developing big data and new artificial intelligence technologies to contribute to the understanding of health and diseases in whole populations and whole health systems, and returning actionable feedback loops to improve existing models of research and care. The use of “real-world" data is continuously increasing but the ultimate role of this data in clinical research remains to be determined.

A high-frequency mobile phone data collection approach for research in social-environmental systems: Applications in climate variability and food security in sub-Saharan Africa

Collecting high-frequency social-environmental data about farming practices in sub-Saharan Africa can provide new insight into environmental changes that farmers face and how they respond within smallholder agro-ecosystems. Traditional data collection methods such as agricultural censuses are costly and not useful for understanding intra-annual and real-time decisions. Short-message service (SMS) has the potential to transform the nature of data collection in coupled social-ecological systems. We present a system for collecting, managing, and synthesizing weekly data from farmers, including data infrastructure for management of big and heterogeneous datasets; probabilistic data quality assessment tools; and visualization and analysis tools such as mapping and regression techniques. We discuss limitations of collecting social-environmental data via SMS and data integration challenges that arise when linking these data with other social and environmental data. In combination with high-frequency environmental data, such data will help ameliorate issues of scale mismatch and build resilience in environmental systems.

Data Quality Assessment and Multi-Organizational Reporting: Tools to Enhance Network Knowledge.

Objective:Multi-organizational research requires a multi-organizational data quality assessment (DQA) process that combines and compares data across participating organizations. We demonstrate how such a DQA approach complements traditional checks of internal reliability and validity by allowing for assessments of data consistency and the evaluation of data patterns in the absence of an external “gold standard.”Methods:We describe the DQA process employed by the Data Coordinating Center (DCC) for Kaiser Permanente’s (KP) Center for Effectiveness and Safety Research (CESR). We emphasize the CESR DQA reporting system that compares data summaries from the eight KP organizations in a consistent, standardized manner.Results:We provide examples of multi-organization comparisons from DQA to confirm expectations about different aspects of data quality. These include: 1) comparison of direct data extraction from the electronic health records (EHR) and 2) comparison of non-EHR data from disparate sources.Discussion:The CESR DCC has developed codes and procedures for efficiently implementing and reporting DQA. The CESR DCC approach is to 1) distribute DQA tools to empower data managers at each organization to assess their data quality at any time, 2) summarize and disseminate findings to address data shortfalls or document idiosyncrasies, and 3) engage data managers and end-users in an exchange of knowledge about the quality and its fitness for use.Conclusion:The KP CESR DQA model is applicable to networks hoping to improve data quality. The multi-organizational reporting system promotes transparency of DQA, adds to network knowledge about data quality, and informs research.

ExpoQual: Evaluating measured and modeled human exposure data

Recent rapid technological advances are producing exposure data sets for which there are no available data quality assessment tools. At the same time, regulatory agencies are moving in the direction of data quality assessment for environmental risk assessment and decision-making. A transparent and systematic approach to evaluating exposure data will aid in those efforts. Any approach to assessing data quality must consider the level of quality needed for the ultimate use of the data. While various fields have developed approaches to assess data quality, there is as yet no general, user-friendly approach to assess both measured and modeled data in the context of a fit-for-purpose risk assessment. Here we describe ExpoQual, an instrument developed for this purpose which applies recognized parameters and exposure data quality elements from existing approaches for assessing exposure data quality. Broad data streams such as quantitative measured and modeled human exposure data as well as newer and developing approaches can be evaluated. The key strength of ExpoQual is that it facilitates a structured, reproducible and transparent approach to exposure data quality evaluation and provides for an explicit fit-for-purpose determination. ExpoQual was designed to minimize subjectivity and to include transparency in aspects based on professional judgment. ExpoQual is freely available on-line for testing and user feedback (exposurequality.com).

An integrated system for data quality and conditions assessment for the ATLAS Tile Calorimeter

An integrated system for data quality and conditions assessment for the ATLAS Tile Calorimeter is known amongst the ATLAS Tile Calorimeter as the Tile-in-One. It is a platform for combining all of the ATLAS Tile Calorimeter offline data quality tools in one unified web interface. It achieves this by using simple main web server to serve as central hub and group of small web applications called plugins, which provide the data quality assessment tools. Every plugin runs in its own virtual machine in order to prevent interference between the plugins and also to increase stability of the platform.

Exposure Data Quality Issues and TSCA Assessments

EPA’s Federal Register Notice on Procedures for Chemical Risk Evaluation under the Amended Toxic Substances Control Act includes several references to the use of quality assessments for evaluating information and inclusion of quality assessments in systematic reviews. The Notice also points to the importance of using predefined criteria for assessing quality. However, rapid technological advances are producing exposure data for which there are no available data quality assessment tools. While various fields have developed approaches to rigorously and transparently assessing data quality, there is as yet no holistic approach for assessing both measured and modeled data in the context of a fit-for-purpose assessment. Using key elements from existing approaches to assessing exposure data quality, we developed an instrument - ExpoQual - for evaluating the quality of human exposure data that is applicable to both traditional sources of exposure data (the focus here is primarily quantitative measured and modeled data) as well as newer and developing approaches and includes fit-for-purpose considerations. The key strength of ExpoQual is that it facilitates a structured, reproducible and transparent approach to exposure data quality evaluation and forces an explicit determination with regards to fit-for-purpose. The instrument is agnostic regarding whether error introduced by quality issues is systematic (biased in a particular direction) or random and the framework construct does not include data rejection. ExpoQual is designed for development into an online tool; early uses with case studies and feedback from the exposure community will result in an iterative process of instrument advancement. In this presentation we describe the ExpoQual instrument and a case study testing the efficacy of the instrument.

Exploring completeness in clinical data research networks with DQe-c.

ObjectiveTo provide an open source, interoperable, and scalable data quality assessment tool for evaluation and visualization of completeness and conformance in electronic health record (EHR) data repositories.Materials and MethodsThis article describes the tool’s design and architecture and gives an overview of its outputs using a sample dataset of 200 000 randomly selected patient records with an encounter since January 1, 2010, extracted from the Research Patient Data Registry (RPDR) at Partners HealthCare. All the code and instructions to run the tool and interpret its results are provided in the Supplementary Appendix.ResultsDQe-c produces a web-based report that summarizes data completeness and conformance in a given EHR data repository through descriptive graphics and tables. Results from running the tool on the sample RPDR data are organized into 4 sections: load and test details, completeness test, data model conformance test, and test of missingness in key clinical indicators.DiscussionOpen science, interoperability across major clinical informatics platforms, and scalability to large databases are key design considerations for DQe-c. Iterative implementation of the tool across different institutions directed us to improve the scalability and interoperability of the tool and find ways to facilitate local setup.ConclusionEHR data quality assessment has been hampered by implementation of ad hoc processes. The architecture and implementation of DQe-c offer valuable insights for developing reproducible and scalable data science tools to assess, manage, and process data in clinical data repositories.

Acoustic data quality assessment tools and findings for ocean observing systems

Ocean Networks Canada (ONC) operates long time series, ocean observatories in the Pacific and Arctic. These include the large VENUS and NEPTUNE observatories, many small community based observatories and the Underwater Listening Station (ULS) for the Vancouver Fraser Port Authority. Passive acoustic monitoring systems are a component of all ONC observatories and passive acoustic data quality is therefore a concern. All the observing systems have multiple underwater electronics and sensor types, many of which can negatively impact the passive acoustic sensor data. Hydrophone sensitivity degradation due to time, water absorption, and biofouling need to be assessed to ensure accurate ambient noise measurements and accurate vessel underwater radiated noise level measurements. The performance and suitability of the hydrophones for specific areas also needs to be assessed so the acoustic analysts can be aware of the hydrophone induced data limitations. ONC has been examining the use of in situ calibration verifications, spectral probability density (SPD) plots, spectrograms, and wave data as tools to assess the passive acoustic data quality. The preliminary findings on the impact of all of the above acoustic error sources are presented.