Data Quality Feedback Research Articles

De-identified data quality assessment approaches by data vendors who license data to healthcare and life sciences researchers.

To gain insights into how data vendor companies (DVs), an important source of de-identified/anonymized licensed patient-related data (D/ALD) used in clinical informatics research in life sciences and the pharmaceutical industry, characterize, conduct, and communicate data quality assessments to researcher purchasers of D/ALD. A qualitative study with interviews of DVs executives and decision-makers in data quality assessments (n = 12) and content analysis of interviews transcripts. Data quality, from the perspective of DVs, is characterized by how it is defined, validated, and processed. DVs identify data quality as the main contributor to successful collaborations with life sciences/pharmaceutical research partners. Data quality feedback from clients provides the basis for DVs reviews and inspections of quality processes. DVs value customer interactions, view collaboration, shared common goals, mutual expertise, and communication related to data quality as success factors. Data quality evaluation practices are important. However, no uniform DVs industry standards for data quality assessment were identified. DVs describe their orientation to data quality evaluation as a direct result of not only the complex nature of data sources, but also of techniques, processes, and approaches used to construct data sets. Because real-world data (RWD), eg, patient data from electronic medical records, is used for real-world evidence (RWE) generation, the use of D/ALD will expand and require refinement. The focus on (and rigor in) data quality assessment (particularly in research necessary to make regulatory decisions) will require more structure, standards, and collaboration between DVs, life sciences/pharmaceutical, informaticists, and RWD/RWE policy-making stakeholders.

Open-source FlexNIRS: A low-cost, wireless and wearable cerebral health tracker

Currently, there is great interest in making neuroimaging widely accessible and thus expanding the sampling population for better understanding and preventing diseases. The use of wearable health devices has skyrocketed in recent years, allowing continuous assessment of physiological parameters in patients and research cohorts. While most health wearables monitor the heart, lungs and skeletal muscles, devices targeting the brain are currently lacking. To promote brain health in the general population, we developed a novel, low-cost wireless cerebral oximeter called FlexNIRS. The device has 4 LEDs and 3 photodiode detectors arranged in a symmetric geometry, which allows for a self-calibrated multi-distance method to recover cerebral hemoglobin oxygenation (SO2) at a rate of 100 Hz. The device is powered by a rechargeable battery and uses Bluetooth Low Energy (BLE) for wireless communication. We developed an Android application for portable data collection and real-time analysis and display. Characterization tests in phantoms and human participants show very low noise (noise-equivalent power <70 fW/√Hz) and robustness of SO2 quantification in vivo. The estimated cost is on the order of $50/unit for 1000 units, and our goal is to share the device with the research community following an open-source model. The low cost, ease-of-use, smart-phone readiness, accurate SO2 quantification, real time data quality feedback, and long battery life make prolonged monitoring feasible in low resource settings, including typically medically underserved communities, and enable new community and telehealth applications.

Challenges For Implementing Collections Data Quality Feedback: synthesizing the community experience

Much data quality (DQ) feedback is now available to data providers from aggregators of collections specimen and related data. Similarly, transcription centres and crowdsourcing platforms also provide data that must be assessed and often manipulated before uploading to a local database and subsequently published with aggregators. In order to facilitate broader DQ information use aggregators (GBIF, ALA, iDigBio, VertNet) and others, through the TDWG BDQ Interest Group, are harmonizing the DQ information provided - transforming part of the DQ feedback process. But, collections sharing data face challenges when trying to evaluate and integrate the information changes offered (by aggregators) for given records in local collection management systems and collection databases. Sharing DQ integration experiences can help reveal risks and opportunities. Discovering others have the same conundrums helps develop a community of belonging and may assist in removing duplication of effort. It is important to leverage the knowledge and experience of those who are currently validating data to improve the efficiency and effectiveness of the process. Documenting and classifying these challenges also facilitates motivation and community building by informing those who would tackle these challenges. In this case, talks from aggregators and data providers give all of us a chance to learn from their stories about implementing and integrating DQ feedback. Following the symposium, a special interest group (SIG at SPNHC) meeting offers everyone an opportunity to add their experiences with aggregator DQ feedback. See the SIG meeting: "Add Your Input to Challenges for Implementing Collections Data Quality Feedback: synthesizing the community experience", for details. For tractable issues, we plan to assemble and note the expected ways in which these barriers can be overcome. Where possible, we can tap into existing community resources (SPNHC, TDWG, biology.stackexchange.com, iDigBio, etc.) to help our data providers implement future data updates and track changes. At the same time, we plan to analyze the intractable issues - documenting why they remain challenging - and what, if any potential solutions are available or likely to be available in the future. This information provides future projects like DiSSCo (Distributed System of Scientific Collections) and BCoN (Biodiversity Collections Network) and others worldwide the information required to plan more effectively for cyber/human infrastructure. Synthesizing this input helps visionaries better understand, anticipate and support DQ management and data mobilization efforts going forward by informing design of future proposals and global projects structured with these outcomes in mind. At the end of the workshop, we intend to publish our findings, and merge them with the results of a global survey on the same topic.

Integrating Data Quality Feedback: a Data Provider's Perspective

The Herbarium of Rancho Santa Ana Botanic Garden [RSA-POM] is the third largest herbarium in California and consists of &amp;gt;1.2 million specimens, of which ~50% are digitized. As a data provider, RSA-POM publishes its data with several aggregators, including the Consortium of California Herbaria, JSTOR, Symbiota (which is subsequently pulled into iDigBio and GBIF), as well as its own local webportal. Each submission of data needs to be prepared and formatted according to the aggregator’s specifications for publication. Feedback on data quality (DQ) ranges from an individual user (often only a few records at a time) to large aggregators (frequently in large batches). While some DQ items are easy fixes with little time and effort to correct, others can be more challenging and often require expertise beyond the skillset of curatorial staff. In other instances, there are issues concerning an aggregator’s ability to provide updated data for repatriation. This talk will discuss the efforts of the RSA-POM Herbarium to provide data to various aggregators as well as perspectives on the challenges, limitations, and constraints when integrating DQ items from an aggregator back into the local database.

Who Has Time for Biological Collections Data Quality Feedback? Maybe a Community Can Help

At an institution without a permanent collections manager or curators, who has time to publish data or research issues on that data? Collections with little or no institutional support often benefit from passionate volunteers who continually seek ways to keep them relevant. The University of Texas at El Paso Biodiversity Collections (UTEP-BC) has been cared for in this manner by a small group of dedicated faculty and emeritus curators who have managed with no budget to care for the specimens, perform and publish research about them, and publish a good portion of the collections data. An IMLS grant allowed these dedicated volunteers to hire a Collections Manager who would migrate the already published data from the collections and add unpublished specimen records from the in-house developed FileMaker Pro database to a new collection management system (Arctos) that would allow for better records management and ease of publication. Arctos is a publicly searchable web-based system, but most collections also see the benefit of participation with biodiversity data aggregators such as the Global Biodiversity Information Facility (GBIF), iDigBio, and a multitude of discipline-specific aggregators. Publication of biodiversity data to aggregators is loaded with hidden pathways, acronyms, and tech-speak with which a curator, registrar, or collections manager may not be familiar. After navigating the process to publish the data the reward is feedback! Now data can be improved, and everyone wins, right? In the case of UTEP-BC data, the feedback sits idle as the requirements of the grant under which the Collection Manager was hired take precedence. It will likely remain buried until long after the grant has run its course. Fortunately, the selection of Arctos as a collection management system allowed the UTEP-BC Collection Manager to confer with others publishing biodiversity data to the data aggregators. Members of the Arctos Community have carried on multiple conversations about publishing to aggregators and how to handle the resulting data quality flags. These conversations provide a synthesis of the challenges experienced by collections in over 20 institutions when publishing biodiversity data to aggregators and responding (or not) to their data quality flags. This presentation will cover the experiences and concerns of one Collection Manager as well as those of the Arctos Community related to publishing data to aggregators, deciphering their data quality flags, and development of appropriate responses to those flags.

DA+ data acquisition and analysis software at the Swiss Light Source macromolecular crystallography beamlines.

Data acquisition software is an essential component of modern macromolecular crystallography (MX) beamlines, enabling efficient use of beam time at synchrotron facilities. Developed at the Paul Scherrer Institute, the DA+ data acquisition software is implemented at all three Swiss Light Source (SLS) MX beamlines. DA+ consists of distributed services and components written in Python and Java, which communicate via messaging and streaming technologies. The major components of DA+ are the user interface, acquisition engine, online processing and database. Immediate data quality feedback is achieved with distributed automatic data analysis routines. The software architecture enables exploration of the full potential of the latest instrumentation at the SLS MX beamlines, such as the SmarGon goniometer and the EIGER X 16M detector, and development of new data collection methods.

Quality of HIV Testing Data Before and After the Implementation of a National Data Quality Assessment and Feedback System.

In 2010, the Centers for Disease Control and Prevention (CDC) implemented a national data quality assessment and feedback system for CDC-funded HIV testing program data. Our objective was to analyze data quality before and after feedback. Coinciding with required quarterly data submissions to CDC, each health department received data quality feedback reports and a call with CDC to discuss the reports. Data from 2008 to 2011 were analyzed. Fifty-nine state and local health departments that were funded for comprehensive HIV prevention services. Data collected by a service provider in conjunction with a client receiving HIV testing. National data quality assessment and feedback system. Before and after intervention implementation, quality was assessed through the number of new test records reported and the percentage of data values that were neither missing nor invalid. Generalized estimating equations were used to assess the effect of feedback in improving the completeness of variables. Data were included from 44 health departments. The average number of new records per submission period increased from 197 907 before feedback implementation to 497 753 afterward. Completeness was high before and after feedback for race/ethnicity (99.3% vs 99.3%), current test results (99.1% vs 99.7%), prior testing and results (97.4% vs 97.7%), and receipt of results (91.4% vs 91.2%). Completeness improved for HIV risk (83.6% vs 89.5%), linkage to HIV care (56.0% vs 64.0%), referral to HIV partner services (58.9% vs 62.8%), and referral to HIV prevention services (55.3% vs 63.9%). Calls as part of feedback were associated with improved completeness for HIV risk (adjusted odds ratio [AOR] = 2.28; 95% confidence interval [CI], 1.75-2.96), linkage to HIV care (AOR = 1.60; 95% CI, 1.31-1.96), referral to HIV partner services (AOR = 1.73; 95% CI, 1.43-2.09), and referral to HIV prevention services (AOR = 1.74; 95% CI, 1.43-2.10). Feedback contributed to increased data quality. CDC and health departments should continue monitoring the data and implement measures to improve variables of low completeness.