Title: Improving Data Quality in Clinical Research Informatics Tools
Maintaining data quality is a fundamental requirement for any successful and long-term data management. Providing high-quality, reliable, and statistically sound data is a primary goal for clinical research informatics. In addition, effective data governance and management are essential to ensuring accurate data counts, reports, and validation. As a crucial step of the clinical research process, it is important to establish and maintain organization-wide standards for data quality management to ensure consistency across all systems designed primarily for cohort identification, allowing users to perform an enterprise-wide search on a clinical research data repository to determine the existence of a set of patients meeting certain inclusion or exclusion criteria. Some of the clinical research tools are referred to as de-identified data tools. Assessing and improving the quality of data used by clinical research informatics tools are both important and difficult tasks. For an increasing number of users who rely on information as one of their most important assets, enforcing high data quality levels represents a strategic investment to preserve the value of the data. In clinical research informatics, better data quality translates into better research results and better patient care. However, achieving high-quality data standards is a major task because of the variety of ways that errors might be introduced in a system and the difficulty of correcting them systematically. Problems with data quality tend to fall into two categories. The first category is related to inconsistency among data resources such as format, syntax, and semantic inconsistencies. The second category is related to poor ETL and data mapping processes. In this paper, we describe a real-life case study on assessing and improving the data quality at one of healthcare organizations. This paper compares between the results obtained from two de-identified data systems i2b2, and Epic Slicedicer, and discuss the data quality dimensions' specific to the clinical research informatics context, and the possible data quality issues between the de-identified systems. This work in paper aims to propose steps/rules for maintaining the data quality among different systems to help data managers, information systems teams, and informaticists at any health care organization to monitor and sustain data quality as part of their business intelligence, data governance, and data democratization processes. more »« less
Cicero, Carla; Koo, Michelle S; Braker, Emily; Abbott, John; Bloom, David; Campbell, Mariel; Cook, Joseph A; Demboski, John R; Doll, Andrew C; Frederick, Lindsey M; et al
(, PLOS ONE)
Meloro, Carlo
(Ed.)
More than tools for managing physical and digital objects, museum collection management systems (CMS) serve as platforms for structuring, integrating, and making accessible the rich data embodied by natural history collections. Here we describe Arctos, a scalable community solution for managing and publishing global biological, geological, and cultural collections data for research and education. Specific goals are to: (1) Describe the core features and implementation of Arctos for a broad audience with respect to the biodiversity informatics principles that enable high quality research; (2) Highlight the unique aspects of Arctos; (3) Illustrate Arctos as a model for supporting and enhancing the Digital Extended Specimen concept; and (4) Emphasize the role of the Arctos community for improving data discovery and enabling cross-disciplinary, integrative studies within a sustainable governance model. In addition to detailing Arctos as both a community of museum professionals and a collection database platform, we discuss how Arctos achieves its richly annotated data by creating a web of knowledge with deep connections between catalog records and derived or associated data. We also highlight the value of Arctos as an educational resource. Finally, we present the financial model of fiscal sponsorship by a nonprofit organization, implemented in 2022, to ensure the long-term success and sustainability of Arctos.
Meghani, Salimah H; Mooney-Doyle, Kim; Barnato, Amber; Colborn, Kathryn; Gillette, Riley; Harrison, Krista L; Hinds, Pamela S; Kirilova, Dessi; Knafl, Kathleen; Schulman-Green, Dena; et al
(, Journal of Pain and Symptom Management)
Data sharing is increasingly an expectation in health research as part of a general move toward more open sciences. In the United States, in particular, the implementation of the 2023 National Institutes of Health Data Management and Sharing Policy has made it clear that qualitative studies are not exempt from this data sharing requirement. Recognizing this trend, the Palliative Care Research Cooperative Group (PCRC) realized the value of creating a de-identified qualitative data repository to complement its existing de-identified quantitative data repository. The PCRC Data Informatics and Statistics Core leadership partnered with the Qualitative Data Repository (QDR) to establish the first serious illness and palliative care qualitative data repository in the U.S. We describe the processes used to develop this repository, called the PCRC-QDR, as well as our outreach and education among the palliative care researcher community, which led to the first ten projects to share the data in the new repository. Specifically, we discuss how we co-designed the PCRC-QDR and created tailored guidelines for depositing and sharing qualitative data depending on the original research context, establishing uniform expectations for key components of relevant documentation, and the use of suitable access controls for sensitive data. We also describe how PCRC was able to leverage its existing community to recruit and guide early depositors and outline lessons learned in evaluating the experience. This work advances the establishment of best practices in qualitative data sharing.
Jiang, Li-Qing; Pierrot, Denis; Wanninkhof, Rik; Feely, Richard A.; Tilbrook, Bronte; Alin, Simone; Barbero, Leticia; Byrne, Robert H.; Carter, Brendan R.; Dickson, Andrew G.; et al
(, Frontiers in Marine Science)
Effective data management plays a key role in oceanographic research as cruise-based data, collected from different laboratories and expeditions, are commonly compiled to investigate regional to global oceanographic processes. Here we describe new and updated best practice data standards for discrete chemical oceanographic observations, specifically those dealing with column header abbreviations, quality control flags, missing value indicators, and standardized calculation of certain properties. These data standards have been developed with the goals of improving the current practices of the scientific community and promoting their international usage. These guidelines are intended to standardize data files for data sharing and submission into permanent archives. They will facilitate future quality control and synthesis efforts and lead to better data interpretation. In turn, this will promote research in ocean biogeochemistry, such as studies of carbon cycling and ocean acidification, on regional to global scales. These best practice standards are not mandatory. Agencies, institutes, universities, or research vessels can continue using different data standards if it is important for them to maintain historical consistency. However, it is hoped that they will be adopted as widely as possible to facilitate consistency and to achieve the goals stated above.
This paper reflects on the significance of ABET’s “maverick evaluators” and what it says about the limits of accreditation as a mode of governance in US engineering education. The US system of engineering education operates as a highly complex system, where the diversity of the system is an asset to robust knowledge production and the production of a varied workforce. ABET Inc., the principal accreditation agency for engineering degree programs in the US, attempts to uphold a set of professional standards for engineering education using a voluntary, peer-based system of evaluation. Key to their approach is a volunteer army of trained program evaluators (PEVs) assigned by the engineering professional societies, who serve as the frontline workers responsible for auditing the content, learning outcomes, and continuous improvement processes utilized by every engineering degree program accredited by ABET. We take a look specifically at those who become labeled “maverick evaluators” in order to better understand how this system functions, and to understand its limitations as a form of governance in maintaining educational quality and appropriate professional standards within engineering education. ABET was established in 1932 as the Engineers’ Council for Professional Development (ECPD). The Cold War consensus around the engineering sciences led to a more quantitative system of accreditation first implemented in 1956. However, the decline of the Cold War and rising concerns about national competitiveness prompted ABET to shift to a more neoliberal model of accountability built around outcomes assessment and modeled after total quality management / continuous process improvement (TQM/CPI) processes that nominally gave PEVs greater discretion in evaluating engineering degree programs. However, conflicts over how the PEVs exercised judgment points to conservative aspects in the structure of the ABET organization, and within the engineering profession at large. This paper and the phenomena we describe here is one part of a broader, interview-based study of higher education governance and engineering educational reform within the United States. We have conducted over 300 interviews at more than 40 different academic institutions and professional organizations, where ABET and institutional responses to the reforms associated with “EC 2000,” which brought outcomes assessment to engineering education, are extensively discussed. The phenomenon of so-called “maverick evaluators” reveal the divergent professional interests that remain embedded within ABET and the engineering profession at large. Those associated with Civil and Environmental Engineering, and to a lesser extent Mechanical Engineering continue to push for higher standards of accreditation grounded in a stronger vision for their professions. While the phenomenon is complex and more subtle than we can summarize in an abstract, “maverick evaluators” emerged as a label for PEVs who interpreted their role, including determinations about whether certain content “appropriate to the field of study,” utilizing professional standards that lay outside of the consensus position held by the majority of the member of the Engineering Accreditation Commission. This, conjoined with the engineers’ epistemic aversion to uncertainty and concerns about the legal liability of their decisions, resulted in a more narrow interpretation of key accreditation criteria. The organization then designed and used a “due-process” reviews process to discipline identified shortcomings in order to limit divergent interpretations. The net result is that the bureaucratic process ABET built to obtain uniformity in accreditation outcomes, simultaneously blunts the organization’s capacity to support varied interpretations of professional standards at the program level. The apparatus has also contributed to ABET’s reputation as an organization focused on minimum standards, as opposed to one that functions as an effective driver for further change in engineering education.
Wu, Y.; Liu, C.; Sebald, L.; Nguyen, P.; Yesha, Y.
(, Lecture notes in networks and systems)
Irfan Awan; Muhammad Younas; Jamal Bentahar; Salima Benbernou
(Ed.)
Multi-site clinical trial systems face security challenges when streamlining information sharing while protecting patient privacy. In addition, patient enrollment, transparency, traceability, data integrity, and reporting in clinical trial systems are all critical aspects of maintaining data compliance. A Blockchain-based clinical trial framework has been proposed by lots of researchers and industrial companies recently, but its limitations of lack of data governance, limited confidentiality, and high communication overhead made data-sharing systems insecure and not efficient. We propose 𝖲𝗈𝗍𝖾𝗋𝗂𝖺, a privacy-preserving smart contracts framework, to manage, share and analyze clinical trial data on fabric private chaincode (FPC). Compared to public Blockchain, fabric has fewer participants with an efficient consensus protocol. 𝖲𝗈𝗍𝖾𝗋𝗂𝖺 consists of several modules: patient consent and clinical trial approval management chaincode, secure execution for confidential data sharing, API Gateway, and decentralized data governance with adaptive threshold signature (ATS). We implemented two versions of 𝖲𝗈𝗍𝖾𝗋𝗂𝖺 with non-SGX deploys on AWS blockchain and SGX-based on a local data center. We evaluated the response time for all of the access endpoints on AWS Managed Blockchain, and demonstrated the utilization of SGX-based smart contracts for data sharing and analysis.
AbuHalimeh, Ahmed. Improving Data Quality in Clinical Research Informatics Tools. Retrieved from https://par.nsf.gov/biblio/10421457. Frontiers in Big Data 5. Web. doi:10.3389/fdata.2022.871897.
AbuHalimeh, Ahmed. Improving Data Quality in Clinical Research Informatics Tools. Frontiers in Big Data, 5 (). Retrieved from https://par.nsf.gov/biblio/10421457. https://doi.org/10.3389/fdata.2022.871897
@article{osti_10421457,
place = {Country unknown/Code not available},
title = {Improving Data Quality in Clinical Research Informatics Tools},
url = {https://par.nsf.gov/biblio/10421457},
DOI = {10.3389/fdata.2022.871897},
abstractNote = {Maintaining data quality is a fundamental requirement for any successful and long-term data management. Providing high-quality, reliable, and statistically sound data is a primary goal for clinical research informatics. In addition, effective data governance and management are essential to ensuring accurate data counts, reports, and validation. As a crucial step of the clinical research process, it is important to establish and maintain organization-wide standards for data quality management to ensure consistency across all systems designed primarily for cohort identification, allowing users to perform an enterprise-wide search on a clinical research data repository to determine the existence of a set of patients meeting certain inclusion or exclusion criteria. Some of the clinical research tools are referred to as de-identified data tools. Assessing and improving the quality of data used by clinical research informatics tools are both important and difficult tasks. For an increasing number of users who rely on information as one of their most important assets, enforcing high data quality levels represents a strategic investment to preserve the value of the data. In clinical research informatics, better data quality translates into better research results and better patient care. However, achieving high-quality data standards is a major task because of the variety of ways that errors might be introduced in a system and the difficulty of correcting them systematically. Problems with data quality tend to fall into two categories. The first category is related to inconsistency among data resources such as format, syntax, and semantic inconsistencies. The second category is related to poor ETL and data mapping processes. In this paper, we describe a real-life case study on assessing and improving the data quality at one of healthcare organizations. This paper compares between the results obtained from two de-identified data systems i2b2, and Epic Slicedicer, and discuss the data quality dimensions' specific to the clinical research informatics context, and the possible data quality issues between the de-identified systems. This work in paper aims to propose steps/rules for maintaining the data quality among different systems to help data managers, information systems teams, and informaticists at any health care organization to monitor and sustain data quality as part of their business intelligence, data governance, and data democratization processes.},
journal = {Frontiers in Big Data},
volume = {5},
author = {AbuHalimeh, Ahmed},
}
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