Ten simple rules for organizations to support research data sharing

Champieux, Robin; Solomonides, Anthony; Conte, Marisa; Rojevsky, Svetlana; Phuong, Jimmy; Dorr, David A; Zampino, Elizabeth; Wilcox, Adam B.; Carson, Matthew B.; Holmes, Kristi L.

doi:10.1371/journal.pcbi.1011136

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…They are “based on the premise that people, organizations, functional areas, processes, etc., evolve through a process of development and growth towards a more advanced maturity accomplishing several stages [ 12 ].” Examples of clinical informatics maturity models include The HIMSS Electronic Medical Record Adoption Model [ 13 ], the Continuity of Care Maturity Model [ 14 ], and the Quintegra maturity model for electronic healthcare (eHMM) [ 15 ]. There has also been active development of research informatics maturity models, focusing on domains like research data warehouses and research data sharing [ 16 – 18 ]. Models exist covering a variety of domains, including the PACS (picture archiving and communication systems) [ 19 ] and Healthcare Analytics Adoption Model for data analysis [ 20 ], but no models focus on SEDoH information that will facilitate organizational advancements.…”

Section: Introductionmentioning

confidence: 99%

Development of a social and environmental determinants of health informatics maturity model

Espinoza,

Sehgal,

Phuong

et al. 2023

J. Clin. Trans. Sci.

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Introduction: Integrating social and environmental determinants of health (SEDoH) into enterprise-wide clinical workflows and decision-making is one of the most important and challenging aspects of improving health equity. We engaged domain experts to develop a SEDoH informatics maturity model (SIMM) to help guide organizations to address technical, operational, and policy gaps. Methods: We established a core expert group consisting of developers, informaticists, and subject matter experts to identify different SIMM domains and define maturity levels. The candidate model (v0.9) was evaluated by 15 informaticists at a Center for Data to Health community meeting. After incorporating feedback, a second evaluation round for v1.0 collected feedback and self-assessments from 35 respondents from the National COVID Cohort Collaborative, the Center for Leading Innovation and Collaboration’s Informatics Enterprise Committee, and a publicly available online self-assessment tool. Results: We developed a SIMM comprising seven maturity levels across five domains: data collection policies, data collection methods and technologies, technology platforms for analysis and visualization, analytics capacity, and operational and strategic impact. The evaluation demonstrated relatively high maturity in analytics and technological capacity, but more moderate maturity in operational and strategic impact among academic medical centers. Changes made to the tool in between rounds improved its ability to discriminate between intermediate maturity levels. Conclusion: The SIMM can help organizations identify current gaps and next steps in improving SEDoH informatics. Improving the collection and use of SEDoH data is one important component of addressing health inequities.

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Section: Introductionmentioning

confidence: 99%

Development of a social and environmental determinants of health informatics maturity model

Espinoza,

Sehgal,

Phuong

et al. 2023

J. Clin. Trans. Sci.

Self Cite

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Distributed, immutable, and transparent biomedical limited data set request management on multi-capacity network

Yu,

Edelson,

Pham

et al. 2024

Journal of the American Medical Informatics Association

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Objective Our study aimed to expedite data sharing requests of Limited Data Sets (LDS) through the development of a streamlined platform that allows distributed, immutable management of network activities, provides transparent and intuitive auditing of data access history, and systematically evaluated it on a multi-capacity network setting for meaningful efficiency metrics. Materials and Methods We developed a blockchain-based system with six types of smart contracts to automate the LDS sharing process among major stakeholders. Our workflow included metadata initialization, access-request processing, and audit-log querying. We evaluated our system using synthetic data on three machines with varying specifications to emulate real-world scenarios. The data employed included ∼1000 researcher requests and ∼360 000 log queries. Results On average, it took ∼2.5 s to register and respond to a researcher access request. The average runtime for an audit-log query with non-empty output was ∼3 ms. The runtime metrics at each institution showed general trends affiliated with their computational capacity. Discussion Our system can reduce the LDS sharing request time from potentially hours to seconds, while enhancing data access transparency in a multi-institutional setting. There were variations in performance across sites that could be attributed to differences in hardware specifications. The performance gains became marginal beyond certain hardware thresholds, pointing to the influence of external factors such as network speeds. Conclusion Our blockchain-based system can potentially accelerate clinical research by strengthening the data access process, expediting access and delivery of data links, increasing transparency with clear audit trails, and reinforcing trust in medical data management. Our smart contracts are available at: https://github.com/graceyufei/LDS-Request-Management.

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Cultura e anomia no compartilhamento de dados: uma perspectiva a partir dos pesquisadores

Monteiro,

Sant’Ana

2024

Transinformação

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Resumo A retenção ou publicação de dados pode diversificar conforme a cultura, os valores predominantes em determinada área do conhecimento ou as práticas adotadas por pesquisadores individuais. A anomia pode ocorrer quando há um desequilíbrio entre as metas institucionais e os valores culturais, com maior relevância dos valores culturais coletivos ou individuais. Dessa forma, pesquisadores podem não concordar com as metas institucionais e praticar condutas desviantes. O objetivo deste artigo foi apontar indícios da influência cultural sobre o estado de anomia em pesquisadores no processo de gestão e compartilhamento dos dados de sua pesquisa. Foi utilizada a pesquisa documental e exploratória e o método de análise de conteúdo com aplicação de framework que considera as dimensões institucional e individual. Os resultados apontaram indícios de que os pesquisadores têm pouca clareza sobre as metas institucionais para a gestão e o compartilhamento de dados. Conclui-se que a possível anomia no contexto dos pesquisadores está associada com desconhecimento ou dubiedade sobre as normas institucionais, sociais e morais, desequilíbrio entre as metas e os caminhos para atingi-las, descumprimento das orientações institucionais e pouca habilidade para a gestão de dados.

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Ten simple rules for organizations to support research data sharing

Cited by 3 publications

References 13 publications

Development of a social and environmental determinants of health informatics maturity model

Development of a social and environmental determinants of health informatics maturity model

Distributed, immutable, and transparent biomedical limited data set request management on multi-capacity network

Cultura e anomia no compartilhamento de dados: uma perspectiva a partir dos pesquisadores

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