Implementing a Cloud Based Method for Protected Clinical Trial Data Sharing

Luthria, Gaurav; Qing-bo, Wang

doi:10.1142/9789811215636_0057

Cited by 4 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2015, following the publication of the IOM consensus study, Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk, Tudur Smith et al [20] published a set of good practice principles for data sharing, which emphasized controlled and secure access, participant consent and confidentiality, and a multistakeholder approach for supporting the required resources [1]. Additional publications since this time have included the development of data sharing principles for specific diseases such as the Alzheimer disease, critiques of data access review policies, and proposed strategies for implementing protected cloud-based methods of clinical trial data sharing [21][22][23]. In a more comprehensive critique on data sharing and the reuse of individual participant-level data from clinical trials, Ohmann et al [24] published a number of principles and recommendations that resulted from a multistakeholder consensus process.…”

Section: The Current State Of Clinical Trial Data Sharingmentioning

confidence: 99%

Data Sharing Goals for Nonprofit Funders of Clinical Trials

et al. 2021

View full text Add to dashboard Cite

Sharing clinical trial data can provide value to research participants and communities by accelerating the development of new knowledge and therapies as investigators merge data sets to conduct new analyses, reproduce published findings to raise standards for original research, and learn from the work of others to generate new research questions. Nonprofit funders, including disease advocacy and patient-focused organizations, play a pivotal role in the promotion and implementation of data sharing policies. Funders are uniquely positioned to promote and support a culture of data sharing by serving as trusted liaisons between potential research participants and investigators who wish to access these participants’ networks for clinical trial recruitment. In short, nonprofit funders can drive policies and influence research culture. The purpose of this paper is to detail a set of aspirational goals and forward thinking, collaborative data sharing solutions for nonprofit funders to fold into existing funding policies. The goals of this paper convey the complexity of the opportunities and challenges facing nonprofit funders and the appropriate prioritization of data sharing within their organizations and may serve as a starting point for a data sharing toolkit for nonprofit funders of clinical trials to provide the clarity of mission and mechanisms to enforce the data sharing practices their communities already expect are happening.

show abstract

Section: The Current State Of Clinical Trial Data Sharingmentioning

confidence: 99%

Data Sharing Goals for Nonprofit Funders of Clinical Trials

et al. 2021

View full text Add to dashboard Cite

show abstract

“… 17 Although the culture is changing, sharing data directly between at times competing industry partners and academic partners is hardly routine and still has numerous challenges with data privacy and a lack of resources. 8 , 11 , 17 , 21 , 23 , 26 , 29 , 31 …”

Section: Discussionmentioning

confidence: 99%

“… 25 Several solutions have been proposed such as new web-based networking technologies, database management systems, and review committees for deidentification. 6 , 16 , 21 , 26 , 27 …”

Section: Introductionmentioning

confidence: 99%

Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart

Schröder,

Muller,

Vradi

et al. 2023

Big Data

View full text Add to dashboard Cite

Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart, case study 1, needed to pool data from seven heterogeneous data sets: five randomized controlled trials from three different industry partners, and two disease registries. Sharing IPD was not considered feasible due to legal requirements and the sensitive medical nature of these data. In addition, harmonizing the data sets for a federated data analysis was difficult due to capacity constraints and the heterogeneity of the data sets. An alternative option was to share summary statistics through contingency tables. Here it is demonstrated that this method along with anonymization methods to ensure patient anonymity had minimal loss of information. Although sharing IPD should continue to be encouraged and strived for, our approach achieved a good balance between data transparency while protecting patient privacy. It also allowed a successful collaboration between industry and academia.

show abstract

“…There are numerous benefits to sharing clinical data, including reducing duplicate trials, increasing transparency and validity (Bath et al, 2010;Doiron et al, 2013;Wilkinson et al, 2019), higher generalisability of results (Noale et al, 2005) and more efficient usage of secondary data (Doiron et al, 2013;Lo, 2015;Ohmann et al, 2017;Rosenblatt et al, 2015). However, disadvantages of sharing data are the difficulties in ensuring data privacy, poor compensation to the data generators, and the risk of data mishandling and misinterpretation Kochhar et al, 2019;Luthria and Wang, 2019;Ohmann et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…In the EU, sharing such sensitive personal data is prohibited by the General Data Protection Regulation (GDPR) (EU 2016/679), except from where exemptions laid down in national law allow for the processing for scientific research purposes . Several solutions have been proposed such as new web-based networking technologies, database management systems, and review committees for de-identification (Doiron et al, 2013;Ford et al, 2009;Luthria and Wang, 2019;Muilu et al, 2007;Wolfson et al, 2010). Novel techniques using deep learning and blockchain techniques are being proposed for privacy-preservation of centralised servers, however the current conventional central medicinal information storing systems are still considered inadequate (Alzubi et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Responsible governance for data-intensive health research networks

Muller

View full text Add to dashboard Cite

Big Data hold great promise for scientific health research and healthcare. Yet data-intensive health research in which various types of data from different sources are collected, (re)used, shared and/or linked on a large scale also creates distinct problems. Data-intensive health research takes place in complex networks. In these networks, (semi-)public organisations like hospitals and private organisations like pharmaceutical companies collaborate, depending upon each other and sharing goals, interests, and motivations. Moreover, ethical and legal requirements and social demands need to be met to accomplish responsible data-intensive health research. This requires adequate coordination, that is, governance. By engaging in interdisciplinary, theoretical and empirical social sciences-bioethics research, this PhD thesis develops a learning approach for responsible governance of data-intensive health research networks. This research addresses three important challenges: (1) ethical and legal fragmentation, (2) connecting with stakeholders and society, and (3) untameable heterogeneity and complexity. First, the thesis analyses the role of accountability and impediments to health data sharing collaborations. Second, it considers the concept of social licence and the potential of dynamic interfacing for support and trust in data-intensive health research. Third, patient and public preferences for governance are studied and principled operational guidance is provided. This PhD thesis stipulates learning governance for responsible data-intensive health research, which employs situated social and structural circumstances. Learning governance relies on performance feedback and dialogue about experiences with implementation of provisional norms, goals and governance arrangements. Such governance is characterised by self-regulation, social sanctioning and self-organisation. This requires experimentalist design, strengthening of collective control and participatory governance arrangements.

show abstract

Implementing a Cloud Based Method for Protected Clinical Trial Data Sharing

Cited by 4 publications

References 19 publications

Data Sharing Goals for Nonprofit Funders of Clinical Trials

Data Sharing Goals for Nonprofit Funders of Clinical Trials

Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart

Responsible governance for data-intensive health research networks

Contact Info

Product

Resources

About