The Challenges of Implementing Comprehensive Clinical Data Warehouses in Hospitals

Campone, Mario; Cuggia, Marc

doi:10.3390/ijerph19127379

Cited by 6 publications

(6 citation statements)

References 24 publications

(23 reference statements)

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“…Big Data health, digital health, connected health—this multiplicity of terms illustrates the scientific and technical revolution at work made possible by the use of digital technology, high-speed processing of health data, Artificial Intelligence and the profound upheavals currently taking place and yet to come in health systems. Far from being limited to facilitating the transmission of data and constituting a support to medico-technical or medico-administrative management, the concept of Big Data opens up unprecedented prospects for monitoring the state of health of populations, aiding decision making in medicine and characterizing risks [ 5 , 16 ].…”

Section: Big Data: Digital Technology For Cancer Care and Researchmentioning

confidence: 99%

“…Even if CCCs have established hallmarks, a greater emphasis is needed to create more effective data warehouses (DWHs) in CCCs to support the organization and the elaboration of processes essential for producing quality outcomes for patients and effectiveness in the translational process. CCC DWHs are crucial tools for improving overall understanding of cancer diseases and patient prevention, care and research in oncology worldwide [ 3 , 4 , 5 ]. Indeed, state-of-the-art DWHs that meet the highest international standards in terms of quality, interoperability and openness to outpatient settings are essential to CCCs to strengthen the relevance of the network they constitute and to deliver integrated research, outstanding innovation and excellence in patient outcomes [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…and its quality (Do the data meet quality criteria enabling us to say that they are interpretable and complete?). It is impossible to be satisfied with erroneous or fragmentary clinical data or poor quality medical imaging, which only leads to false modeling and less than robust results from Artificial Intelligence algorithms [ 5 ]. In order to meet this dual requirement of data structuring and quality, more and more CCCs have decided to develop their own DWHs [ 3 , 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…It is impossible to be satisfied with erroneous or fragmentary clinical data or poor quality medical imaging, which only leads to false modeling and less than robust results from Artificial Intelligence algorithms [ 5 ]. In order to meet this dual requirement of data structuring and quality, more and more CCCs have decided to develop their own DWHs [ 3 , 5 , 6 , 7 , 8 ]. The interest of these DWHs is, in particular, that they allow RWE studies to be carried out, i.e., studies conducted on the basis of data collected in routine care practice outside the traditional framework of clinical trials.…”

Section: Introductionmentioning

confidence: 99%

“…Interaction between these DWHs and preexisting population-based databases and registries are essential developments for the coming years for CCCs, particularly for tracking the long-term medical outcomes of treatments [ 4 ]. These DWHs in CCCs can be fruitful platforms in oncology for research around survivorship, which considers cancer as a long-term condition, but also for the validation of quality-of-life indicators and health economics [ 3 , 5 ]. While the large-scale use of health data is a source of progress and medical advances, it legitimately raises questions of a legal and ethical nature.…”

Section: Introductionmentioning

confidence: 99%

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Opportunities and Obstacles to the Development of Health Data Warehouses in Hospitals in France: The Recent Experience of Comprehensive Cancer Centers

Raimbourg

Bigot

Simmet

et al. 2023

IJERPH

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Big Data and Artificial Intelligence can profoundly transform medical practices, particularly in oncology. Comprehensive Cancer Centers have a major role to play in this revolution. With the purpose of advancing our knowledge and accelerating cancer research, it is urgent to make this pool of data usable through the development of robust and effective data warehouses. Through the recent experience of Comprehensive Cancer Centers in France, this article shows that, while the use of hospital data warehouses can be a source of progress by taking into account multisource, multidomain and multiscale data for the benefit of knowledge and patients, it nevertheless raises technical, organizational and legal issues that still need to be addressed. The objectives of this article are threefold: 1. to provide insight on public health stakes of development in Comprehensive Cancer Centers to manage cancer patients comprehensively; 2. to set out a challenge of structuring the data from within them; 3. to outline the legal issues of implementation to carry out real-world evidence studies. To meet objective 1, this article firstly proposed a discussion on the relevance of an integrated approach to manage cancer and the formidable tool that data warehouses represent to achieve this. To address objective 2, we carried out a literature review to screen the articles published in PubMed and Google Scholar through the end of 2022 on the use of data warehouses in French Comprehensive Cancer Centers. Seven publications dealing specifically with the issue of data structuring were selected. To achieve objective 3, we presented and commented on the main aspects of French and European legislation and regulations in the field of health data, hospital data warehouses and real-world evidence.

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Section: Big Data: Digital Technology For Cancer Care and Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Opportunities and Obstacles to the Development of Health Data Warehouses in Hospitals in France: The Recent Experience of Comprehensive Cancer Centers

Raimbourg

Bigot

Simmet

et al. 2023

IJERPH

Self Cite

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A systematic review and meta-data analysis of clinical data repositories in Africa and beyond: recent development, challenges, and future directions

Adewole,

Alozie,

Olagunju

et al. 2024

Discov Data

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A Clinical Data Repository (CDR) is a dynamic database capable of real-time updates with patients' data, organized to facilitate rapid and easy retrieval. CDRs offer numerous benefits, ranging from preserving patients' medical records for follow-up care and prescriptions to enabling the development of intelligent models that can predict, and potentially mitigate serious health conditions. While several research works have attempted to provide state-of-the-art reviews on CDR design and implementation, reviews from 2013 to 2023 cover CDR regulations, guidelines, standards, and challenges in CDR implementation without providing a holistic overview of CDRs. Additionally, these reviews need to adequately address critical aspects of CDR; development and utilization, CDR architecture and metadata, CDR management tools, CDR security, use cases, and artificial intelligence (AI) in CDR design and implementation. The collective knowledge gaps in these works underscore the imperative for a comprehensive overview of the diverse spectrum of CDR as presented in the current study. Existing reviews conducted over the past decade, from 2013 to 2023 have yet to comprehensively cover the critical aspects of CDR development, which are essential for uncovering trends and potential future research directions in Africa and beyond. These aspects include architecture and metadata, security and privacy concerns, tools employed, and more. To bridge this gap, in particular, this study conducts a comprehensive systematic review of CDR, considering critical facets such as architecture and metadata, security and privacy issues, regulations guiding development, practical use cases, tools employed, the role of AI and machine learning (ML) in CDR development, existing CDRs, and challenges faced during CDR development and deployment in Africa and beyond. Specifically, the study extracts valuable discussions and analyses of the different aspects of CDR. Key findings revealed that most architectural models for CDR are still in the theoretical phase, with low awareness and adoption of CDR in healthcare environments, susceptibility to several security threats, and the need to integrate federated learning in CDR systems. Overall, this paper would serve as a valuable reference for designing and implementing cutting-edge clinical data repositories in Africa and beyond.

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Leveraging Big Data Intelligence for Electronic Health Data Collection and Mining in Smart Innovative Cities: Approaches and Applications

Zanjani,

Shahinzadeh

et al. 2023

2023 7th Iranian Conference on Advances in Enterprise Architecture (ICAEA)

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The Challenges of Implementing Comprehensive Clinical Data Warehouses in Hospitals

Cited by 6 publications

References 24 publications

Opportunities and Obstacles to the Development of Health Data Warehouses in Hospitals in France: The Recent Experience of Comprehensive Cancer Centers

Opportunities and Obstacles to the Development of Health Data Warehouses in Hospitals in France: The Recent Experience of Comprehensive Cancer Centers

A systematic review and meta-data analysis of clinical data repositories in Africa and beyond: recent development, challenges, and future directions

Leveraging Big Data Intelligence for Electronic Health Data Collection and Mining in Smart Innovative Cities: Approaches and Applications

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