Understanding value in health data ecosystems: A review of current evidence and ways forward

Marjanovic, Sonja; Ioana, Ghiga; Miaoqing, Yang; Knack, Anna

doi:10.7249/rr1972

Cited by 12 publications

(2 citation statements)

References 46 publications

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“…Informing workforce development programs will involve helping job seekers to be aware of why data in certain areas need to be collected, what the best data processes are, how to manage and share insights from healthcare data from current systems, as well as being prepared for future changes. Workforce development will require capacity building associated with data use and decision making, time demands of engagement and changes in the doctor-patient relationship [12]. These advancements require new considerations of ethics in health information and use of health technologies [6].…”

Section: Discussionmentioning

confidence: 99%

Health Informatics Workforce in the Digital Health Ecosystem

Meehan

2024

Studies in Health Technology and Informatics

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Workforce development needs to align with the healthcare data ecosystem emerging from digital transformation in healthcare. Careers for health informaticists are emerging as translational agents between clinicians and data scientists. Digital tools and mechanisms in healthcare, through electronic health records (EHR), devices, capabilities including artificial intelligence (AI), machine learning (ML), interoperability and health information exchange (HIE) allow clinicians and stakeholders to capture, store, access and use health data and information in ways unseen in years past, creating a new digital health ecosystem. This transformation is evolving both technologies and the strategies to influence health outcomes. Careers in health informatics are now part of this data ecosystem, and it is important to examine the current status and its implications for job seekers and for workforce development.

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Section: Discussionmentioning

confidence: 99%

Health Informatics Workforce in the Digital Health Ecosystem

Meehan

2024

Studies in Health Technology and Informatics

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“…and bias control [35]. Various initiatives aim to address the nexus of technical, legal, ethicsrelated, governance and data protection-related, and cultural challenges arising for health data ecosystems [36]. A first attempt to document and analyze all research, regulatory and ethical requirements stemming from the aggregation and analysis of clinical data from different health organizations was presented in [56], providing a solution architecture considering technical and organizational aspects.…”

Section: Related Workmentioning

confidence: 99%

Knowledge graphs for enhancing transparency in health data ecosystems1

Aisopos

Jozashoori

Emetis

et al. 2023

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Tailoring personalized treatments demands the analysis of a patient’s characteristics, which may be scattered over a wide variety of sources. These features include family history, life habits, comorbidities, and potential treatment side effects. Moreover, the analysis of the services visited the most by a patient before a new diagnosis, as well as the type of requested tests, may uncover patterns that contribute to earlier disease detection and treatment effectiveness. Built on knowledge-driven ecosystems, we devise DE4LungCancer, a health data ecosystem of data sources for lung cancer. In this data ecosystem, knowledge extracted from heterogeneous sources, e.g., clinical records, scientific publications, and pharmacological data, is integrated into knowledge graphs. Ontologies describe the meaning of the combined data, and mapping rules enable the declarative definition of the transformation and integration processes. DE4LungCancer is assessed regarding the methods followed for data quality assessment and curation. Lastly, the role of controlled vocabularies and ontologies in health data management is discussed, as well as their impact on transparent knowledge extraction and analytics. This paper presents the lessons learned in the DE4LungCancer development. It demonstrates the transparency level supported by the proposed knowledge-driven ecosystem, in the context of the lung cancer pilots of the EU H2020-funded project BigMedilytic, the ERA PerMed funded project P4-LUCAT, and the EU H2020 projects CLARIFY and iASiS.

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