How to Design a Governable Digital Health Ecosystem

Morley, Jessica; Floridi, Luciano

doi:10.1007/978-3-030-80083-3_8

Cited by 4 publications

(6 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Once the technical and organizational barriers have been overcome, with data managed, reused, stored, extracted and distributed properly [53], health must also pay attention to behavior focused on interactions between human, artificial and hybrid actors. This reflects the importance of adhering to social, ethical and professional norms in the inclusion of demands related to justice, responsibility and transparency [54].…”

Section: Discussionmentioning

confidence: 99%

“…This reflects the importance of adhering to social, ethical and professional norms in the inclusion of demands related to justice, responsibility and transparency [54]. In short, increasing dependence on "quality information" increases its possibilities [53], but it also presents regulators and policymakers with considerable challenges related to their governance in health.…”

Section: Discussionmentioning

confidence: 99%

“…Clearly, there is a need to take a proactive 'digital ethics' approach to these challenges, so that the transition from an assessment of what is morally good to what is politically feasible and legally feasible takes place before ethical mistakes lead to social rejection [53], and leave institutions and researchers unable to benefit from the so-called 'double advantage', where opportunities are capitalized and risks mitigated [50] of an ethical approach to governance [53].…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Data Quality in health research: a systematic literature review

Bernardi

Alves

Crepaldi³

et al. 2022

Preprint

View full text Add to dashboard Cite

Decision-making and strategies to improve service delivery need to be supported by reliable health data to generate consistent evidence on health status, so the data quality management process must ensure the reliability of the data collected. Thus, through an integrative literature review, the main objective of this work is to identify and evaluate digital health technology interventions designed to support the conduct of health research based on data quality. After analyzing and extracting the results of interest, 33 articles were included in the review. This transdisciplinarity may be reaching the threshold of significant growth and thus forcing the need for a metamorphosis of the area from focusing on the measurement and evaluation of data quality, today focused on content, to a direction focused on use and context In general, the main barriers reported in relation to the theme of research in the area of health data quality cite circumstances regarding a) use, b) systems and c) health services. . The resources presented can help guide medical decisions that do not only involve medical professionals, and indirectly contribute to avoiding decisions based on low-quality information that can put patients' lives at risk.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Data Quality in health research: a systematic literature review

Bernardi

Alves

Crepaldi³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Once the technical and organizational barriers have been overcome, with data managed, reused, stored, extracted, and appropriately distributed [46], health care must also pay attention to behavior focused on interactions between human, artificial, and hybrid actors. This interaction reflects the importance of adhering to social, ethical, and professional norms, including demands related to justice, responsibility, and transparency [60].…”

Section: Future Directionsmentioning

confidence: 99%

“…As a large part of this quality depends on the collection stage, interventions must target the local level where it occurs and must encompass professionals at the operational level and forms at the technical level. Identifying and addressing behavioral and organizational challenges and building technical capacity are critical [60], increasingly fostering a data-driven culture [29,30].…”

Section: Strengthsmentioning

confidence: 99%

Data Quality in Health Research: Integrative Literature Review

Bernardi¹,

Alves²,

Crepaldi³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

Background Decision-making and strategies to improve service delivery must be supported by reliable health data to generate consistent evidence on health status. The data quality management process must ensure the reliability of collected data. Consequently, various methodologies to improve the quality of services are applied in the health field. At the same time, scientific research is constantly evolving to improve data quality through better reproducibility and empowerment of researchers and offers patient groups tools for secured data sharing and privacy compliance. Objective Through an integrative literature review, the aim of this work was to identify and evaluate digital health technology interventions designed to support the conducting of health research based on data quality. Methods A search was conducted in 6 electronic scientific databases in January 2022: PubMed, SCOPUS, Web of Science, Institute of Electrical and Electronics Engineers Digital Library, Cumulative Index of Nursing and Allied Health Literature, and Latin American and Caribbean Health Sciences Literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and flowchart were used to visualize the search strategy results in the databases. Results After analyzing and extracting the outcomes of interest, 33 papers were included in the review. The studies covered the period of 2017-2021 and were conducted in 22 countries. Key findings revealed variability and a lack of consensus in assessing data quality domains and metrics. Data quality factors included the research environment, application time, and development steps. Strategies for improving data quality involved using business intelligence models, statistical analyses, data mining techniques, and qualitative approaches. Conclusions The main barriers to health data quality are technical, motivational, economical, political, legal, ethical, organizational, human resources, and methodological. The data quality process and techniques, from precollection to gathering, postcollection, and analysis, are critical for the final result of a study or the quality of processes and decision-making in a health care organization. The findings highlight the need for standardized practices and collaborative efforts to enhance data quality in health research. Finally, context guides decisions regarding data quality strategies and techniques. International Registered Report Identifier (IRRID) RR2-10.1101/2022.05.31.22275804

show abstract

Digital health policy decoded: Mapping national strategies using Donabedian's model

Saheb,

Saheb

2024

Health Policy

View full text Add to dashboard Cite

How to Design a Governable Digital Health Ecosystem

Cited by 4 publications

References 68 publications

Data Quality in health research: a systematic literature review

Data Quality in health research: a systematic literature review

Data Quality in Health Research: Integrative Literature Review

Digital health policy decoded: Mapping national strategies using Donabedian's model

Contact Info

Product

Resources

About