EN
Toward robust information: data quality in healthcare systems
Abstract
In healthcare, data moves with the patients they reference, creating interdependencies between healthcare organizations. This means that poor data management in one organization can negatively and cascading affect other organizations and the quality of care a patient receives. The large number of different data sources in healthcare leads to significant complexity in their management. As a result, there is currently a reactive approach to data quality management, which contributes to a lack of trust in data, as users only become aware of data quality issues when they first try to use the data. This paper examines the issues that define and control data quality and the mechanisms that can be developed to achieve and maintain good data quality in the light of the literature.
Keywords
References
- Provost, LP, Murray, SK, (2022). The health care data guide: learning from data for improvement. John Wiley & Sons. Howie, L, Hirsch, B, Locklear, T, Abernethy, A, (2014). Assessing the value of patient-generated data to comparative effectiveness research. Health affairs 33 7, 1220-8. https://doi.org/10.1377/hlthaff.2014.0225.
- D'Amore, J, McCrary, L, Denson, J, Li, Vitale, C, Tokachichu, P, Sittig, D, McCoy, A, Wright, A, (2021). Clinical data sharing improves quality measurement and patient safety. Journal of the American Medical Informatics Association: JAMIA. 28, 1534-1542. https://doi.org/10.1093/jamia/ocab039.
- Strasberg, H, Rhodes, B, Fiol, G, Jenders, R, Haug, P, Kawamoto, K, (2021). Contemporary clinical decision support standards using health level seven international fast healthcare interoperability resources. Journal of the American Medical Informatics Association. 28.8: 1796-1806.
- Lu, C, (2014). Uncertainties in real‐world decisions on medical technologies. International Journal of Clinical Practice. 68. https://doi.org/10.1111/ijcp.12434.
- Rodriguez-Lainz, A, McDonald, M, Fonseca-Ford, M, Penman-Aguilar, A, Waterman, S, Truman, B, Cetron, M., & Richards, C, (2018). Collection of Data on Race, Ethnicity, Language, and Nativity by US Public Health Surveillance and Monitoring Systems: Gaps and Opportunities. Public Health Reports. 133, 45 - 54. https://doi.org/10.1177/0033354917745503.
- Bali, A, & Ramesh, M, (2017). Designing effective healthcare: Matching policy tools to problems in China. Public Administration and Development. 37.1: 40-50.
- Mavrogiorgou, A, Kiourtis, ., Perakis, K, Miltiadou, D, Pitsios, S, Kyriazis, D, (2019). Analyzing data and data sources towards a unified approach for ensuring end-to-end data and data sources quality in healthcare 4.0. Computer methods and programs in biomedicine, 181, 104967.
- De Lusignan, S, Stephens, PN, Adal, N, Majeed, A, (2002). Does feedback improve the quality of computerized medical records in primary care?. Journal of the American Medical Informatics Association, 9(4), 395-401. Were, V, Moturi, C, (2017). Toward a data governance model for the Kenya health professional regulatory authorities. The TQM Journal, 29(4), 579-589.
Details
Primary Language
English
Subjects
Data Quality
Journal Section
Research Article
Authors
Publication Date
May 30, 2024
Submission Date
May 1, 2024
Acceptance Date
May 21, 2024
Published in Issue
Year 2024 Volume: 1 Number: 1