Research Article

Toward robust information: data quality in healthcare systems

Volume: 1 Number: 1 May 30, 2024
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

  1. 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.
  2. 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.
  3. 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.
  4. Lu, C, (2014). Uncertainties in real‐world decisions on medical technologies. International Journal of Clinical Practice. 68. https://doi.org/10.1111/ijcp.12434.
  5. 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.
  6. Bali, A, & Ramesh, M, (2017). Designing effective healthcare: Matching policy tools to problems in China. Public Administration and Development. 37.1: 40-50.
  7. 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.
  8. 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

Publication Date

May 30, 2024

Submission Date

May 1, 2024

Acceptance Date

May 21, 2024

Published in Issue

Year 2024 Volume: 1 Number: 1

APA
Turhan, S. N. (2024). Toward robust information: data quality in healthcare systems. Transactions on Computer Science and Applications, 1(1), 24-30. https://izlik.org/JA54BD62UT