Data quality in hospital information systems: Lessons learned from analyzing 30 years of patient data in a regional German hospital
Förstel S, Förstel M, Gallistl M, Zanca D, Eskofier B, Rothgang EM (2024)
Publication Type: Journal article
Publication year: 2024
Journal
Book Volume: 192
Article Number: 105636
DOI: 10.1016/j.ijmedinf.2024.105636
Abstract
Background: The integration of Hospital Information Systems (HIS) into healthcare delivery has significantly enhanced patient care and operational efficiency. Nonetheless, the rapid acceleration of digital transformation has led to a substantial increase in the volume of data managed by these systems. This emphasizes the need for robust mechanisms for data management and quality assurance. Objective: This study addresses data quality issues related to patient identifiers within the Hospital Information System (HIS) of a regional German hospital, focusing on improving the accuracy and consistency of these administrative data entries. Methods: Employing a combination of data analysis and expert interviews, this study reviews and programmatically cleanses a dataset with over 2,000,000 patient data entries extracted from the HIS. The areas of investigation are patient admissions, discharges, and geographical data. Results: The analysis revealed that roughly 25% of the dataset was rendered unusable by errors and inconsistencies. By implementing a thorough data cleansing process, we significantly enhanced the utility of the dataset. In doing so, we identified the primary issues affecting data quality, including ambiguities among similar variables and a gap between the intended and actual use of the system. Conclusion: The findings highlight the critical importance of enhancing data quality in healthcare information systems. This study shows the necessity of a careful review of data extracted from the HIS before it can be reliably utilized for machine learning tasks, thereby rendering the data more usable for both clinical and analytical purposes.
Authors with CRIS profile
Dario Zanca
Machine Learning and Data Analytics Lab
Björn Eskofier
Machine Learning and Data Analytics Lab
Involved external institutions
Ostbayerische Technische Hochschule (OTH) Amberg-Weiden
Germany (DE)
Kliniken Nordoberpfalz
Germany (DE)
Germany (DE)
Kliniken Nordoberpfalz
Germany (DE)
How to cite
APA:
Förstel, S., Förstel, M., Gallistl, M., Zanca, D., Eskofier, B., & Rothgang, E.M. (2024). Data quality in hospital information systems: Lessons learned from analyzing 30 years of patient data in a regional German hospital. International Journal of Medical Informatics, 192. https://doi.org/10.1016/j.ijmedinf.2024.105636
MLA:
Förstel, Stefan, et al. "Data quality in hospital information systems: Lessons learned from analyzing 30 years of patient data in a regional German hospital." International Journal of Medical Informatics 192 (2024).
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