Data quality in hospital information systems: Lessons learned from analyzing 30 years of patient data in a regional German hospital

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 system...

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Bibliographic Details
Published in:	International journal of medical informatics (Shannon, Ireland) Vol. 192; p. 105636
Main Authors:	Förstel, Stefan, Förstel, Markus, Gallistl, Markus, Zanca, Dario, Eskofier, Bjoern M., Rothgang, Eva M.
Format:	Journal Article
Language:	English
Published:	Ireland Elsevier B.V 01-12-2024
Subjects:	Data cleansing Data quality Digital transformation in healthcare Healthcare informatics Hospital information systems Data quality Hospital information systems Healthcare informatics Data cleansing Digital transformation in healthcare
Online Access:	Get full text
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1386-5056 1872-8243 1872-8243
DOI:	10.1016/j.ijmedinf.2024.105636