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

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.