gms | German Medical Science

1. Introduction: In nursing research, the availability of comprehensive data is crucial. Hospital information systems (HIS) contain valuable data required for nursing research. However, the key question is how to effectively extract this data for research purposes. This paper outlines the steps taken to extract relevant HIS data, sharing experiences and insights. The required nursing staff calculation according to the German nursing staff assessment tool Pflegepersonalregelung (PPR 2.0) mandated by the Pflegepersonalbemessungsverordnung (PPBV) [1] serves as an exemplary case for detailed HIS data use.

2. State of the Art: Nursing care assessment approaches require specific data from HIS, making data extraction crucial. Current approaches, including Data Warehousing and ETL processes, are complex and resource-intensive, posing barriers to implementation. APIs like Fast Healthcare Interoperability Resources (FHIR) are not yet fully configured in many hospitals, necessitating alternative approaches.

Additionally, there are no publicly available datasets specifically for nursing research. For example, the MIMIC database [2]? offers comprehensive clinical data but lacks specific data needed for nursing research. This lack of dedicated datasets necessitates developing specialized data extraction methods.

3. Concept: The presented approach is implemented in a paediatric clinic using ORBIS HIS [3], focusing on HIS data extraction for nursing staff calculation based on PPR 2.0. First, all necessary data according to PPR 2.0 were identified. Based on PPBV requirements, a model was developed to describe the process of calculating nursing staff requirements. Further necessary data elements within the HIS database were identified. The built-in report generator in the Orbis HIS was used to extract the data, allowing the creation of customized reports and generating SQL queries.

4. Implementation: The data extraction process resulted in a comprehensive dataset suitable for PPR 2.0 calculations. The dataset includes 1,264,474 records from February 2017 until May 2024. It contains patient data (case ID, birth date, admission date, discharge date, admission type), nursing care data (executed nursing measures, execution day, execution time), and department data (ward).

5. Lessons Learned: Accessing the HIS database posed an initial challenge as direct access to the data tables was not granted by the HIS provider. Thus, common data extraction methods were not applicable. The in-built report generator was identified as a suitable tool to perform the data extraction via SQL queries. Understanding the data structure of the HIS tables posed another significant challenge, requiring extensive database exploration to identify tables and fields necessary for the PPR 2.0 calculations.

Our approach enables data extraction using an in-built tool, bypassing the need for direct access to the HIS database and the implementation of more complex methods. However, new reports must be defined, and exports conducted each time new tables or data are required.

Utilizing data for nursing research is essential. Therefore, methods to make data accessible are necessary. This study highlights the feasibility of using integrated tools for data extraction from HIS for nursing research, as demonstrated by the PPR 2.0 calculations. This method can also be adapted to extract other datasets for various nursing research purposes.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.