OBIK – Ontology-based Benchmarking
|The goal of the OBIK project (Ontology-Based Benchmarking Infrastructure for Hospitals), is to objectively compare the performance of hospitals across the country, thereby identifying potential for optimization and developing suggestions for improvement.|
Modern medicine is not only becoming more complex and diverse, but also more expensive. Austria must pay a high price for its high-quality health system and the calls for a comprehensive health care reform are growing louder. A large share of the costs is due to complications during treatments and their consequences. To boost quality in this sense, an extensive benchmarking of hospitals is necessary.
The goal of the OBIK project (Ontology-Based Benchmarking Infrastructure for Hospitals), which is directed by Department for Process Management in Health Care (PMG) of the University of Applied Sciences in Steyr, is to objectively compare the performance of hospitals across the country, thereby identifying potential for optimization and developing suggestions for improvement. In order to ensure the objectivity and comparability of results, not only shall the services be compared, but the individual situation of the patient should also be taken into account. This is known as a „risk adjustment.“
As with all large-scale studies, the structured collection and safe storage of medical data plays an important role. Since the benchmarking process itself is subject to adjustments and improvements, a sound but flexible basis for the collected data is necessary.
With these requirements, the department, represented by Dr. Klaus Arthofer, approached the Research Unit Medical Informatics to make use of their expertise in the field of medical data collection. Another technical challenge is the fact that the benchmarking data enters the system electronically from various hospital information systems, mainly through web forms entered by specially trained data entry clerks. All this data is merged into a database and then subjected to further validation to either correct erroneous data or to exclude it from the analysis. The rules for such validations are also captured in a flexible adaptive data structure. Thus, the high demands on the flexibility of data structures, as well as rules and regulations, require the use of specific meta-data models.
Afterwards, the merged, checked and adjusted data is analyzed by the experts of the Process Management in Health Care department and forms the basis for precise improvements and reforms that now not only rely on economic-oriented criteria, but on performance-oriented criteria as well.
The successful completion of this project is built upon on the very good cooperation with the project management team, the PMG study program at the University of Applied Sciences in Steyr and the project partner FAW GmbH Hagenberg. We would like to express our sincere gratitude at this point for their teamwork.