Data quality is essential for utilizing real world data (RWD) in scientific context. Based on drug prescriptions in a hospital information system (HIS), algorithms performed a mapping of unstructured drug data to ATC codes. Visualization of the resulting distribution of structured to unstructured data based on ATC codes was created and used to explore a defined limitation of the current drug prescription highlighting the example of proton pump inhibitors. As a second step, a generalization of this approach was inductively created. As result we were able to identify 4 crucial steps for a feedback loop framework: The first step being the actual use of the HIS by clinician for drug prescription, second the processing of the entered unstructured and structured data and performing automatic analyses and visualization of the resulting distributions. The third step included an interdisciplinary expert evaluation of the data distribution followed by the fourth step, consisting of feedback to the stakeholders and generating actions as teaching or re-modelling of the system incorporating the actual learning process. The presented approach represents a continuously learning system based on RWD, although it is limited by analyzing the distribution of mapped unstructured text to ATC codes and therefore does not allow to analyze free text not mapped to ATC codes (false negatives). Future work will focus on the evaluation of this approach to analyze the impact on prescription data quality and the potential improvement on patient safety in general.