# Merging Ontologies and Data from Electronic Health Records
## 4 Description of Our Approach
### Data Acquisition
## 6 Conclusions

EHR is an exceptional tool that allows citizens and clinicians to keep up with technology and obtain faster and easier-to-access results, offering a significant improvement in the efficacy and management of patient's health care services. To make these services more efficient, this work presents an original approach to improve the usability of medical records, reducing the complexity of the data and adding new and useful information to facilitate the doctor's work. Extensive testing have been carried out on actual medical data provided by the HL7 organisation, which successfully validate our work. In currently common clinical settings, doctors often use EHRs with classic tools with which it is difficult to achieve a complete picture of a patients whole clinical record since each section is a separate CDA document based on potentially different keywords and dictionaries, strictly requiring (different) software tools to be managed or simply to be readable. In this context, this work proposes an improved scenario where our single tool is able to process all the EHR data and offer an easy-to-read, complete, and enriched medical record. Without our tool, several tools would have to be used independently of each other, leading to a considerable workload for the doctor as they are then required to have knowledge of and usage experience with all of them. In addition, they would need to spend a lot of time manually researching any correlations or associations with existing medical knowledge. They would be required to create special queries to manually search and read any available ontology data. Using our approach, our proposed single tool would allow for the effective presentation and navigation of the clinical information, already combined with any pertinent knowledge found in the matching ontologies, as a data aggregate which is immediately readable and clearly understandable for the doctors. Conceptualization, S.C., E.T. and G.V. ; methodology, S.C., E.T. and G.V. ; software, S.C. and G.V. ; validation, A.C., E.T. and G.V. ; writing--original draft preparation, A.C., E.T. and G.V. ; writing--review and editing, A.C. and E.T. ; supervision, A.C. and E.T. All authors have read and agreed to the published version of the manuscript. Figure 6: Example of a patient’s medical record with ID 444222222, showing data taken from three HL7 CDA files (centre) and HDO ontology (accessed on 10 November 2023). Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. The authors acknowledge the support of the University of Catania PIACERI 2020/22 Project \"TEAMS\" and project PO FESR Sicilia 2014-2020 Biospec3. Conflicts of Interest: [PERSON] has been involved as consultant and expert in the field. He is an employee of the company Cyberenta. The authors declare no conflicts of interest. ## References

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