Development of a Query processing optimization model in electronic health records systems | ||||
| International Journal of Library and Information Sciences | ||||
| Article 98, Volume 10, Issue 1, January 2023, Page 170-208 PDF (2.02 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijlis.2022.104030.1120 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Aalaa جعفر Alsadek   ; Rania أحمد Mohell
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| library and information science department, Faculty of art, Alexandria university, Alexandria, Egypt | ||||
| Abstract | ||||
| As a primary data source, electronic health records provide great opportunities for healthcare service improvement. The success of EHR systems is measured by the accuracy and comprehensiveness of its outputs, as well as its role and support in decision making. Therefore, the study aims at evaluating the effectiveness of e-Dylphyn system, a blood bank management system used in Egypt, and its capability to manage the electronic health records of the unit’s patients. The study was conducted in the regional center of blood transfer service in Alexandria. The queries and reports on patients’ data as well as statistics for decision making support were observed on system. The case study methodology was followed for evaluating the performance of the system and health informatics service in the center. Furthermore, the case study results were employed for optimizing queries in the system. The study has concluded that system does not support EHR in an advanced way. It was also concluded that it does not provide integration for different secondary blood banks with the regional center. Therefore, the system cannot be reliable for data correlation or gathering detailed data on the center activity in delivering health care service. The most significant study result was the system’s query ability to generate a detailed report on inpatients volume and their categorization. In conclusion, it is recommended to build a framework that depends on swift big data analysis to make good use of the enormous data stored in secondary blood banks. | ||||
| Keywords | ||||
| Electronic Health Record; Query processing; Query optimization; e-Delphyn; Blood Bank | ||||
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