A Web-based Management System for Diagnostic Laboratory with Business Process Re-Engineering and Data Analytics
Management information systems are a challenge for many small businesses. With fast-changing technology, data management has become an integral part of business processes, especially in the healthcare industry. This study uses Business Process Re-Engineering (BPR) and Data Analytics for a Diagnostic Laboratory to improve its operational efficiency through a web-based management system. The obsolete system, consisting of eight modules, is replaced with a sophisticated system involving twenty-one modules. The goal is to evaluate the impact of technology on business operations and user satisfaction. The results indicate that the most recent technology enables board members to monitor corporate performance from any location using various web browsers. The database securely saves and categorizes all operational data. Transactions, test results, and reports are instantly created in PDF format. Subsequently, the gathered information is utilized by means of descriptive and prescriptive analysis. The system acceptance is also assessed using a questionnaire that adheres to the ISO 25010 standards. With an optimal score of 5.0, the results showed a weighted average of 4.79 (“Strongly Agree”), which is significantly higher than the prior system average of 3.69 (“Agree”). The conclusions emphasize that the system has a crucial role in remote management, efficient database management, improved operational efficiency, and reduced turnaround time. Recommendations encompass the integration of digital payment methods for transactions and advocating the established system as a benchmark for other diagnostic laboratories in the health industry. This research has a significant impact on the improvement of diagnostic laboratory management systems.
Authors
- Ryndel V. Amorado (ORCID: https://orcid.org/0000-0002-6379-2602)
- Jhun-Jhun P. Leynes
Institutions
- Batangas State University (PH)
Publication Details
- Published
- 2024-10-02
- DOI
- https://doi.org/10.1109/icset63729.2024.10775219
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00