The development of a business intelligence system for analyzing online appointment behaviors: A case study at the first affiliated hospital of Zhengzhou University

Background Healthcare institutions face evolving management needs, with outpatient data analysis being increasingly critical. While business intelligence (BI) applications in healthcare have grown substantially, 1 , 2 existing research on appointment systems predominantly focuses on patient choice modeling and queue optimization, with limited attention to the online appointment decision-making process itself. This study addresses this gap by developing a specialized BI system for analyzing detailed online appointment behaviors. Methods We designed an Online Appointment Business Intelligence (OABI) system employing Google Analytics for tracking user interactions with minimal intrusiveness. The system was implemented over an 11-month period at The First Affiliated Hospital of Zhengzhou University. OABI features a layered architecture comprising Operational Data Store (ODS), Data Warehouse Detail (DWD), Data Warehouse Summary (DWS), and Application Data Service (ADS) layers. Ethical approval was obtained from the Institutional Review Board of The First Affiliated Hospital of Zhengzhou University (Approval No: 2022-KY-1338-002). Results OABI successfully tracked over 50,000 daily appointment-related events with data collection completeness exceeding 98%. The system identified critical behavioral patterns including a significant positive correlation between doctor detail page views and appointment conversion rates ( r = 0.78, P < .01). Implementation resulted in a 43% faster report generation speed compared to the previous SQL Server Reporting Services (SSRS)-based system, along with a 17% improvement in overall appointment conversion rate over six months of operation. User satisfaction surveys from eight departments yielded an average score of 4.3 out of 5. Conclusions The layered OABI architecture demonstrates significant advantages for capturing and analyzing detailed online appointment behaviors without disrupting existing systems. By providing insights into patient decision-making processes, this approach supports data-driven improvements to healthcare delivery. Future work should expand the model's application across multiple healthcare institutions while addressing scalability challenges.