JMIR Publications

ABSTRACT

Background:

With the increase in the number of primary healthcare institutions, their patient volumes have relatively decreased, while tertiary hospitals have seen a significant rise in patient visits. The imbalance between the proportions of different types of medical institutions and patient visit frequencies in China leads to inefficient use of healthcare resources.

Objective:

Analyzing hospital outpatient scenarios and studying factors influencing patient clinic visits is crucial for optimizing hospital outpatient resource allocation.

Methods:

This study first analyzes the queuing behavior patterns of patients when making appointments at outpatient clinics, establishing a service value model for patient clinic visits. Based on this value model, we can estimate the average visit time for patients. Using the M/M/1 queuing model, we calculate the longest tolerable queue length for patients. Additionally, we construct a highly scalable data processing service system to ensure the integrity of patient visit data and use stream computing methods to real-time calculate indicators related to patient decision-making patterns.

Results:

We built a service value model for patient outpatient visits and monitored patient visits in real time based on historical patient visit information through a scalable stream computing system. The experimental results show that the response time of the system can be completed in a few seconds, which can meet the requirements of real time in practical applications.

Conclusions:

This study establishes a patient clinic visit decision-making model based on the service value patients receive and develops a highly available and scalable information distribution processing middleware to collect patient visit data and perform real-time calculations. The system provides indicators such as patient visit times and the longest tolerable queue length, which help optimize resource allocation and improve patient satisfaction.