A cost-effective and adaptable queue management system to increase efficiency in patient queue management

Abstract

Healthcare systems worldwide, particularly in resource-limited settings like Sri Lanka, face significant challenges related to high patient volumes and constrained resources. These challenges often lead to extended wait times and reduced patient satisfaction. This study presents an innovative, adaptable queue management system designed to replace inefficient manual methods, enhance operational efficiency, and optimise patient flow. Scalable to meet the needs of both small clinics and large hospitals, the system functions across various connectivity scenarios, ensuring flexibility in diverse environments. The system comprises patient, doctor, and administrative interfaces. Upon patient registration, a QR code will be generated, and the patient can use the QR code to check-in. A printed queue token will be issued when a patient checks-in. Doctors can manage their queues and access real-time patient information. Administrators oversee overall system operations, including advertisement management and key performance indicator (KPI) tracking, to monitor and enhance healthcare delivery in addition to having the ability to add, remove, or edit users. Built on a robust technology stack that includes HTML, CSS, JavaScript, PHP, SQLite3 for database management, and AES-256-CBC encryption for secure data handling, the system is designed for reliability and scalability. Embedded ESP32 devices with OLED displays and LEDs provide offline functionality, while multicast DNS (mDNS) ensures seamless device connectivity to local networks without requiring Internet access which is critical for rural healthcare facilities. The system features a custom-built algorithm, leveraging Random Forest Regression, to analyse historical and real-time queue data. This allows for precise queue time estimates and significantly improves staff and patient planning. The system outperforms the traditional manual systems, which lack both real-time prediction capabilities and efficiency. The system performance was meticulously improved using various optimisation techniques such as batch processing, database indexing, and algorithm optimisation, which led to an execution time of 22 seconds to be brought down to 1.5 seconds on a 1.4 million row data set, where the execution involved processing, sorting, encrypting, decrypting, and storing data. A one-tailed t-test was performed to compare the execution times of test runs with optimisation and without optimisation. There was a significant difference in execution times between test runs without optimization (M = 21.84, SD = 1.16) and execution times between test runs with optimization (M = 1.52, SD = 0.28); t(43) = 107.76, p < 0.001. The system was validated for 10 years of sample data and the results demonstrate that the system is robust and responsive under real-world conditions. Continuous validation is ongoing in diverse healthcare environments to further assess its impact on optimizing queue management, resource allocation, and patient satisfaction. This scalable and adaptable system represents a substantial advancement in healthcare management, offering a transformative solution to meet the evolving needs of healthcare facilities despite scarce infrastructure.