(Research Article) Development of Rational Laboratory Use Model for Quality Service Improvement of Ta Phraya Hospital, Sa Kaeo Province

Abstract

Clinical laboratory testing is a fundamental process of health service systems. However, repeatability of test orders without proper clinical indications remains a significant challenge, affecting cost of treatments, personnel workload, and efficacy of patient care. Ta Phraya Hospital, Sa Kaeo Province, has encountered this challenge across multiple laboratory test items. Although the Rational Laboratory Use (RLU) policy has been established, a systematic quality improvement process model suited to the community hospital context remains unclear. This study aimed to develop a process model supporting quality improvement activities based on the RLU approach at Ta Phraya Hospital, Sa Kaeo Province, using action research methodology following the P-A-O-R cycle of Kemmis and McTaggart model. The study was conducted between June and October 2025. Twenty medical personnel were purposively selected to participated the study. Research tools included a questionnaire, in-depth interview guide, observation form, and satisfaction assessment form, all validated by experts. Quantitative and qualitative data were collected and further analyzed. The developed model consisted of seven steps: establishment of the RLU committee, policy announcement, development of test ordering guidelines, implementation of an information technology (IT)-based pop-up alert system, personnel training, monitoring and evaluation, and lesson learned. Key outcomes included a reduction in duplicate HbA1c test orders within 90 days from 28.9% to 19.7%, a decrease in orders lacking of clinical indications from 35.5% to 23.1%, a reduction in average costs per test from 145.8 to 112.4 THB, and a high level of staff satisfaction with the pop-up system (mean = 4.67 out of 5), with statistical significance (p < 0.05). The developed RLU model effectively reduced unnecessary laboratory testing, improved resource efficiency, and fostered interprofessional collaboration. This demonstrates potential RLU prototype that can be scalable applied to community hospitals, in accordance with the Smart Health System policy.