Towards the development of an integrated model for examining the determinants affecting the use of Queue Management Solutions in Healthcare
ALQUDAH, ADI AHMAD ALI
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Over the years, long queues were recognized as a common problem in the healthcare domain, and it is significant to manage them for patients' safety and overall satisfaction. Prolonged queues in healthcare organizations can produce high levels of distraction for the employees instead of focusing on their original activities. As a solution, queue management technologies became more popular in healthcare organizations to solve queue issues, gather data, and generate statistical reports for the current and future flow trends. The adoption of new technologies in healthcare has been turned into a must rather than a luxury due to the rapid changes of technology advancements and people's needs. In general, the success of technology adoption in healthcare relies on the behavior of end-users towards accepting and using the technology. Queue management solutions (QMS) face resistance from users, and their acceptance is not assured. A quick review of the literature showed a lack of studies that discuss the acceptance of QMS. Therefore, this research has three main objectives. First, conducting a systematic review to address the research gaps in the existing literature and understand the extensively utilized acceptance models in healthcare and their related constructs. The systematic review included empirical studies published between January 2010 and December 2019 on the topic of technology acceptance in healthcare. A total of 1,768 studies have been reviewed, and 142 studies were found eligible and considered in the analysis. Through the analysis, the technology acceptance model (TAM) and the Unified theory of acceptance and use of technology (UTAUT) have been recognized as the prevailing models in technology acceptance in healthcare. Additionally, 11 factors from various acceptance models were found extensively investigated to understand and analyze the technology acceptance in the healthcare domain. These factors include Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Attitude Towards, Behavioral Intention, Use Behavior, Computer Anxiety, Computer Self-Efficacy, Innovativeness, and Trust. In line with the gaps found in the literature, this research has presented a case study for the currently implemented queue management solution (QMS) in the out-patient department (OPD) in a healthcare organization in UAE. The research discussed the suggested business and technical optimizations that include integrating the QMS with the electronic medical records solution (EMR). The integration was achieved using Health Level Seven (HL7) integration standards, including the exchange of custom-designed XML and HL7 messages. The goal of the integration was to implement a novel tool for patient’s self-check-in and enhance the ease of use and usefulness of QMS. As a pilot implementation, the feasibility of the newly implemented tool was assessed through a simulation experiment in the internal medicine clinic over two different weeks (control and intervention). A total of 127 appointments were identified as eligible and included in the study. The patient’s journey was split into five stages: identification, wait to triage, triage process, wait to treatment, and treatment process. The results revealed that the new tool is beneficial, and the median times to finish the processes within the patient’s journey have significantly decreased. To evaluate the use of the enhanced QMS, this research develops an integrated model based on the integration of various constructs extracted from different theoretical models, including the UTAUT, TAM, and social cognitive theory (SCT) along with trust and innovativeness as external factors. The model was empirically validated using the partial least squares-structural equation modelling (PLS-SEM) approach based on data collected through a questionnaire survey from 242 healthcare professionals. In brief, the results exposed that the suggested model can be helpful to explore the acceptance of information technologies in healthcare. The model has explained 66.5% of the total variance in the behavioral intention to use the enhanced QMS, along with 59.3% of the total variance for the actual use of the enhanced QMS. The results indicated that innovativeness and computer self-efficacy factors have a positive significant influence on the effort expectancy of professionals to use QMS. The computer anxiety factor has a negative significant influence on the effort expectancy to use QMS. Besides, trust and computer self-efficacy factors have a positive significant influence on the performance expectancy when using QMS. Other results, related implications, limitations, along with future research were also clarified and discussed.