An investigation of the opportunities and challenges facing the acceptance and adoption of patient focused booking systems in Australian outpatient clinics for improved organisational and patients outcomes

Many public health organisations report problems relating to outpatient booking systems, such as (1) dissatisfied patients who have to wait long periods to obtain appointments with health specialists and (2) wasted resources and inefficiencies associated with last-minute cancellations, requests for rescheduling and large numbers of patients who did not attend (DNA). These problems arise because outdated scheduling systems consider only organisational needs, effectively choosing patients at the top of a waiting list and assigning them to a physician’s available time slots. The failure to consider patient preferences leads to frequent cancellations, requests for rescheduling and did not attend (DNAs). A growing body of literature highlights that self-service technologies (SSTs), which place patient preferences at the forefront of the booking system, can help improve patient satisfaction and organisational efficiency.
Patient-focused booking (PFB) systems are a method of scheduling that allow patients to self-book their outpatient appointments and be involved during the booking procedure. PFB systems are thought to (a) increase patient satisfaction (since patients are given the autonomy to select convenient times); (b) reduce the incidence of DNAs and requests for rescheduling, and (c) reduce the time that employees spend scheduling appointments (since patients do this themselves). PFB systems are already commonly used in primary care systems nationally and internationally and are referred to in the literature as Advance Access (AA) or Open Access systems (OA). PFB systems were introduced in the secondary healthcare system internationally in Europe during the 2000s. PFB systems have been successfully implemented in the United Kingdom (UK) as a compulsory national system. Their implementation reduced DNAs in some areas of the UK from 32% to 2%. However, PFBs are not widely adopted in Australian outpatient clinics, with their lack of use potentially contributing to poor patient experiences and wasted organisational resources.
The overarching aim of this thesis is to identify potential opportunities and challenges facing the acceptance and adoption of PFB systems in outpatient clinics, thereby improving both organisational and patient outcomes. The aim is met by conducting three related investigations: a systematic literature review, a meta-analysis and an empirical study. The Unified Theory of Acceptance and Use of Technology (UTAUT) is used to frame the investigations, focusing on core variables identified in the theory: Perceived Usefulness (PU); Perceived Ease of Use (PEOU); social influence; enablers; barriers; facilitating conditions; and moderators, such as age, experience and voluntary use of technology.

The literature review (Chapter 2) considers published articles from health and other sectors (education, billing, finance/banking, gaming and wearable technology). It was considered necessary to look at non-health sectors because there is resistance to the idea of adopting PFB systems in the healthcare industry, despite the involvement of SSTs in a wide range of other industries. There are also relatively few studies on SSTs in the health sector. Therefore, this study investigated whether learnings from other sectors could be applied to identify potential factors preventing taking up PFB for the benefit of the health sector. The review focused on 80 studies from different industries. Insights from this literature were used to:

1. Identify core variables likely to influence the acceptance of SSTs that have been considered in studies undertaken in the health sector.
2. Identify core variables likely to influence the acceptance of SSTs that have been considered in other sectors but not yet in the health sector.
3. Develop a conceptual model that describes the acceptance of PFB for both organisations and customers.
4. Describe SST acceptance in health and other sectors using the mirror model. The model is a modified UTAUT model that clearly shows (and allows one to differentiate) factors that affect SST from both an organisation’s and a user’s perspective, hence the term ‘mirror’.
5. Identify core UTAUT relationships for more detailed investigation in the meta-analysis.

The acceptance of SSTs is driven by the attitudes and behaviours of both individuals and organisations. The review highlighted that researchers who focused on different sectors and/or on different groups (organisations versus customers/patients) tended to focus on different relationships described by the UTUAT model. There are clear differences in industry structures, customer needs and the types of services across sectors, so the behaviours and intentions of individuals (customers /patients) and organisations (health/non-health) are likely to be driven by different factors. The meta-analysis in Chapter 4 sought to learn more about what those factors might be. It divided the 80 articles described in the literature review into six subsamples (the following underlined words are used hereafter as a short-hand descriptor of each subsample):

1. all organisations (health and non-health sector) and all users (customers and patients)
2. health sector only (organisations and ‘patients’ as health organisation customers)
3. non-health sectors (organisations and customers)
4. health sector and non-health sector
5. all organisations (health and non-health organisations)
6. all users (customers as non-health services and patients as health services users).

The meta-analysis then used the Mann–Whitney U-test (implemented in Statistical Package for Social Sciences version 20) to compare published results (relating to the significance of relationships between core UTUAT variables) across subsamples. The aim of this analysis was to examine whether the reported results from journal articles about relationships between core UTUAT variables were different for various subsamples. The study looked at six main variables and their relationship with the acceptance decision across different subsamples of data. The key variables are PU, PEOU, facilitating condition, enablers, barriers and BI. The analysis did not reveal any statistically significant differences in the relationship between PU, PEOU and BI on acceptance, suggesting that these variables are equally important predictors of technology acceptance across multiple sectors for organisations and users. However, health studies often fail to consider both enablers and barriers’ effects on organisational and individual SST acceptance, where multilevel management changes are needed to adopt new technologies. Additionally, health services need to act as a customer service industry with one-to-one relationships between patients and healthcare providers.
The empirical chapter (Chapter 5) aimed to fill some of the critical information gaps identified in the literature review and learn more about the opportunities and barriers faced in the adoption of SST in the health sector, focusing on outpatient clinics in Australian hospitals. The study employed a mixed-method approach, providing insights from two semi-structured interviews with managers of outpatient clinics (expert opinions) and 26 questionnaires collected from outpatient clinic managers across a diverse range of Australian hospitals. The responses suggest that the inadequate performance of outpatients’ departments is partially due to outdated booking systems that have an organisational focus, confirming insights from the literature. The results also reveal a general lack of awareness of PFBs—clearly explaining the lack of uptake in some clinics. Moreover, comments made during interviews and in the questionnaires highlighted core facilitating conditions/factors that, if addressed, could help overcome the PFB ‘technology’ resistance. These factors include:

1. updating the patient administration system to ensure its compatibility with PFBs
2. training programs for outpatient clerks. With a confirmation that PFB will not cause any job loss and will not replace human job with a technology.
3. transferring knowledge from previous adoption attempts and from research studies to health policymakers. By assessing the existing outpatients’ health information systems. The comprehensive outpatient data could guide decision-making equally at both patient and system levels.
4. exploring financial resources for the new system

The empirical part of this study thus provides essential insights into ways to help bridge the gap between outpatient department needs and the requirements for a successfully implemented (and accepted) PFB.
In summary, the main aim of this study was to find ways to increase the adoption of PFB as a form of SST in outpatient clinics, thereby improving organisational and patient outcomes. It undertakes three research activities (i.e., a systematic review, a meta-analysis and an empirical study) to meet those aims and makes two important contributions to the literature. The first is a methodological contribution: the ‘mirror’ model of acceptance. Although devised for the health sector, it was constructed using insights from multiple sectors and is therefore widely relevant. The second contribution is empirical: this study identified general variables and specific facilitating conditions likely to influence the adoption of PFBs in Australian outpatient clinics. The study contributes to the already existing literature on the capability of SST for improving the scheduling process in outpatient departments. It has also improved our understanding of the enablers and barriers facing health managers in adopting PFB and may help improve the quality of health services in Australia and elsewhere.

The remainder of the abstract is found in the notes below.