1. Introduction
The quality of healthcare delivery is shaped not only by clinical skills and treatment protocols, but also by how effectively patients are guided into appropriate care [
1]. From a dental public health perspective, triage systems play a critical role in managing access, prioritizing need, and ensuring that limited clinical resources are used efficiently [
2]. Structured triage systems have therefore been adopted in both emergency medicine and dentistry to manage patient flow, stratify cases by urgency, and direct patients to suitable care pathways [
3,
4,
5]. When integrated with standardized care pathways and digital decision support tools, these systems can streamline workflows, reduce avoidable visits, and improve patient experience at the population level [
6,
7,
8]. Well-designed triage processes also help shorten waiting times and optimize the use of available healthcare resources, contributing to more equitable and efficient service delivery [
4,
8,
9].
A systematic review by Farzandipour et al. (2024) reported that structured tele-triage can reduce unnecessary emergency department visits by 1.2% to 22.2%, while also improving patient satisfaction, with reported rates ranging from 53% to 98% across diverse settings [
9]. In dentistry, the accuracy and timeliness of triage decisions are particularly important, as delays or misclassification can exacerbate oral disease and increase demand for urgent care [
1]. During the COVID-19 pandemic, telephone-based dental triage enabled the remote management of urgent dental complaints, with studies indicating that approximately 49–70% of cases could be safely managed without face-to-face visits [
3,
4]. During periods of high demand, dental triage systems also support symptom control and preserve in-person clinical capacity for patients with time-sensitive needs, reinforcing their value as a public health tool [
3,
4].
Beyond traditional telephone triage, recent technological advances including teledentistry platforms, mobile health (mHealth) applications, and AI-assisted systems have expanded the scope and precision of remote patient assessment and care routing [
7,
10,
11]. These technologies typically incorporate structured symptom questionnaires, image capture, and real-time communication features, which together support more responsive and accurate triage decisions [
7,
10,
12]. Studies suggest that AI-supported and smartphone-based triage systems can improve operational efficiency and patient experience [
7,
11]. From a dental public health standpoint, such tools have the potential to reduce waiting times and improve access to care, although evidence remains variable across clinical contexts and implementation models [
7,
10,
11]. A systematic review by Gurgel-Juarez et al. (2022) reported that teledentistry can improve access and demonstrates a diagnostic sensitivity of 80–88% and specificity of 73–95% for referral and treatment planning, with performance influenced by modality and setting [
10].
Despite the expanding adoption of digital triage solutions, persistent systemic and operational inefficiencies continue to limit their public health impact. These challenges can undermine triage effectiveness by delaying access to care and negatively shaping patient experience [
6,
13]. Structural barriers including limited digital infrastructure, workforce shortages, and socioeconomic constraints further restrict equitable access and influence care-seeking behavior [
6,
14]. Such issues are particularly evident in Saudi public hospitals, where resource imbalances, workforce limitations, and fragmented digital systems contribute to inefficiencies and delays in service delivery [
15,
16]. In these contexts, unclear or poorly integrated triage pathways may result in patients being directed to inappropriate clinics, disrupting continuity of care and diminishing overall system performance [
11,
17].
Within academic dental institutions, triage systems play a dual role: facilitating access to care while aligning case complexity with the educational requirements of students and interns. At the Dental Teaching Hospital of Umm Al-Qura University (DTH-UQU), patients begin their care journey by completing a digital triage form that captures demographic information and chief complaints prior to formal registration. However, reliance on self-reported symptoms can limit diagnostic accuracy, as patients may not fully capture the clinical context of their condition. This can lead to inconsistencies and potential misclassification [
14,
18]. Existing evidence shows wide variation in self-triage accuracy, ranging from 11.5% to 90.0%, underscoring the need for careful validation of automated triage tools to ensure patient safety and appropriate resource allocation [
14,
18].
Following submission, triage staff review patient information and assign cases to appropriate clinics or student providers. In practice, this process is often constrained by misalignment between patient needs and educational requirements, repeated registrations, and unclear prioritization criteria. These challenges can create confusion, prolong waiting times, and delay access to care, with implications not only for individual patients but also for system efficiency and equity [
16].
To date, much of the research on triage systems has focused on technical performance indicators, such as diagnostic or triage accuracy, which vary widely across systems and settings [
14]. In contrast, patient-centered outcomes, particularly user experience and patterns of service use, have received less attention [
6]. This limits our understanding of how triage system design influences patient behavior, continuity of care, and psychological responses such as anxiety or uncertainty. Emerging evidence highlights that patient experience, shaped by communication quality and system usability, is a key driver of satisfaction and continued engagement with digital health services [
7,
19]. However, many studies remain focused on narrow measures, such as satisfaction scores, with limited exploration of broader experiential and contextual factors that influence engagement and access [
7,
19].
Despite the growing reliance on digital triage systems, qualitative research examining the experiences of patients and healthcare staff particularly within teaching hospitals and Middle Eastern contexts remains scarce [
20]. From a dental public health perspective, understanding these experiences is essential to ensure that digital triage systems are usable, equitable, and responsive to population needs [
7]. These gaps highlight the importance of examining how patients perceive triage systems and how such perceptions shape adherence, trust, and subsequent care-seeking behavior.
Accordingly, this qualitative study aims to explore the perceptions, experiences, and expectations of patients and staff regarding the digital triage system at DTH-UQU. By identifying communication gaps and usability challenges, this study seeks to amplify the voices of service users and providers and to inform the development of patient-centered, efficient triage processes that support clinical education while advancing equitable access to oral healthcare.
2. Materials and Methods
2.1. Ethical Considerations
Ethical approval for this study was obtained from the Institutional Review Board of Umm Al-Qura University (Approval No. HAPO-02-K-012-2024-02-1985, on 13 February 2024). All participants were provided with written information about the study and gave informed consent prior to participation. Consent was obtained before each interview, and participants were assured of confidentiality and their right to withdraw at any stage without consequence.
2.2. Study Design
This study employed a qualitative descriptive design to explore the perspectives of dental professionals, administrative staff, and patients regarding the current electronic triage system at DTH-UQU. The study was conducted and reported in accordance with the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines (
Supplementary File S1).
2.3. Setting and Sample
The study was conducted at DTH-UQU, an academic dental institution located in Makkah, Saudi Arabia. A purposive sampling strategy was used to recruit a diverse group of participants from three key stakeholder categories: dental professionals representing major clinical specialties, administrative staff involved directly in the triage process, and patients who had recently used the electronic triage system.
Dental professionals and administrative staff were selected based on their seniority and active involvement in patient triage procedures. Staff participants were eligible if they were currently affiliated with DTH-UQU and had at least two years of professional experience. Patient participants were eligible if they were aged 18 years or older and had completed the electronic triage process at DTH-UQU. Individuals not affiliated with DTH-UQU or with less than two years of professional experience were excluded. Patients were identified from records of individuals who had used the electronic triage system during the preceding six months. Eligible patients were contacted, provided with an information sheet and consent form, and invited to participate in the study. A total of 20 patients were invited, of whom 16 agreed to participate.
Dental professionals and administrative staff were invited to participate via institutional email. A total of 12 staff members were invited, of whom 9 agreed to participate. For those who expressed interest, interviews were scheduled at a convenient time. Patient interviews were conducted by telephone, while interviews with dental professionals were carried out in their offices.
2.4. Data Collection
Data were collected between 1 September 2024 and October 2025 through semi-structured, individual interviews conducted by O.S. and M.A., both qualified dentists.
A topic guide, informed by the existing literature and reviewed by subject-matter experts, was used to support consistency across interviews (
Table 1) [
1,
14,
16]. Patient interviews were conducted in Arabic to allow participants to express their experiences comfortably in their native language. All interviews were audio-recorded and transcribed verbatim in Arabic. The transcripts were then translated into English by bilingual members of the research team who were proficient in both languages and familiar with the research context. To ensure translation quality and preserve meaning, the translated transcripts were reviewed against the original Arabic versions by a second bilingual researcher. Any discrepancies or ambiguities were discussed within the research team and resolved by consensus. In addition, a purposive subset of transcripts was independently checked to further support the accuracy and consistency of the translation. Staff and faculty interviews were conducted in English. Translations were reviewed within the research team to ensure accuracy and preservation of meaning.
Two pilot interviews with patient participants were conducted by both researchers to assess the clarity and suitability of the interview questions, ensure consistent use of the topic guide, and minimize potential interviewer bias [
21]. Data from the pilot interviews were not included in the final analysis.
To maintain consistency across interview modes, all interviews followed a semi-structured format. Interviewers used similar probing and rapport-building approaches to encourage detailed responses. In telephone interviews, additional attention was paid to verbal cues, such as tone, pauses, and emphasis, to account for the absence of non-verbal communication. These measures were intended to support both the depth and comparability of data across formats.
The interview guide consisted of open-ended questions exploring participants’ experiences with the electronic triage system, perceived effectiveness and outcomes, expectations, and suggestions for improvement. Interviews typically lasted between 15 and 45 min. NVivo 14 software (Lumivero, Denver, CO, USA) was used to support data organization, management, and coding.
2.5. Data Analysis
All audio recordings were transcribed verbatim before analysis. The data were analyzed using Braun and Clarke’s (2006) reflexive thematic analysis approach [
22], following its six phases: (1) familiarization with the data, (2) generation of initial codes, (3) searching for themes, (4) reviewing themes, (5) defining and naming themes, and (6) producing the final report.
The analysis followed a primarily inductive approach, allowing themes to emerge from the data rather than being driven by pre-existing theoretical frameworks. However, the process was also informed by the study aims and relevant research, reflecting a more pragmatic and exploratory orientation.
NVivo 14 software was used to support data management, coding, and organization. It also facilitated comparisons across participant groups and provided a space for storing analytic notes and documenting emerging patterns during the analysis.
A coding framework was developed iteratively by the research team and refined as the analysis progressed. Initial coding was conducted independently by members of the team to support close engagement with the data. Codes and emerging themes were then compared and discussed in regular team meetings, where differences in interpretation were examined and used to refine code definitions and strengthen the analysis. This iterative process helped establish a shared understanding of the coding framework.
Decisions related to code development, refinement, and theme generation were documented throughout, creating an audit trail to enhance transparency and rigor. To further support trustworthiness, member checking was conducted with a purposive subset of participants (n = 8), including both patients and staff. Participants were contacted following preliminary analysis and provided with a summary of the key findings. They were invited to comment on whether the interpretations reflected their experiences [
23]. No major discrepancies were identified during this process.
Data collection and analysis were conducted concurrently and continued until thematic saturation was reached, defined as the point at which no new codes, categories, or themes emerged from successive interviews. Saturation was assessed iteratively, and recruitment was discontinued once additional interviews yielded no substantially new insights [
24].
To ensure trustworthiness, this study addressed credibility, dependability, confirmability, and transferability. Credibility was supported through prolonged engagement with the data, iterative analysis, and member checking with participants. Dependability was enhanced by maintaining a clear audit trail documenting coding decisions, theme development, and analytic processes. Confirmability was supported through reflexive practices and team discussions, ensuring that interpretations were grounded in the data rather than researcher assumptions. Transferability was facilitated by providing a detailed description of the study context, participant characteristics, and data collection processes, enabling readers to assess the applicability of findings to similar settings [
25].
2.6. Methodological Rigor and Reflexivity
Rigor and trustworthiness were addressed throughout the study in accordance with established qualitative research principles. Credibility was enhanced through the use of semi-structured interviews, iterative data analysis, and member checking with selected patient and staff participants to ensure that interpretations accurately reflected their experiences. Confirmability was strengthened through independent coding by multiple researchers and regular analytic discussions to compare interpretations and resolve discrepancies by consensus. Transferability was facilitated by providing a detailed description of the study setting, participant characteristics, and research context.
Reflexivity was considered throughout the research process. The interviews were conducted by O.S. and M.A., both qualified dentists with academic and clinical backgrounds in dental and health sciences and formal training in qualitative research methods. Both interviewers graduated from the same dental school and were familiar with the institutional context of the study site. No prior personal relationship was established with patient participants before study commencement. Participants were informed about the purpose of the study, the researchers’ professional roles, and the aim of exploring experiences with the digital triage system to inform service improvement.
The researchers maintained reflexive awareness throughout data collection and analysis. During interviews, they relied on open-ended questions and avoided leading prompts, allowing participants to describe their experiences in their own terms. For example, rather than assuming a shared understanding of clinical procedures, interviewers encouraged participants to explain their experiences in detail, even when these appeared familiar.
The researchers also considered how their professional backgrounds might shape interpretation. During analysis, regular discussions were used to revisit initial assumptions and explore alternative readings of the data. Instances in which participants’ accounts diverged from expected clinical pathways were examined closely rather than set aside, allowing for the development of new insights.
At the same time, the team’s clinical and institutional familiarity informed the interpretation of nuanced responses, particularly in relation to workflow challenges, patient–provider interactions, and system constraints within the teaching hospital context. This combination of reflexive awareness and contextual understanding supported both the depth and credibility of the analysis.
4. Discussion
This study examines the use of a digital dental triage system within an academic dental setting and identifies a clear disconnect between what patients expect and how the system currently functions. While patients consistently described the system as easy to use, this sense of usability did not translate into overall satisfaction.
Some subthemes appeared more prominently in staff accounts than in patient narratives, particularly those related to clinical processes, workflow management, and institutional constraints. This likely reflects the differing roles of the two groups, as staff have greater insight into system-level operations that may not be visible to patients. Rather than seeking equal representation across groups, the analysis focused on capturing the distinct perspectives each group brought to the study. These differences were treated as analytically meaningful, offering complementary insights into how the digital triage system operates.
Gaps in communication, delayed responses, and unclear care pathways undermined confidence and limited the perceived value of the triage process. These findings suggest that, from the patient’s perspective, system success is defined not only by how simple it is to complete, but by whether it provides timely feedback, clear direction, and a meaningful pathway to care [
19,
26]. At the same time, staff tended to define system quality in terms of clinical adequacy and diagnostic usefulness, highlighting a clear difference in how success was understood by users and providers. This divergence in criteria contributed to frequent misclassification, inefficient patient routing, and recurring referral loops, illustrating the limitations of relying on simplified, unstructured self-reported data for clinical decision-making in digital triage systems [
6,
14]. Rather than reflecting individual error, these challenges point to structural tensions between patient-friendly design and the clinical detail required for effective triage.
The observed usability–satisfaction gap can be understood through Expectation-Confirmation Theory, which suggests that satisfaction depends largely on whether system performance aligns with users’ prior expectations of outcomes [
27,
28]. In this study, patients often associated completion of the triage form with prompt communication and appointment scheduling. When these expectations were not met, satisfaction declined even though the system itself was perceived as easy to use. These findings are consistent with broader digital health literature showing that usability alone is insufficient to sustain engagement or satisfaction in the absence of meaningful feedback, transparency, and perceived responsiveness [
19,
27,
28]. Similarly, research on mHealth interventions suggests that patient satisfaction is shaped more by perceived effectiveness, communication quality, and continuity of care than by interface simplicity alone [
19,
29]. Taken together, the mismatch observed in this study reflects a breakdown in feedback mechanisms and expectation management, rather than a failure of interface design itself.
While Expectation Confirmation Theory helps explain how patients evaluate system performance against their expectations, the findings also highlight broader system-level influences. Factors such as workflow integration, communication processes, institutional constraints, and staff capacity shaped how digital triage functioned in practice. The gap between patient expectations and system delivery therefore reflects not only a perceptual mismatch but also an implementation challenge within a complex healthcare setting. Considering both perspectives provides a more comprehensive understanding of digital triage performance.
Communication breakdown emerged as the most critical system-level weakness, directly linking patient dissatisfaction with inefficiencies in care delivery. From a dental public health perspective, the absence of post-registration communication such as confirmation, status updates, or clear next steps creates barriers to access, particularly for patients with urgent or time-sensitive oral health needs. This finding is consistent with previous studies, such as Sexton et al. (2022), which identified communication as a key determinant of patient experience in digital triage systems [
6], and Woods et al. (2019), who reported that unclear communication pathways negatively affect user engagement in mHealth platforms [
19]. Patients’ descriptions of the system as a “black box” highlight how silence after submission led to uncertainty, repeated registrations, prolonged waiting, and loss of trust. Similar patterns have been reported in telehealth research, where inadequate communication is associated with increased anxiety, reduced engagement, and poorer perceived quality of care [
6,
19,
30].
These communication failures also carry implications for equity and unmet need. Delays, unclear pathways, and repeated triage attempts disproportionately affect patients with lower health literacy, limited digital access, or fewer resources to navigate the system, potentially exacerbating existing oral health disparities. Evidence from digital triage studies indicates that weak feedback loops can compromise both patient safety and continuity of care, particularly when triage outputs are poorly coordinated with downstream services [
6,
29]. Staff accounts in this study further suggest that communication gaps operate across multiple levels, patient–system, staff–staff, and system-level governance, underscoring the need for standardized protocols and transparent reporting.
The findings also reveal a policy-relevant tension between simplicity and clinical adequacy. While simplified triage forms improve accessibility and uptake, over-reliance on unstructured patient self-reporting can undermine diagnostic accuracy and efficient resource allocation. This aligns with previous research, such as Dornellas et al. (2023) and Liu et al. (2024), which highlight the importance of structured data capture and the limitations of relying solely on patient-reported information for accurate clinical triage [
12,
18]. From a public health standpoint, strengthening communication, standardization, and system integration is essential to ensure that digital triage supports equitable access, minimizes unmet oral health needs, and contributes to sustainable service delivery in publicly funded and academic dental settings.
This study contributes to dental public health by highlighting how organizational and educational structures shape the real-world performance of digital triage systems in teaching hospitals. In academic dental settings, triage decisions are inherently influenced by student competency levels, case allocation requirements, and limited clinical capacity. Patients, however, are largely unaware of these constraints. As a result, delays, non-response, or case reassignment are often interpreted as system inefficiencies rather than as reflections of institutional realities. These findings underscore the importance of evaluating digital health interventions within their operational and educational contexts, particularly where care delivery and workforce training are closely intertwined.
Both patients and staff identified capacity limitations and workflow inefficiencies as major barriers to system effectiveness. From the patient perspective, these constraints manifested as prolonged and uncertain waiting periods, while staff described increased administrative burden due to duplicate registrations, unclear prioritization, and reduced clinical efficiency. These findings are consistent with previous research, such as Alrumaim et al. (2025) and Riboli-Sasco et al. (2023), which reported that digital triage systems can increase demand without a corresponding expansion in clinical capacity, particularly when communication pathways and feedback mechanisms are limited [
31,
32]. In this study, repeated registrations were closely linked to the absence of confirmation or updates, highlighting how communication failures can inflate demand, obscure unmet need, and place additional strain on already limited public dental resources.
The suggested improvements point away from isolated usability enhancements and toward system-level redesign grounded in public health principles. Patients emphasized the need for consistent communication such as confirmation messages, real-time status updates, and transparent waiting times to transform uncertainty into informed waiting and restore trust. Staff, in turn, focused on improving input quality and workflow efficiency through simpler language, structured symptom checklists, and guided image capture to reduce misclassification. Both groups converged on the importance of stronger digital integration, including interoperability with electronic health records, automated detection of duplicate registrations, and real-time reporting. Together, these recommendations reflect the need to align patient-facing interfaces with back-end system intelligence to support equitable access, efficient resource allocation, and sustainable service delivery [
7,
33].
The implications of this study extend to the design and implementation of next-generation digital triage systems in dentistry. Emerging technological tools offer potential solutions to many of the challenges identified. For example, AI-assisted symptom checkers and image-based diagnostic tools, such as recently developed mobile health (mHealth) applications for detecting oral conditions, could enhance the accuracy of patient-reported information and reduce misclassification [
34]. Tools incorporating guided image capture and automated clinical assessment may support more reliable remote triage, particularly for conditions that are difficult for patients to describe using text alone.
In addition, real-time communication platforms and automated notification systems could address the communication gaps identified in this study by providing timely updates and clearer care pathways. Integration with electronic health records and the use of predictive analytics may further improve workflow efficiency and patient routing. Together, these technologies highlight how combining digital usability with clinically informed decision support systems may strengthen both patient experience and system performance in academic and public dental healthcare settings.
This study contributes to the limited qualitative literature on digital dental triage in academic settings by incorporating both patient and staff perspectives and by examining system-level factors beyond usability alone [
35]. The findings offer practical insights for academic and public dental services facing similar capacity pressures. Teaching hospitals often share key characteristics, including the integration of clinical care and student training, which can give rise to comparable challenges related to triage, workflow, and patient expectations. At the same time, differences in organizational structure, patient populations, and digital infrastructure across institutions may shape how these systems function in practice.
The findings are particularly relevant to publicly funded dental systems managing high demand and constrained resources. However, differences in health system organization, workforce capacity, and levels of digital adoption should be considered when applying these results to other settings. Future multi-site studies would help assess how digital triage systems perform across a wider range of academic and public healthcare contexts.
This study did not specifically examine differences in experiences across patient characteristics such as age, education, or digital literacy. As a result, potential variation between subgroups may not have been fully captured. Future research could explore how these factors influence patient engagement with digital triage systems.
In addition, interviews were conducted using different modes (telephone for patients and in person for staff), which may have influenced the depth or nature of responses. While steps were taken to maintain consistency across interview formats, these differences should be considered when interpreting the findings.