Development and application of a data-driven model for the optimization of waiting lists and the evaluation of its impact on service quality: a pilot project in a teaching hospital

Introduction According to current Italian healthcare regulations, the term waiting list refers to the set of scheduled but not yet provided healthcare services, managed based on clinical priority—assessed by the prescribing physician—and related time priority [1]. Furthermore, prolonged waiting times are a major source of patient dissatisfaction, with significant impacts on psychological well-being [2-4]. In this context, the increasing pressure on healthcare professionals to shorten waiting times raises the risk of burnout and work-related stress, which can negatively affect the quality of care provided. Objectives The main goal of this project is to develop and validate a data-driven model aimed at optimizing waiting lists in a University Hospital by combining objective data with the subjective perceptions of both patients and healthcare professionals, in line with national and regional standards. Specific objectives include: assessing ex-ante waiting times by service type, priority level, and available resources using advanced data analytics; reallocating appointment slots to ensure compliance with maximum allowed waiting times, thereby enhancing organizational efficiency; analyzing the organizational and psychological well-being of healthcare professionals through validated surveys; measuring perceived quality and patient well-being with validated surveys; and monitoring key performance indicators (KPIs)—such as average and maximum waiting times, adherence to priority levels, and service volumes—to evaluate the impact of the data-driven model against organizational standards. Materials and Methods The project lasted three years and was carried out at a large teaching hospital in central Italy, selected as a pilot site because of its organizational complexity and high volume of outpatient activities. In the initial phase, a thorough contextual analysis of the organization was conducted, focusing on outpatient scheduling management, booking flows, and average waiting times, also considering the standards established by the Piano Nazionale di Governo delle Liste d’Attesa (PNGLA) 2019–2021 [1] and regional regulations. Two ad hoc questionnaires, validated by the researcher, were employed: 1. Healthcare Personnel Work Well-Being Questionnaire o Administered to staff working in outpatient services (physicians, nurses, healthcare technicians). o Explores perceived workload, professional satisfaction, organizational support, and overall psychological well-being. o Delivered online via a secure platform (accessible through a dedicated link). 2. Patient Perception of Service Quality Questionnaire o Administered to outpatient service users. o Includes sections on overall satisfaction, clarity of information received, trust in the healthcare system, and psychological well-being associated with waiting times. o Delivered in two formats: paper (with subsequent entry into a centralized database) and online via a secure platform (accessible through link and QR code). In addition to subjective data, a monitoring system of Key Performance Indicators (KPIs) was developed to measure organizational and systemic dimensions, including:  ex-ante waiting times,  percentage of services delivered within PNGLA [1] thresholds,  service volumes by priority class. These data enabled the implementation of a data-driven model capable of integrating objective information and subjective perceptions, in line with international recommendations for people- centred healthcare [5]. Results By September 23, 2025, the survey had been completed by 312 healthcare professionals and 198 patients. The main descriptive characteristics are summarized in the following tables. Gender N Percentage Female 209 66,99 % Male 100 32,05 % Prefer not to answer 3 0,96 % Professional Role N Percentage Physician 156 50 % Nurse 141 45,19 % Healthcare technician 15 4,81 % The analysis of mean scores across the dimensions assessed in the Work Well-Being Questionnaire revealed a moderate level of organizational well-being and perceived work management, while interpersonal relationships were rated relatively higher. Variable Mean Std. Dev. Age 53.02 10.11 Years of service 21.27 12.96 Organizational well-being 2.81 0.87 Work management 2.72 0.91 Relationships and support 3.40 0.89 ANOVA results indicated no significant differences across Integrated Care Departments with respect to well-being (F(8,303)=1.01, p=0.432) or work management (F(8,303)=1.78, p=0.081). However, significant differences emerged in the dimension of relationships and support among colleagues (F(8,247)=2.52, p=0.012), suggesting that this aspect is particularly sensitive to organizational context. Similarly, no significant differences were found across professional roles for well-being (F(2,309)=1.04, p=0.354) or management (F(2,309)=0.63, p=0.535). In contrast, an effect approaching significance was observed for relationships (F(2,253)=2.92, p=0.056), indicating that physicians tend to report more positive interpersonal relationships compared to other professional groups. Correlation analyses demonstrated a strong positive association between well-being and work management (r=0.76, p<0.001), as well as significant correlations between well-being and support (r=0.65, p<0.001) and between management and support (r=0.55, p<0.001). These findings confirm that the three domains under investigation are closely interconnected. Regression analysis, controlling for age and professional role, did not reveal significant effects on well-being or work management, highlighting the limited impact of individual-level variables. On the contrary, for the dimension of relationships, being a physician was associated with higher scores (β=0.25, p=0.030). In the end, the correlation between perceived management (as reported by healthcare professionals) and patient-reported reception scores was examined, stratified by macro-area. The interaction regression model was significant (F(7,109)=2.42, p=0.024), with an R² of 0.13. Specifically, in the Medicine macro-area, a negative slope of the interaction was observed (β=-1.09, p=0.067), suggesting that in these departments, an increase in perceived management does not necessarily correspond to greater patient satisfaction. This may point to the presence of additional mediating factors such as clinical complexity or heavy care burden. Conversely, in other areas (e.g., Surgery), the relationship appeared more linear and positive. Variable Coeff. Std. Err. t p Average management 0.48 0.27 1.81 0.073 Macro-area: Medicine 3.18 1.70 1.87 0.064 Management – Medicine -1.09 0.59 -1.85 0.067 Macro-area: Surgery 1.96 3.22 0.61 0.544 Management – Surgery -0.43 1.01 -0.42 0.675 A further area of investigation in this thesis involved the analysis of waiting lists by priority class (U – urgent, B – short, D – deferrable, P – programmable). Data were compared before and after the intervention on outpatient schedules in order to assess potential reductions in mean waiting times. Priority Class Mean Waiting Time Pre-Intervention (days) Mean Waiting Time Post- Intervention (days) % Reduction B 80.6 38.2 52.6 D 157.4 154.7 1.7 P 251.2 271.7 -8.2 U 120.0 40.0 66.7 The results demonstrate a particularly relevant improvement for class U (urgent) cases, with an average reduction of 66.7%, and for class B, with a 52.6% decrease. Class D showed only a marginal improvement (1.7%), whereas class P recorded a worsening (+8.2%). Specifically, for the service ECOCOLORDOPPLER of supra-aortic trunks in class U, the pre- intervention waiting time was drastically reduced from 192.0 days to 2.0 days, thereby fully meeting the prescribed standard. Similarly, for the service First ENT outpatient visit in class U, the average waiting time decreased from 10.0 to 3.0 days, achieving full compliance with the 3.0-day standard. Overall, these findings confirm the effectiveness of the reorganization intervention for urgent and short-term services, with concrete improvements documented in both instrumental diagnostic procedures and initial specialist consultations. Discussion The results of this pilot project confirm that targeted organizational interventions—such as the revision of scheduling systems and the introduction or modification of priority classes—can lead to significant improvements in waiting times and patient-perceived satisfaction, particularly for urgent and short-term services. These findings are consistent with international literature showing that shorter waiting times are associated with higher patient satisfaction [6]. Conversely, Bleustein et al. (2014) demonstrated that prolonged waiting times are perceived negatively by patients [7]. Another relevant finding concerns differences between service areas (macro-areas), particularly in Medicine, where the impact of staff-perceived management on patient satisfaction appears attenuated or even reversed compared to other macro-areas. Existing literature demonstrates that organizational factors such as staff well-being, teamwork, and patient safety are interconnected, and that improvements in one domain do not automatically ensure uniform outcomes across all contexts [8]. The results are also aligned with other sector-specific studies, including recent meta-analyses showing that burnout and low professional satisfaction negatively affect perceived quality of care [9]. In particular, staff engagement has been positively correlated with both patient safety and satisfaction [10]. Finally, the moderation analysis highlighted that the relationship between perceived management by healthcare professionals and patient satisfaction is not uniform: the organizational context (department type, clinical complexity, service demand density) strongly modulates the link between management effectiveness and external perception. Conclusion This pilot project demonstrated that more rational management of scheduling and priority systems can substantially reduce waiting times, with a positive impact on patient satisfaction, particularly in U and B priority classes. However, deferrable (D) and programmable (P) services continue to represent a critical area: further investigation into the underlying causes is required in order to design and implement effective corrective measures. Another key aspect that emerged concerns the well-being of healthcare professionals, which indirectly influences patient perceptions. This finding suggests that investments in organizational climate and staff support can also positively affect patient experience. Moreover, the moderation analysis revealed significant differences across macro-areas, indicating that not all contexts respond equally to the proposed interventions. 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