Abstract
Introduction
Growing elective surgical waiting lists remain a persistent global challenge, exacerbated by the COVID-19 pandemic, which led to the widespread suspension of non-urgent procedures. In the Gulf region, elective surgery cancellation rates reached up to 72% by 2020, compounding surgical backlogs. At Salmaniya Medical Complex, Bahrain’s largest public tertiary hospital, the resulting backlog overwhelmed existing systems that relied on fragmented spreadsheets and manual logs. This Quality Improvement Project (QIP) aimed to improve surgical waiting list management using a centralized, data-driven approach to reduce backlog, improve scheduling, and enhance theatre utilization.
Methods
This retrospective pre-post QIP included all adult patients (≥18 years) listed for elective, non-cancer surgery across secondary and tertiary surgical specialties between January 2019 and December 2023. Interventions, implemented from January to August 2024, included: (1) consolidating all waiting list data into a single computerized system; (2) validating and updating entries through direct patient contact; and (3) integrating a centralized repository within the Electronic Patient Record (EPR) system. This enabled real-time tracking, automated prioritization, and flexible scheduling. The primary outcome was waiting list volume. Secondary outcomes were mean waiting time to surgery and theatre utilization. Descriptive statistics and paired t-tests were applied to assess the significance of any differences observed with a significance of p<0.05.
Results
From January to August 2024, the surgical waiting list decreased by 51.74%, from 9,597 to 4,627 patients. Major reductions (relative to the baseline waiting list) were observed in Ophthalmology (69.40%), Urology (59.23%), and Orthopedics (58.59%). Mean waiting times (SD) decreased from 12.7 (7.72) to 7.2 (4.75) months (p = 0.014), corresponding to a 158-day reduction. Mean monthly caseload in the main operating theatres (MOT) increased from 1,469 (139.3) cases in 2023 to 1,608 (94.4) in 2024 (p < 0.001).
Conclusion
A centralized, EPR-integrated surgical waiting list system significantly reduced surgical backlogs and delays, while improving coordination, prioritization, and theatre utilization. Differences in outcomes across departments reflect varying case complexities and highlight the need for tailored strategies. This scalable model supports more efficient post-pandemic recovery and long-term service resilience.
Keywords
Introduction
Elective surgical waiting lists represent a persistent challenge for healthcare systems globally, with prolonged delays negatively impacting patient outcomes, operational efficiency, and public trust. 1 The COVID-19 pandemic further exacerbated these issues, causing widespread suspension of elective procedures and a dramatic rise in surgical backlogs.2,3 International modeling studies, based on estimated surgical case volumes across 190 countries, estimated that over 28 million elective surgeries were canceled or postponed worldwide during 12 weeks of peak disruption, with each additional week compounding the backlog, and with an overall global cancellation rate of 72.3% of elective surgical procedures.1,4 In the United Kingdom alone, it is estimated that 1.5 million elective surgeries were canceled in 2020, highlighting the scale of the problem and its consequences for patient care and system performance.5,6
Across the Middle East, the pandemic’s effect on surgical services mirrored global trends but presented some distinct regional challenges. In Bahrain and neighboring Gulf countries, the suspension of elective surgeries led to a rapid accumulation of backlogged cases. 2 In Saudi Arabia, orthopedic procedures decreased by 75.6% in the second quarter of 2020 compared to the same period in 2019, with subspecialties like sports medicine and arthroplasty disproportionately affected due to their elective nature. 7 Kuwait, Bahrain, Oman, and Qatar reported cancellation rates of approximately 70% for elective cases, while the United Arab Emirates and Saudi Arabia saw rates as high as 72%. 1 Waiting times increased, and patient anxiety rose as a result. Health systems in the region responded by gradually resuming services, adopting prioritization frameworks, and increasingly relying on digital health tools to streamline scheduling and manage waiting lists more efficiently.4,8 Despite these efforts, returning to pre-pandemic waiting list levels remains a significant challenge, highlighting the need for sustained, data-driven solutions. 9
Various policies have been studied to reduce the size and waiting times, including management of referral pathways, patient prioritization, perioperative time management, quality improvement methods for surgical care pathways, and waiting time targets for hospitals. 10-12 Current literature suggests that effective management of surgical waiting lists requires more than simply allocating additional resources; evidence increasingly supports multidimensional strategies that combine process optimization, digital transformation, and robust data-driven decision-making.10,13 Informatics solutions, particularly the integration of real-time electronic patient record (EPR) systems, have demonstrated potential to enhance prioritization, scheduling, and coordination of surgical services, leading to measurable reductions in waiting times and improved theatre utilization.5,12-14
At Salmaniya Medical Complex (SMC), Bahrain’s principal public tertiary care provider, surgical waiting list management prior to the pandemic relied on fragmented, department-specific systems, including independently maintained spreadsheets, paper logs, and non-standardized tracking processes. While these approaches allowed basic tracking, they were limited by duplication, inconsistent data entry, lack of real-time updating, and minimal interoperability across departments. Consequently, there was no unified or reliable overview of waiting lists, impairing prioritization, coordination, and efficient allocation of operating theatre capacity.
These limitations became more pronounced during the COVID-19 pandemic, when elective surgeries were temporarily suspended in line with national safety protocols.6,15 Services gradually resumed in 2022; however, the resulting backlog highlighted the need for a more integrated, system-wide approach to surgical flow management. 13
The transition to a centralized system represented both a technical and organizational redesign. This involved sequential steps including: (1) identification and consolidation of all existing data sources; (2) standardization of data definitions and fields; (3) systematic validation of patient records; and (4) integration into the Electronic Patient Record (EPR) system to enable real-time access and continuous updating. This phased approach ensured data integrity while minimizing disruption to ongoing clinical operations.
The primary aim of this retrospective pre-post quality improvement project is to evaluate the system-level impact of centralized flow management, with a primary operational objective to achieve a ≥50% reduction in elective surgical waiting lists, over an 8-month period from January 2024 to August 2024. The secondary objectives included reducing average waiting times and improving theatre utilization. These outcomes are not solely operational metrics but are also associated with wider clinical and economic implications, including reduced morbidity, decreased indirect costs such as productivity loss, and more efficient use of healthcare resources. This study, therefore, situates its findings within this broader health system context.
Methodology
Study Design and Setting
This retrospective pre-post QIP was conducted at Salmaniya Medical Complex (SMC), the largest public government hospital in Bahrain, providing secondary and tertiary care to a population of approximately 1.7 million. SMC comprises 1,200 beds and 16 operating rooms. The study focused on all patients scheduled for elective, non-cancer surgeries who were on the surgical waiting list between January 2019 and December 2023, with interventions and data collection spanning January 2024 to August 2024. This quality improvement study is reported in accordance with the SQUIRE 2.0 (Standards for Quality Improvement Reporting Excellence) guidelines. 16
Study Population and Data Sources
The study included all adult patients (≥18 years) listed for elective surgery across secondary and tertiary surgical specialties during the study period. Exclusion criteria were age below 18 years and procedures classified as emergency or cancer-related. Data were extracted from two primary sources: physical admission documents and the hospital’s electronic health record system (ISEHA). Where discrepancies were identified, records were reviewed and updated during the validation process via direct patient contact. Collected variables included patient age, sex, referring surgical department, type of surgery, date added to the waiting list, as well as contact details and relevant clinical notes.
Interventions
The Quality Improvement Project (QIP) was implemented in three sequential phases. In the first phase, we transitioned away from scattered departmental Excel files and paper logs, consolidating all surgical waiting list data into a single, computerized system. This step enhanced data accessibility, ensured consistency in record-keeping, and improved coordination across surgical departments.
In the second phase, the focus shifted to validating and updating the surgical waiting list, which had grown substantially due to pandemic-related disruptions. Over six months, a consultant-led team supported by approximately 60 rotating surgical interns systematically reviewed the list and contacted patients to verify their surgical status and eligibility. The outcomes of this verification process were carefully documented, categorizing patients as still awaiting surgery at SMC, having already undergone surgery elsewhere, no longer requiring the procedure, having been referred to another center, being deceased, or being unreachable despite multiple contact attempts. The number of contact attempts and reasons for removal from the list were also recorded. This comprehensive review ensured the accuracy of the waiting list, removed ineligible cases, and allowed us to gauge the precise number of patients on the surgical waiting lists.
As part of this phase, patients who remained eligible for surgery at SMC were also assessed for potential referral to external medical centers. This proactive screening process aimed to identify suitable candidates for transfer, thereby reducing the burden on the hospital’s operating theatres, optimizing internal resource utilization, and accelerating access to care for all patients on the waiting list.
In the final phase of the project, a centralized repository was established within the hospital’s Electronic Patient Record (EPR) system to consolidate and manage the updated surgical waiting list. More than 8,000 patient records were digitized, enabling real-time tracking, automated prioritization, and more flexible scheduling. This centralized platform facilitated dynamic coordination between teams, optimized theatre utilization, and reduced scheduling inefficiencies. By embedding a data-driven, continuously accessible framework into the hospital’s digital infrastructure, this intervention laid the foundation for sustainable improvements in surgical waiting list management.
Measures
To evaluate the impact of the intervention, primarily quantitative measures were used. The primary outcome was the change in the absolute number of patients on the surgical waiting list, comparing the total at the start of the intervention (January 2024) to the total at the end of the study period (August 2024). This directly reflected the effectiveness of the intervention in reducing the backlog of elective surgeries.
Secondary outcomes included changes in surgical waiting times and operating theatre utilization. Waiting time was defined as the mean duration, in months, from the date a patient was added to the waiting list to the date of surgery. Waiting-time analyses were calculated among patients who underwent surgery within the measurement periods, using weighted means by consultant caseload. Theatre utilization was assessed by comparing the total number of elective operations performed before and after the intervention, using data initially gathered from paper theatre logs and subsequently from the EPR system. These measures provided insight into improvements in the timeliness of care and efficiency of resource use.
Process measures were also evaluated to assess the effectiveness of the implemented strategies. Data completeness and accuracy were measured by the proportion of patients with verified demographic information, updated surgical status, and complete medical records. This was assessed before and after the transition to the EPR system, with discrepancies resolved through direct patient contact and consultant-led review. The effectiveness of patient communication strategies was evaluated by tracking the proportion of patients successfully contacted and the time required to update their records.
Informal feedback from clinical and administrative teams was used iteratively during implementation to refine workflows; however, no formal qualitative data collection or structured qualitative analysis was undertaken as part of this study.
Analysis
Data were extracted from the database records and analyzed using descriptive statistics, including means and standard deviations, to provide overviews of patient numbers and waiting times. The proportion of patients on waiting lists, verified as eligible for surgery, and referred was calculated using percentages. Data normality was assessed using the Kolmogorov-Smirnov test. Paired t-tests were applied to assess the significance of any differences observed with a significance of p<0.05. All calculations were performed in IBM SPSS Statistics Version 29.
Ethical Considerations
This study was conducted in accordance with ethical guidelines for quality improvement initiatives in healthcare. Patient confidentiality was maintained by anonymizing all data before analysis, and access to patient records was restricted to authorized personnel only. No conflicts of interest were reported by the study team.
Results
Over an eight-month period from January 2024 to August 2024, there was a 51.74% reduction in the absolute number of patients on the surgical waiting list. The total number of patients decreased from 9,597 in January to 4,755 by July 2024. This was further reduced to 4,627 by August 2024 following patient referrals, resulting in an overall reduction of 51.74%. Among the surgical specialties, Ophthalmology achieved the greatest reduction in waiting list size at 69.40%, followed by Urology at 59.23% and Orthopedics at 58.59%. These reductions across departments are summarized in Figure 1. The average percentage reduction across all departments was 51.9%. A Shapiro-Wilk test indicated that the differences between the baseline recorded waiting list and verified waiting list numbers were not significantly different from a normal distribution (p = 0.164). A paired-samples t-test was conducted to evaluate the intervention’s impact. There was a statistically significant difference between the average number of patients on departmental waiting lists before and after the intervention (p-value = 0.018), as shown in Table 1. Absolute numbers of patients on the waiting list before and after the intervention (Jan 2024 vs Aug 2024), by department Elective Surgical Waiting List Outcomes by Department Before and After Verification and Onward Referral (Jan–Aug 2024) *Verified active waiting list: patients confirmed eligible and still awaiting elective surgery at SMC after validation.
Mean Waiting Time to Surgery by Department Before and After the QIP Interventions (Jan–Aug 2024)
*Days were calculated using a conversion of 28.25 days per month.
**There was an increase in the average waiting times post-intervention for Vascular surgery.
Theatre utilization also improved significantly over the same period. In the main operating theatre (MOT), the mean monthly case volume increased from 1,469 (SD 139.3) in 2023 to 1,608 (SD 94.4) in 2024 (p < 0.001). Endoscopy activity also increased, as the number of cases rose from 294 in January 2023 to 326 in January 2024, and the mean monthly endoscopy caseload increased from 256 (SD 54.3) in 2023 to 317 (SD 33.4) in 2024 (p < 0.001). These changes are shown in Figure 2. Mean monthly procedural volume in the Main Operating Theatre (MOT) and Endoscopy theatres (ENDO) before and after the intervention, comparing January–October 2023 (pre-intervention) with January–October 2024 (post-intervention)
Discussion
This study demonstrates the impact of implementing a centralized Electronic Patient Record (EPR) system on surgical waiting list management at SMC. The intervention led to a 51.7% reduction in the total number of patients on the waiting list, an average decrease of 158 days in waiting times, and a significant improvement in operating theatre utilization. The reduction reflects both verification (removal of ineligible or duplicated entries) and improved scheduling capacity facilitated by centralization and referral pathways. These outcomes underscore how centralized, data-driven solutions can address longstanding inefficiencies in surgical scheduling and resource coordination, particularly when healthcare systems are under strain, such as during the aftermath of the COVID-19 pandemic.11,17
The 50% reduction target in surgical waiting lists was based on international benchmarks. The SIGLIC program in Portugal achieved a 60% reduction through centralized scheduling, while Rathnayake et al. reported reductions of 25%–65% across multiple systems using prioritization tools. These precedents informed our goal as both realistic and evidence-based, particularly in addressing post-pandemic backlogs at SMC.11,18
By replacing a previously fragmented and manual system with a streamlined EPR, the project enhanced interdepartmental communication and scheduling accuracy, enabling timely and equitable access to care. The availability of real-time patient data facilitated dynamic prioritization of cases and improved alignment of surgical teams and theatre resources, thereby minimizing idle time and reducing cancellations. 19 These results are consistent with international experiences, such as the SIGLIC program in Portugal and the SWALIS project in Italy, which similarly demonstrated that centralized systems can dramatically improve access to surgery and reduce waiting times through integrated prioritization models.18-20
The QIP was structured using the Plan-Do-Check-Act (PDCA) framework. 21 In Phase 1, the team transitioned from decentralized Excel and paper-based records to a computerized data storage system, laying the groundwork for real-time surgical scheduling. Phase 2 focused on validating the waiting list post-pandemic, with consultant-led reviews ensuring accurate updates on patient status. Finally, Phase 3 involved integrating the validated patient records into the EPR system, allowing automated prioritization and scheduling. This iterative and structured approach enabled both immediate improvements and long-term sustainability. 22
Variations in effectiveness across specialties highlight the importance of tailoring interventions. Specialties such as Ophthalmology and Urology achieved the highest reductions, primarily due to the prevalence of standardized, short-duration procedures like cataract surgeries and transurethral resections of prostate (TURPs), which are well-suited to centralized scheduling. Conversely, specialties like Plastic Surgery and Bariatrics faced slower progress due to resource constraints, longer case durations, and the need for preoperative optimization. 23 Furthermore, the absence of improvement in vascular surgery waiting times likely reflects the relatively low baseline waiting time in this group (2.6 months), relatively small case numbers (96 patients), and a higher burden of urgent and emergency cases, which may have reduced the measurable impact of the intervention on average waiting time.
The increase in surgical activity, particularly in high-volume specialties such as Ophthalmology, occurred without a proportional increase in infrastructure or workforce during the study period. This suggests that existing resources were previously underutilized or suboptimally coordinated. Centralized scheduling, improved data visibility, and dynamic prioritization enabled more efficient use of theatre capacity, reduced idle time, and minimized cancellations. Operational adaptations included improved allocation of theatre slots, redistribution of cases across surgical lists, and better alignment between surgical teams and available resources. These findings demonstrate that substantial efficiency gains can be achieved through organizational redesign alone, without immediate expansion of capacity.
This study faced some limitations. First, the absence of pre-pandemic baseline data limited direct comparison to earlier system performance. As a single-center quality improvement project, the findings may not be directly applicable to some tertiary-care settings with different infrastructure and digital capacity. Additionally, variation in baseline data gathering across departments may introduce inconsistency in outcomes. There is also potential for verification bias during the patient contact phase of the QIP, although consultant oversight and data triangulation minimized this risk. Furthermore, this study did not include a formal measurement of patient-reported experience or satisfaction. While reduced waiting times are likely to improve patient experience, future studies should incorporate validated patient-reported outcome and experience measures to ensure that operational improvements translate into meaningful patient-centered benefits.
Looking ahead, future iterations of the Quality Improvement Project should focus on tailoring interventions to the unique operational realities of each specialty, while leveraging emerging tools and strategies to enhance efficiency and equity. Integrating predictive analytics into the Electronic Patient Record (EPR) system enables better forecasting of demand, facilitating more dynamic and proactive scheduling.24,25
High-burden departments such as ENT and Ophthalmology, which demonstrated significant backlog reductions, may benefit from additional theatre allocations and targeted workforce support to maintain progress. For resource-intensive procedures like bariatric and orthopedic surgeries, dedicated theatre slots and specialized teams could streamline care and minimize delays. Moreover, implementing multi-attribute prioritization frameworks that incorporate clinical urgency, socio-economic factors, and patient-reported outcomes would ensure more transparent, equitable, and patient-centered decision-making. These tools, which have proven adaptable during crises like the COVID-19 pandemic, offer a sustainable path forward in refining prioritization practices.26,27
The observed reduction in waiting time by an average of 158 days represents not only a clinical improvement but also a significant economic benefit. Prolonged waiting for elective surgery is associated with increased healthcare utilization, deterioration in clinical condition, and indirect societal costs such as loss of productivity. Although a formal economic evaluation was beyond the scope of this study, the magnitude of delay reduction suggests meaningful cost avoidance for both patients and the healthcare system. Future work should seek to quantify these benefits using cost-of-delay models and health economic analyses.
Regular system audits are essential to maintaining efficiency and ensuring adherence to protocols across all departments. Further investigation is also warranted to understand the drivers behind variation in department-level outcomes and to identify opportunities for cross-specialty learning. Lastly, future work should explore the impact of waiting list management improvements on patient satisfaction and clinical outcomes, using validated patient-reported experience and outcome measures to inform ongoing refinements and sustain patient-centered care delivery.
Conclusion
This Quality Improvement Project demonstrates that implementation of a centralized, EPR-integrated system for surgical waiting list management can significantly reduce elective surgery backlogs, shorten waiting times, and improve resource utilization. Beyond achieving operational targets, the intervention illustrates how system redesign can deliver broader value across clinical, economic, and patient-centered domains. This scalable model provides a practical framework for healthcare systems addressing post-pandemic backlogs and highlights the importance of data-driven, coordinated approaches to sustainable service improvement.
Footnotes
Ethical Considerations
This study was conducted in accordance with ethical guidelines for quality improvement initiatives in healthcare. Patient confidentiality was maintained by anonymizing all data before analysis, and access to patient records was restricted to authorized personnel only. No conflicts of interest were reported by the study team. The project was reviewed by the Salmaniya Medical Complex Institutional Review Board and was deemed exempt from formal ethical approval.
Consent to Participate
Formal written informed consent was not obtained. During waiting list validation, patients contacted by telephone were informed that the call was part of waiting list updating and service improvement, and their information was updated based on their verbal agreement and absence of objection.
Author Contributions
RMD contributed to the conceptualization of the project, design, data collection, validation, initial drafting of the manuscript, and revision. MA contributed to the conceptualization of the project, design, data collection, validation, initial drafting of the manuscript, and revision. ZJ contributed to conceptualization, day-to-day implementation of the intervention, data collection, validation, and final revision of the manuscript. SF led data analysis, interpretation of findings, drafting of the initial draft, and final revision of the manuscript. JK contributed to project oversight and supervision, conceptualization of the work, interpretation of findings, revision of the manuscript, and final approval. All authors have revised and approved the manuscript submitted. RMD acts as the guarantor for this project.
Authors RMD and MA contributed equally and are joint first authors.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.