Decreasing patient appointment waiting days for ultrasound diagnosis in Saint Peter Specialized Hospital: a quality improvement project.

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Learn more: PMC Disclaimer | PMC Copyright Notice BMJ Open Qual . 2026 Jun 3;15(2):e003388. doi: 10.1136/bmjoq-2025-003388 Decreasing patient appointment waiting days for ultrasound diagnosis in Saint Peter Specialized Hospital: a quality improvement project Dawit Dagne Moges 1 , Dawit Dereje Leuleberehan 1 , Zenebe Shumeye Dilo 1 , Assefa Tesfaye Menerba 1 , Ibrahim Heyredin Awoll 1 , Biniam Yohannes Wotango 2, ✉ Author information Article notes Copyright and License information 1 Saint Peter Specialized Hospital, Addis Ababa, Ethiopia 2 Gandhi Memorial Hospital, Addis Ababa, Ethiopia ✉ Biniam Yohannes Wotango; biniamy50@gmail.com None declared. Received 2025 Feb 25; Accepted 2026 May 20; Collection date 2026. Copyright © Author(s) (or their employer(s)) 2026. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: https://creativecommons.org/licenses/by-nc/4.0/ . PMC Copyright notice PMCID: PMC13239432  PMID: 42235992 Abstract Background Ultrasound examination turnaround time refers to the duration from when a clinician orders the test to when results are reported. In many public hospitals, limited workforce capacity, inefficient workflows and unstructured scheduling contribute to prolonged waiting times for diagnostic imaging. Timely ultrasound diagnosis is essential for appropriate clinical decision-making and patient flow. This project aimed to reduce excessive waiting times for ultrasound appointments in a high-volume public hospital Methods A Quality Improvement (QI) project was conducted in the Radiology Department of Saint Peter Specialized Hospital between August and November 2022. The aim was to reduce the median waiting time for ultrasound appointments from 28 days to less than 5 days. A root cause analysis (RCA) identified key contributors, including delayed start times, absence of structured scheduling, workflow interruptions and limited machine availability. Driver diagrams and prioritisation matrices informed intervention selection and tested through sequential Plan–Do–Study–Act (PDSA) cycles. Interventions included workflow optimisation, extended effective working hours, productivity enhancement, structured appointment scheduling and deployment of point-of-care ultrasound for emergency cases. Results The maximum waiting time for senior radiologist ultrasound appointments decreased from 28 days to ≤5 working days. The backlog for technologist-led examinations was eliminated. Daily productivity increased from 10 to 48 scans per radiologist and from 18 to 65 scans per technologist. Run charts demonstrated progressive and sustained improvement across four PDSA cycles. Conclusion and recommendations The QI project achieved significant reductions in ultrasound waiting times through organisational and workflow redesign without additional funding. Sustainability relied on staff engagement, leadership support and embedding new practices into routine operations. These low-cost, context-appropriate strategies may be applicable to other resource-constrained hospitals facing similar challenges. Keywords: RADIOLOGY, Quality improvement, Waiting Lists, Continuous quality improvement WHAT IS ALREADY KNOWN ON THIS TOPIC Overcrowded waiting areas in public hospitals lead to long waiting times for ultrasounds, delaying diagnosis and patient care. Efficient scheduling and resource management are crucial to reducing waiting times and improving service delivery. WHAT THIS STUDY ADDS Workflow optimisation, structured scheduling, productivity enhancement and strategic use of point-of-care ultrasound reduced ultrasound waiting times from 28 days to ≤5 days and eliminated appointment backlogs in a high-volume public hospital. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY The study highlights that optimising ultrasound scheduling and resource use can drastically reduce waiting times and improve service delivery. These strategies could be adopted in other hospitals, especially in low-resource settings, with ongoing monitoring and staff engagement to maintain improvements. Introduction Problem description Prolonged waiting times for ultrasound examinations are a common challenge in public hospitals and contribute to delayed diagnosis, prolonged hospital stays, patient dissatisfaction and suboptimal clinical outcomes. In diagnostic imaging services, inefficiencies in workflow and scheduling can exacerbate the mismatch between demand and available capacity. 1 At Saint Peter Specialized Hospital, the backlog of ultrasound appointments exceeded 28 days in early 2022, resulting in overcrowded waiting areas and delayed diagnoses. Project context Saint Peter Specialized Hospital is a public hospital serving a large urban and rural population in Ethiopia. During the project period, the radiology department operated with three radiologists, two ultrasound technologists and two functional ultrasound machines, serving both outpatient and inpatient populations, including emergency referrals. Prior to the intervention, there was no structured appointment system; patients queued physically, staff start times were inconsistent and emergency cases frequently displaced scheduled patients. As a result, effective operational hours averaged only 4 hours per day despite nominal full-day staffing. Problem statement From March 2022 to June 2022, radiology department reports indicated that patients were scheduled for ultrasound examinations with an average waiting period of over 28 days. The prolonged appointment time negatively impacted timely diagnosis and treatment, deemed unacceptable for timely care, necessitating a structured QI intervention. Aim statement The project aimed to reduce ultrasound waiting times from a median of 28 days to less than 5 days within 4 months (August–November 2022). Assessment of problem and analysis of causes A root cause analysis (RCA) was conducted through multidisciplinary meetings involving radiologists, technologists, clerks and hospital management. The key contributors identified were the following: Workforce capacity: Three radiologists and two technologists were responsible for more than 100 daily ultrasound requests. Operational inefficiency: Work frequently started after 10:00 AM, with frequent interruptions and average effective working time limited to approximately 4 hours per day. Absence of structured scheduling: Patients queued physically, leading to duplication, inequitable access and inefficient use of staff time. Resource bottlenecks: Only two functional ultrasound machines were available. Emergency disruptions: Urgent inpatient and emergency department cases routinely displaced scheduled outpatient examinations. A driver diagram ( figure 1 ) was developed to map these root causes and guide intervention selection. Figure 1. Driver diagram showing mapped root causes and guided selection of interventions. The driver diagram in figure 1 showed that the mapped root cause and interventions guided by each of the mapped root causes. ER, emergency department; ICU, intensive care unit; U/S, ultrasound. Open in a new tab Interventions Workflow optimisation A clear duty roster was introduced to ensure a standardised start time of 8:00 AM. Supervisors monitored adherence, and peer accountability was encouraged through regular team meetings. Mid-day interruptions were minimised, increasing effective operational hours from 4 to 7 hours per day. Productivity enhancement Standardised ultrasound examination protocols were introduced to reduce variability in scan duration. Clerks prepared patient documentation in advance to minimise downtime between scans. Staff were rotated between high-volume and complex cases to balance workload and reduce fatigue. Onsite ultrasound for emergency cases (POCUS) A portable ultrasound machine was assigned to the intensive care unit and emergency department. This intervention prevented urgent cases from interrupting scheduled outpatient services in the main radiology unit. Scheduling system The existing open queuing system was replaced with a structured, time-slot-based booking register. Patients were assigned specific appointment times, reducing overcrowding, duplication and inequitable access. Although paper-based, the system functioned similarly to simplified appointment scheduling models suited to low-resource settings. Intervention design Using the model for improvement, four sequential PDSA cycles were conducted: Cycle 1 tested punctual start times; Cycle 2 piloted extended effective working hours; Cycle 3 implemented point-of-care ultrasound (POCUS) for emergency cases; and Cycle 4 refined and stabilised the structured scheduling system. Measures Outcome measure: Median waiting time for ultrasound appointments (days). Process measures: Number of examinations performed per staff member per day and total daily operational hours. Balancing measure: Staff experience and workload, assessed qualitatively through regular team discussions. Methods of data collection and analysis Daily monitoring logs were maintained by clerks and validated by supervisors. Waiting times were plotted in run charts to track improvement over time ( figure 2 ). Figure 2. Run chart showing waiting days for ultrasound in St. Peter Specialized Hospital. The run chart in figure 2 showed how focused improvement actions, better use of working hours, productivity gains, onsite ultrasound for ER/IPD and regular follow-up drove a sustained shift from a 30-day backlog to near-zero waiting times, reducing delays by over 90%. ER, emergency department; IPD, inpatient department; U/S, ultrasound. Open in a new tab Results Operational hours increased from 4 to 7 hours per day. Radiologist productivity increased from an average of 10 to 48 scans per day, while technologist productivity increased from 18 to 65 scans per day. Median waiting time decreased progressively across PDSA cycles, from 28 days at baseline to ≤5 days by Cycle 3 and was maintained through Cycle 4. The appointment backlog was reduced from approximately 380 patients to fewer than 80 by the end of the project period. Lessons learned Transparent communication and regular feedback meetings were critical to building trust and engagement. Staff initially expressed concern about longer working hours and increased throughput; however, resistance diminished as improvements in patient flow and reduced overcrowding became evident. Motivation was non-financial and relied on recognition, shared ownership of results and visible patient benefit. Continuous monitoring reinforced accountability and supported sustained adherence to new practices. Limitations This project had a relatively short follow-up period of 4 months, limiting assessment of long-term sustainability. Formal quantitative measurement of staff satisfaction was not conducted. While no external funding was received, this did not affect the implementation of core interventions but limited rapid expansion of equipment capacity. Sustainability Improvements were embedded into routine departmental operations through standardised duty rosters, structured scheduling practices and continued use of POCUS for emergency cases. Leadership involvement and peer accountability supported adherence. Sustaining gains in this low-resource setting will require institutionalisation of scheduling practices, continued staff recognition and incremental investment in additional ultrasound capacity. Conclusion Through structured root cause analysis, iterative PDSA cycles and workflow redesign, ultrasound waiting times were reduced from 28 days to ≤5 days without additional funding. This project demonstrates that meaningful improvements in diagnostic service delivery are achievable in resource-limited settings through staff engagement and organisational change. Discussion This quality improvement project demonstrated that substantial reductions in ultrasound appointment waiting time can be achieved through organisational and process-level interventions, even in a resource-constrained public hospital. Reducing the median waiting time from 28 days to ≤5 days, increasing daily scan productivity and shrinking the backlog from 380 to 80 patients underscore how workflow redesign, scheduling discipline and staff engagement can unlock latent system capacity without major financial investment. These findings align with a growing body of evidence suggesting that inefficiencies in diagnostic services are often driven more by process failures than by absolute shortages of equipment or personnel. 1 2 Similar improvements have been reported in recent studies. Zhang et al 3 described a patient-centred, real-time dynamic ultrasound appointment system that reduced waiting times by 78%–90% while improving resource utilisation and reducing staffing pressure. 3 Although our project did not use advanced digital scheduling software, the introduction of structured appointment slots and elimination of physical queuing produced comparable effects, highlighting that even low-technology solutions can yield meaningful gains when thoughtfully implemented. Other studies from middle-income settings have likewise shown that simple appointment systems and workload levelling can significantly reduce diagnostic delays and overcrowding. 4 The use of POCUS for emergency and inpatient cases was another critical contributor to success. By diverting urgent cases away from the main ultrasound schedule, service disruptions were minimised, and planned appointments could proceed as scheduled. Evidence supporting the role of POCUS in low- and middle-income countries (LMICs) is expanding. Pathak et al 5 demonstrated that clinicians trained through a blended learning POCUS programme in Ghana used ultrasound frequently in routine practice, often providing the only available imaging for underserved patients. 5 Similar findings from sub-Saharan Africa and South Asia indicate that POCUS can improve diagnostic timeliness, reduce unnecessary referrals and enhance patient flow when integrated into routine care pathways. 6 Beyond technical interventions, this project highlights the importance of staff engagement and process ownership. Standardising start times, enforcing duty rosters and reorganising task flows parallel observations from studies conducted during the COVID-19 period, where efficient scheduling and attendance systems improved productivity with minimal technological input. 7 However, sustaining such gains remains challenging. While short POCUS training courses and workflow interventions have been shown to improve skills, confidence and short-term performance, 8 evidence on long-term sustainability is limited. 9 Embedding new practices into routine operations, ensuring visible leadership commitment, maintaining feedback mechanisms and gradually expanding resources appear essential for sustaining improvements. Collectively, these findings suggest that pragmatic, context-sensitive quality improvement strategies can meaningfully reduce diagnostic waiting times and may be transferable to other hospitals facing similar constraints, provided local adaptation and continuous monitoring are prioritised. Acknowledgements We would like to thank all radiology department staff and stakeholders involved in the project. Footnotes Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors. Data availability free text: The datasets used and/or analysed during this quality improvement are available from the corresponding author on reasonable request. Patient consent for publication: Not applicable. Ethics approval: Not applicable. Provenance and peer review: Not commissioned; externally peer reviewed. Patient and public involvement: Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details. Data availability statement Data are available upon reasonable request. References 1. Groome LJ, Mayeaux EJ. Decreasing extremes in patient waiting time. Qual Manag Health Care. 2010;19:117–28. doi: 10.1097/QMH.0b013e3181dafeac. [ DOI ] [ PubMed ] [ Google Scholar ] 2. Langley GJ, Moen RD, Nolan KM, et al. The improvement guide: a practical approach to enhancing organizational performance. 2nd. San Francisco: Jossey-Bass; 2009. edn. [ Google Scholar ] 3. Zhang Y, Luo Y, Qiu L, et al. Design of an ultrasound appointment system based on a patient-centered real-time dynamic resource allocation strategy. Front Public Health. 2025;13:1496860. doi: 10.3389/fpubh.2025.1496860. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Bleustein C, Rothschild DB, Valen A, et al. Wait times, patient satisfaction scores, and the perception of care. Am J Manag Care. 2014;20:393–400. [ PubMed ] [ Google Scholar ] 5. Pathak A, Limbani F, Awuku YA, et al. Physicians’ clinical experience and perspectives following a pilot, blended learning, point of care ultrasound course in Ghana- a mixed methods analysis. BMC Med Educ. 2024;24:1415. doi: 10.1186/s12909-024-06250-z. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Steinmetz JP, Berger JP. Ultrasonography as a diagnostic tool in resource-limited settings: a systematic review. Trop Med Int Health. 2020;25:731–42. [ Google Scholar ] 7. Zhang Y, Luo Y, Ling W, et al. Efficient scheduling and attendance system for the ultrasound department under demand uncertainty during COVID-19. Health Informatics J. 2023;29:14604582231213424. doi: 10.1177/14604582231213424. [ DOI ] [ PubMed ] [ Google Scholar ] 8. Al-Absi DT, Simsekler MCE, Omar MA, et al. Evaluation of point-of-care ultrasound training among healthcare providers: a pilot study. Ultrasound J. 2024;16:12. doi: 10.1186/s13089-023-00350-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Reed JE, Card AJ. The problem with Plan-Do-Study-Act cycles. BMJ Qual Saf. 2016;25:147–52. doi: 10.1136/bmjqs-2015-005076. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement Data are available upon reasonable request. 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