Abstract
Background:
The process for kidney transplant listing is often lengthy and fragmented, contributing to delayed access to transplantation.
Objective:
At our center, we aimed to reduce time to listing by 25% within 12 months through a quality improvement (QI) initiative.
Design:
We conducted a mixed-method QI study.
Setting:
Tertiary care academic transplant center.
Participants:
Adult patients listed for transplantation from January 1, 2019, to July 31, 2023.
Methods:
Quantitative data were collected through chart review and qualitative data were gathered from semi-structured interviews with health care providers. Outcome measure was time from evaluation start to transplant listing and process measures included time to obtaining specific consultations and tests. Findings informed a multifaceted intervention, which included (1) improved documentation guidelines, (2) expedited access for delayed investigations, (3) a dedicated transplant nurse for Indigenous patients, and (4) an informatic-enabled coordination tool. Outcomes were then compared to those of patients listed from January 1 to July 31, 2024.
Results:
Among 109 patients in the preintervention cohort, the median time from evaluation to listing was 437 days, with only 9 patients listed predialysis. Indigenous patients, who represent over 25% of our population, accounted for only 11% of those listed. Key delays were identified in cardiology testing, colonoscopies, and mammograms. Ten health care providers were interviewed, and the main themes identified were: lack of resources, confusing task responsibilities, coordination of informatic systems and inequities affecting Indigenous patients. Following preliminary interventions, 22 patients were listed for transplant in just over 6 months—nearly one fifth of the total listed over the prior 4 years—demonstrating a marked acceleration in the listing process. Furthermore, median time to listing improved by 17% to 362 days, with a higher proportion of Indigenous patients (23%) listed and modest reductions in cardiology-related delays. Plan-Do-Study-Act cycles continue to optimize these interventions.
Limitations:
Conducted at a single center which limits the generalizability of findings to other health care centers. Furthermore, the timeline included the COVID-19 pandemic, which may have caused delays and influenced results independent of the QI initiative.
Conclusions:
A multifaceted intervention addressing local challenges showed early signs of success in reducing time to transplant listing and improving access for Indigenous patients. This highlights the critical role of data-driven QI initiatives in optimizing processes and improving patient care.
Introduction
Kidney transplantation is widely regarded as the optimal treatment for patients with end-stage kidney disease (ESKD), offering significant improvements in survival, quality of life, and cost-effectiveness compared to chronic dialysis.1-4 Despite these well-documented benefits, the pathway to transplantation is often marked by challenges that can impede timely access for many eligible patients. These challenges highlight the importance of implementing interventions aimed at improving the efficiency of the transplant evaluation and listing process.
The evaluation process for kidney transplantation is multifaceted, involving medical, psychosocial, and logistical assessments that require coordination among various health care providers and systems. In Quebec, wait time is a key factor in the deceased donor allocation policy. 5 Therefore, timeliness in the transplant workup is essential to provide optimal outcomes. Potential candidates often face bottlenecks at multiple stages, including delays in referrals, prolonged diagnostic workups, and inefficiencies in communication between health care teams. 6 In addition, systemic barriers such as socioeconomic disparities, geographic variability in access to transplant centers, limited hospital capacity for diagnostic tests, and implicit biases further exacerbate delays, disproportionately affecting vulnerable populations.7-10
These barriers, though complex, present opportunities for targeted interventions. Quality improvement (QI) initiatives offer a structured approach to improving processes and outcomes. 11 By using data-driven strategies and engaging stakeholders, QI methodologies can identify root causes of inefficiencies and implement targeted interventions to improve the transplant evaluation process.
This paper explores a QI initiative developed at a large tertiary academic center designed to improve the time to kidney transplant listing by addressing key barriers within the local evaluation process. Our goal was to improve time to listing by developing a multifaceted intervention addressing key challenges and bottlenecks in the listing process.
Methods
Aim Statement
Our aim was to create a multifaceted targeted intervention addressing barriers in our center’s pretransplant process to reduce time to transplant listing by 25% within 12 months.
Study Design
This study employed a mixed-methods approach, combining quantitative chart review with qualitative semi-structured interviews to assess the kidney transplant evaluation process at our tertiary academic center. Data collection was conducted across referring centers within the McGill University Health Center (MUHC), including Indigenous communities served by our center.
The quantitative component included a retrospective chart review of all patients, including those with living donors, referred from our academic center who were listed for kidney transplantation between January 1, 2019, and July 31, 2023. The qualitative component involved interviews with health care practitioners directly involved in the transplant evaluation process to identify bottlenecks and areas for improvement.
This study was conducted within a formal QI framework, adhering to SQUIRE 2.0 guidelines. 12 We engaged key stakeholders to identify shared goals, address concerns, and align priorities. Data analysis guided the implementation strategy, with interventions developed targeting areas demonstrating the longest delays. Key stakeholders were consulted both to ensure alignment with identified needs and to assess feasibility within the local context, thereby enhancing the likelihood of meaningful and sustainable improvement. Subsequent quantitative data collection allowed for iterative refinement of our multifaceted intervention through Plan-Do-Study-Act (PDSA) cycles.
Measures
Our primary outcome measure was the time from the start of evaluation to transplant listing. Process measures included (1) time from the start of evaluation to completing specific consultations and tests, (2) time from the start of evaluation to medical assessment by the transplant team, and (3) time from the start of evaluation to kidney transplantation. Balancing measures included (1) qualitative feedback from health care professionals and patients, and (2) racial equity in access to transplant listing.
A multifaceted intervention was designed using insights from both quantitative and qualitative data. We then compared baseline data to the period between January 1, 2024, and July 31, 2024, to assess the preliminary impact of our interventions.
Data Collection
Quantitative data
We conducted a chart review of patients listed for kidney transplantation between January 1, 2019, and July 31, 2023. Patient characteristics included: age, sex, race, comorbidities, blood type, body mass index (BMI), and calculated panel reactive antibody (cPRA) score. To address potential racial disparities, we identified whether patients were from an Indigenous community.
Additional data included details about the referring team and key time points in the pretransplant process, such as the start of transplant evaluation (defined as the date the transplant clinic received the consultation), the date of the transplant clinic visit, the listing date, and, if applicable, the transplant date. Dates for specific pretransplant tests and consultations were also collected.
To evaluate the impact of interventions implemented, we conducted additional chart reviews for patients listed between January 1 and July 31, 2024, and compared these outcomes with the preintervention cohort. To note, as this was a QI project, several interventions were initiated in the preceding years in order to try and improve the time to listing.
Qualitative data
Between January and March 2023, semi-structured interviews were conducted with ten health care practitioners involved in the care of patients with kidney disease, including six nurses and four nephrologists specializing in transplant and/or chronic kidney disease. They were asked three primary questions: (1) “How do you perceive the current transplant evaluation process?,” (2) “What challenges do you encounter with this process?,” and (3) “What improvements would you suggest?.” Interview recordings were transcribed, anonymized and analyzed using thematic analysis.
Data Analysis
Quantitative data analysis included a descriptive summary of baseline demographic and clinical characteristics, presented as mean values with standard deviation, medians with interquartile ranges or proportions, as appropriate. Median time (in days) was calculated for all time-related variables. Qualitative data were analyzed using thematic analysis with an iterative, inductive approach, with the analysis supported by NVivo (Version 14).
Ethical Considerations
An exemption from Research Ethics Board (REB) review was granted by the McGill University Health Center’s Center for Applied Ethics due to the study’s QI focus.
Results
Baseline Characteristics
Between January 1, 2019, and July 31, 2023, 113 patients referred from our academic center were listed for kidney transplantation. Four patients were excluded due to substantial missing data. Of the included 109 patients (Table 1), median age was 56 years (IQR 42-66), with 70% (n=76) of patients being male. The most prevalent comorbidity was diabetes (31%), followed by coronary artery disease (16%), vascular disease (7%), and congestive heart failure (5%). Median BMI was 26 kg/m² (IQR 23-30). The most common blood types were O+ (45%) and A+ (28%).
Baseline Characteristics.
Only 8% of patients (n=9) were listed before initiating dialysis; median dialysis vintage at time of listing was 430 days (IQR 198-904). Notably, only 11% of listed patients (n=12) were from Indigenous communities, despite these communities representing >25% of our ESKD population, highlighting a significant health disparity.
Measures
Outcome measure—time to listing
Median time from start of evaluation to transplant listing for all patients was 432 days (IQR 203-640) (Figure 1). Differences were observed between referring centers (center 1:448 days, center 2: 428 days, center 3: 254 days, and Indigenous communities: 450 days). Patients on peritoneal dialysis had the lowest median time to listing of 370 days (n=26), compared to patients on hemodialysis (443 days, n=74) and patients in the predialysis clinic (428 days, n=9). The number of comorbidities was positively correlated with time to listing; patients with no comorbidities had a median time of 403 days, while those with one, two, and three comorbidities had median times of 439, 771, and 888 days, respectively.
Outcome and key process measures.
Process measures
Median time from listing to kidney transplantation was 137 days (IQR 63-360), and median total time from evaluation start to transplantation was 639 days (IQR 430-1015). Furthermore, time from start of evaluation to initial consultation with transplant team was 140 days (IQR 61-363).
We also assessed the median time required to complete various tests as part of the transplant evaluation process, from evaluation start to the date of the test. It is important to note that some of these tests may not have been ordered at the start of the evaluation but rather at different points during the listing process. For cancer screening, the median times were 251 days (IQR 76-494) for colonoscopy and 336 days (IQR 159-841) for mammography. Additional imaging tests showed delays, with chest CT taking a median of 316 days (IQR 240-570), abdominal-pelvic CT 318 days (IQR 240-570), and abdominal-pelvic ultrasound taking 195 days (IQR 64-583). Cardiology-related investigations also demonstrated delays, with median times of 142 days (IQR 56-312) for transthoracic echocardiography, 274 days (IQR 80-668) for myocardial perfusion imaging, and 372 days (IQR 166-420) for cardiology consultation. For tuberculosis screening, obtaining a purified protein derivative skin test took a median of 99 days (IQR 30-187). Among the 2 patients referred for the bariatric surgery, initial consultation took a median of 156 days, and one patient proceeded to sleeve gastrectomy after 403 days.
Qualitative Data
Thematic analysis identified four primary themes (Figure 2). The first theme centered on the lack of resources, encompassing both human resources, such as coordinators, and non-human resources, such as access to diagnostic tests. The second theme highlighted the ambiguity in task responsibility, with no clear oversight of appointments and tests required for transplant evaluation. The third theme emphasized the inefficiencies in system coordination, caused by fragmented and parallel systems that included both digital and non-digital processes. The fourth theme addressed barriers faced by Indigenous patients, including issues related to racism, limited access to local services, and the need to travel for specialized tests. Secondary themes included the prolonged duration of the process, which discourages patients and health care providers, and the need for better patient education to navigate the evaluation requirements effectively.
Thematic analysis of health care provider interviews.
Development of a Multifaceted Intervention
We implemented a range of interventions throughout the data collection process and following data analysis (Figure 3). Notably, some interventions were already set in motion before we finalized our data collection, as key bottlenecks had already been identified. We started by establishing clear documentation guidelines to differentiate between baseline testing and any additional required assessments. Concurrently, a rapid-access cardiology pathway was implemented to ensure timely cardiac evaluations for transplant workup. An engaged cardiologist was identified who prioritized patients requiring cardiac clearance, ensuring appointments within one month. Consults in the context of transplant assessment were routed directly to the cardiologist’s office, establishing a streamlined process that facilitated rapid and coordinated care. Similarly, discussions with the gastrointestinal division were held to expedite the colonoscopy process, ensuring that requests arising in context of transplant evaluation were prioritized. Furthermore, regular quarterly meetings between the transplant team and each referring center were reinforced to ensure alignment and provide ongoing communication and updates. A dedicated transplant nursing coordinator, appointed by the Indigenous health authority, was also assigned to patients from Indigenous communities to help streamline testing and coordinate care, minimizing the frequency of visits outside their communities. Finally, our informatics specialist developed a user-friendly, fillable document to streamline the ordering of required consults and tests, while also integrating the transplant workup into our clinic and dialysis electronic medical record (EMR).
Multifaceted intervention.
Preliminary postintervention results
From January 1 to July 31, 2024, 22 patients were listed for transplant over just six months, compared with 109 patients listed over the preceding five years, highlighting a substantial acceleration in the listing process (Table 2). The median time from evaluation start to listing was 362 days (IQR 230-778), reflecting a 17% improvement. While this falls short of our 25% target, ongoing refinements and iterative PDSA cycles continue to drive further progress. Encouragingly, racial disparities narrowed, with 23% of listed patients being Indigenous (n=5). However, only a small subset of patients (n=2, 9%) were listed before dialysis initiation, highlighting an area for further improvement. Furthermore, cardiology investigation times improved only slightly, with a median time to myocardial perfusion imaging of 244 days (IQR 178-278) and cardiology consultation of 311 days (IQR 122-846).
Comparison of Measures Prior and After the Implementation of the Multifaceted Intervention.
IQR = interquartile range.
Discussion
In this study, we implemented a structured, data-driven QI initiative to address inefficiencies in the kidney transplant evaluation process at our center. Through the integration of quantitative and qualitative data, we developed targeted interventions that led to early improvements in median listing time and better representation of Indigenous patients. Although we did not fully achieve our 25% target reduction in listing time, our results represent meaningful progress and support the potential of QI methodologies in addressing both system inefficiencies and health inequities. Ongoing refinements and iterative PDSA cycles continue to drive further progress.
A major strength of our study lies in the combined use of quantitative and qualitative methods to identify inefficiencies and drive improvements in the local transplant listing process. By combining granular data analysis with semi-structured interviews, we created a robust framework to identify and address bottlenecks effectively. As such, our quantitative data revealed specific delays in various pretransplant investigations, such as cardiac testing and cancer screening, significantly contributed to prolonged delays in transplant listing. Moreover, qualitative interviews with health care professionals further identified systemic issues, including limited hospital resources, unclear task delegation, and poor coordination across non-centralized informatics systems. In addition, participants highlighted the multiple barriers faced by Indigenous patients in accessing health care services, particularly those associated to geographic isolation and systemic inequities. This approach emphasized the importance of stakeholder engagement and demonstrated the value of qualitative insights to contextualize quantitative findings.
Several limitations should be acknowledged. First, the potential impact of COVID-19-related delays was not assessed. However, patients whose evaluations began in 2019 (prepandemic) had longer median times to listing than those evaluated during 2020-2021, suggesting that delays at our institution preceded the pandemic. Second, the intervention was evaluated over a short period of six months, although several PDSA cycles had been informally initiated prior to the January 1, 2024, cutoff. Our intent was to underscore the importance of the QI process itself, rather than to focus on outcome data, and to illustrate how data-driven QI initiatives are essential in guiding improvement over time. Third, selection bias is inherent, as only patients who were successfully listed were included, excluding those undergoing workup but not listed. While the QI initiative appeared to improve time to listing, earlier listing could theoretically increase wait time on the transplant list. Nonetheless, preintervention data showed a short median time from listing to transplant (137 days, with 25% within 63 days), suggesting earlier listing remains advantageous. Postintervention data on time to transplant are not yet available, representing an additional limitation. Finally, the absence of patient perspectives limits insight into barriers and facilitators in the listing process. The single-center design, with exclusion of referrals from other centers, also limits generalizability, as local workflows and challenges differ and a locally developed multifaceted QI intervention may not be directly applicable elsewhere.
Nonetheless, the single center nature of this study allowed to focus on addressing bottlenecks that are specific to the unique workflows and resources of our local processes. Of note, a recent randomized clinical trial conducted in Ontario with the goals of improving the completion of key steps in receiving a kidney transplant with a multicomponent intervention unfortunately did not show any benefits. 13 This may have been due to the province-wide nature of the study, including many different transplant programs which operate within their own set of institutional protocols, staffing patterns, patient demographics, and regulatory constraints. These local factors shape the specific challenges and opportunities for improvement, and this may make a single-center approach such as our study necessary to identify and implement tailored solutions.
Moving forward, we are exploring additional strategies to further improve the transplant listing process. Our next steps will focus on sustaining and expanding these improvements through continued PDSA cycles. Improvement strategies include creating an easily accessible, up-to-date common dashboard to centralize critical information, collaborating with radiology to secure dedicated slots for transplant evaluations, and developing a clear, equitable priority system to ensure that patients are seen in the pretransplant clinic in a timely manner. While our findings may not be directly generalizable to other centers, the methodology—anchored in stakeholder engagement, granular data, and iterative testing—offers a replicable model for others seeking to optimize local transplant pathways.
Conclusion
Delays in transplant listing remain a major barrier to kidney transplant, particularly for Indigenous communities. Through data-informed and stakeholder-driven interventions, our QI initiative has shown early improvements in decreasing delays in transplant listing and improving access for Indigenous patients. Continued iteration and equity-focused solutions will be essential in driving sustainable progress.
Footnotes
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported by an internal scholarly award from the McGill University Department of Medicine.
Declaration of Conflicting Interests
The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: This project was supported by an internal scholarly award from the McGill University Department of Medicine. ET receives speaker honorarium from Vantive Inc, is a consultant for Otsuka and receives investigator-initiated funds from Otsuka and GSK Inc, outside of the submitted work. The other authors have no relevant disclosures to declare.
Data Availability Statement
The data underlying this article will be shared on reasonable request to the corresponding author.