Identifying Inequities in Access to Organ Transplantation

Let's improve equity in access to organ transplantation.
person walking in the rain as viewed through a window
Credit Megan Mendenhall. © Duke University, all rights reserved.

The Problem

Although nearly 40,000 organ transplants were performed in 2019, significant disparities remain in the ability of patients from historically marginalized groups to undergo transplantation successfully. This is in part due to the complexity of the transplant selection process. Following referral to a transplant center, patients must navigate a multi step conditional pathway that involves screening to determine a patient’s suitability for evaluation, in-person evaluation via multiple multidisciplinary outpatient visits (e.g., social work, case management, surgery, medicine, psychiatry), diagnostic testing and transplant education, and review of the case by a multidisciplinary committee of transplant clinicians who approve or deny listing for transplant. Patients from historically marginalized groups face unique challenges in completing the process and, as a result, have disproportionately lower rates of accessing the transplant waiting list. 
 
Duke Transplant Center (DTC) rates of listing for transplant among patients referred are estimated at 25%. Reliable quantification of elimination rates at each step of the process is required to fully characterize inequities in access to the transplant waitlist. However, no reliable method exists to monitor patients as they progress through the transplant selection process. 
 
The primary barriers to understanding and advancing equity in transplantation are twofold: limited Social Determinants of Health (SDOH) data is collected on patients referred for transplant, and there is no monitoring of patients during the transplant selection process. We sought to create a custom Electronic Health Record (EHR) query to allow a more accurate assessment of disparities within the selection process, and assess the extent of SDOH data collection among patients referred for transplant. We additionally investigated the DTC culture and capacity to 
change to identify barriers to the implementation of programs designed to improve equity.

Our Solution

To assess health system data infrastructure, we extracted Epic (Epic Systems Corporation, Verona, WI) EHR data on adult (≥18 years old) patient referrals to the Duke Transplant Center (DTC) for kidney or liver transplant from January 1, 2017, to December 31, 2020 (N=7,259). Preliminary extraction and analysis of this referral cohort performed prior to our study exhibited inaccuracies in transplant selection process dates at a rate of >20%. We used Structured Query Language (SQL) queries to obtain patient demographic data, transplant selection process dates, and transplant evaluation notes from the Epic data warehouse known as Clarity. Focusing on initial data querying of selection process dates, we developed five phase definitions for reliable data extraction for our transplant referral cohort (Table 1).
 
We performed three rounds of quality review using the Python programming language to evaluate for completeness, conformance, and plausibility of the demographic and selection process data in our referral cohort. For variables with >5% data missingness, we made code modifications and explored alternative Clarity data sources to ensure thorough and comprehensive inclusion of all available EHR data.
 
We next examined the percentage of inclusion of the 28 PhenX toolkit variables in Epic data collection forms and assessed the missingness of these data for our referral cohort on initial review. Patient-level data elements and most current collection forms were reviewed. Each source’s point of contact was contacted to verify initial findings.
 
Finally, we performed a qualitative organizational assessment of the Duke Transplant Center to assess the current culture, capacity, and readiness of the organization to accept equity-focused interventions. We performed fourteen preliminary stakeholder interviews with abdominal transplant coordinators, surgeons, transplant nephrologists, transplant hepatologists, a pharmacist, referral/intake specialists, and a financial coordinator regarding their perceptions of transplant equity, barriers to patient success, and challenges facing the DTC.

 

Phase

Name

Definition

I

Referral / Screening

Date corresponding to the receipt of referral by the Duke Transplant Center and initiation of EHR documentation by transplant coordinator

II

Evaluation

Date corresponding to the first visit either to a transplant specialist (e.g., surgery, nephrology, cardiology) or to obtain diagnostic testing (e.g., computed tomography scan, echocardiogram) for evaluation of transplant candidacy

III

Committee Review / Decision

Date of the committee review where a decision regarding eligibility for transplant (approved, declined, needs re-representation) was made

IV

Waitlist

Date the transplant candidate was documented by the transplant nurse coordinator as being added to the United Network of Organ Sharing (UNOS) waitlist

V

Transplant

Date the transplant surgery was performed

Table 1 – Our Data Extraction Process

Impact

The overall results of our study support our core hypothesis that a combination of variable data infrastructure, SDOH documentation, and provider perspectives negatively impacts the ability of marginalized patients to successfully complete the transplant selection process. Our study highlights robust opportunities to address inequities in access to solid organ transplantation via (1) improved SDOH data collection infrastructure, (2) continued data monitoring and inequity identification, and (3) implementation of equity-focused education and quality metrics into the transplant center structure.

Analysis of our kidney and liver transplant referral cohort revealed overall decreased odds of listing for transplant and higher odds of elimination at both referral and evaluation phases in the selection process for patients in marginalized groups compared to their privileged counterparts.

A total of 18 variables (64.3%) were included as discrete data collection fields within SDOH forms in Epic, including variables that were poorly represented in the national data source review (access to health services, gender identity, sexual orientation, food insecurity, spirituality, and wealth). Of these eighteen variables, seven variables exhibited 100% missingness for the transplant referral cohort on initial review and after performing a quality review. The other eleven variables ranged in missingness within the cohort from 2.57-69.82% on the initial review and from 0.00-10.92% after data validation.

Our stakeholder interview thematic analysis found four major themes regarding the organizational assessment of the DTC:

  1. Disconnect from community;
  2. Lack of tools to meet patient needs;
  3. Lack of ownership/accountability; and
  4. Clinician/staff knowledge.

Next Steps

Health System Data Infrastructure

The data query method developed by DIHI will be operationalized for both research and quality improvement.

Quality Improvement

The DTC data team will use the data query to begin tracking patients who are referred but not listed for transplant, with a review of these patients integrated into DTC Quality Assurance Project Improvement (QAPI) process.

Research

A study funded by the American Surgical Association will begin July 1st that integrates the data query into two additional centers (Houston Methodist and University of Michigan) for external validation.

Social Determinants of Health
Data Collection

The DTC will form a new workgroup to focus on SDOH data collection with representation from transplant social workers, care coordinators, surgeons, nephrologists, hepatologists, and pharmacists. The team will focus on ensuring the completeness of SDOH data collection throughout the transplant selection process.

Culture and Capacity for Change

Building a foundational knowledge base regarding equity and creating a culture of inclusion is a critical starting point for advancing equity within the DTC. The DTC will follow the model established by Population Health Sciences and institute a Diversity, Equity, Inclusion (DEI) initiative over the next six months that includes: center-wide climate assessment, online and in–person educational sessions, and implicit association testing of clinical faculty with follow up group discussion sessions.

Clinical Operations and Care Improvement

A variety of ongoing equity-focused efforts are already being pursued by DTC clinicians and staff, ranging in topics from pharmacoequity to food insecurity, financial strain, and access to technology. Later this year, the DTC leadership will meet with the project heads to establish objectives and determine what support is required to ensure successful completion. In 2023, regularly scheduled meetings with these project leads and transplant center leadership will begin to monitor progress.

Academic Output

The American Surgical Association Foundation Award (PI: McElroy) was awarded in November 2021 to continue this work. The award period begins on July 1, 2022. The project aims are to:

  1. Implement a data architecture to track patients along the continuum of transplant care at three centers.
  2. Quantify disparities in access to the transplant waitlist based on manually extracted enhanced SDOH data.
  3. Develop a clinical decision-making support tool to inform multidimensional risk assessment by transplant selection committees.

“Social Determinants of Health Data Capture Within National and Health System Data Sources” is accepted for an oral presentation in the Scientific Forum at Clinical Congress 2022, taking place October 16-20 in San Diego, CA.

Social Determinants of Health Data in Organ Transplantation: National Data Sources and Future Directions. Chan N, Moya Mendez M, Henson J, Zaribafzadeh H, Sendak M, Bhavsar N, Balu S, Kirk A, McElroy LM. American Journal of Transplantation. Am J Transplant.
2022 May 18

 

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