Transplant professionals interviewed by Afflo consistently identified a data collection problem prior to patient waitlisting. Pre-waitlisting data is seen as a "black box", even though access to the waitlist is a major equity issue. Waitlist access clearly shows the impact Social Determinants of Health (SDOH) have on transplant patient outcomes.
Data also shows that transplant is by far the best treatment modality for end-stage organ disease. For End-Stage Kidney Disease (ESKD) patients, transplant recipients have an 80% chance of five-year survival compared to a 35% chance for dialysis patients. Because pre-listed patient experiences, mortality, and barriers to access are generally not assessed for metrics and outcomes on transplant system performance, there is limited standardized data.
Interviews and analysis suggest there are multiple points in the pre-listing referral and evaluation process that are inequitable. For example, data shows Black patients in the US are more likely to be referred and listed further along in their disease progression. There are also barriers for patients for whom English is not their first language in understanding the listing, testing, and evaluation process. In the UK, access to a car was a key sociodemographic factor for accessing pre-emptive transplants.
However, there is no workflow to capture, collate, and analyze data from the end-to-end referral, evaluation, and listing process. This includes straightforward medical assessment such as initial lab work, detailed medical assessments ranging from cardiology to GI consults and chest radiography as well HLA comparisons, unacceptable antigen listing, and crossmatch eligibility. It also includes evaluations directly impacted by the SDOH such as dietitian evaluation and ongoing monitoring for patients with higher BMIs, evaluation of caregivers include psychosocial status and living conditions, financial evaluation, as well as surgical evaluations of frailty, suitability, and likelihood of medical compliance which can be non-standard and subject to bias.
Some of this data is potentially available but untapped as it is captured in paper-based and PDF evaluation and assessment forms; however, collection, aggregation, and analysis are seen as costly especially for systems already overburdened and understaffed. Transplant staff emphasized a need for methods for collecting this information with minimal operational burden.