Identifying modifiable process delays in deceased donor kidney allocation through process mining

The National Institute of Diabetes and Digestive and Kidney Diseases awarded New York University $872,273 on August 20, 2026, under the Diabetes, Digestive, and Kidney Diseases Extramural Research program (CFDA 93.847) to identify modifiable process delays in deceased donor kidney allocation through process mining analysis.

The project applies process mining methodology to discover actual kidney placement workflows from data logs at multiple organ procurement organizations (OPOs). The research aims to understand why out-of-sequence allocation has increased tenfold while 28 percent of recovered deceased donor kidneys remain unused, and to develop targeted policy and practice changes that reduce non-use while maintaining public trust in the transplant system. The investigative approach combines qualitative and quantitative dimensions: participant observations and discussions with OPO leadership and organ allocation coordinators over ten days at each participating OPO, paired with analysis of internal OPO allocation event logs to characterize durations, dependencies, and critical paths in deceased donor kidney placement workflows.

The award supports research performed in New York, New York, with a period of performance through May 31, 2031. The funding is classified as a Project Grant under the assistance type framework.

Abstract

Of 30,779 deceased-donor kidneys recovered for transplantation in 2023¬¬, 8583 (28%) were not used. Minutes matter because kidneys that have been recovered and stored degrade quickly while organ procurement organizations (OPOS) make offers for one patient at a time according to the allocation ordering. The urgency of finding someone to accept each kidney has triggered a 10-fold increase in organ procurement organizations' (OPO) use of discretionary, center-targeted, out-of-sequence (OOS) allocation. OOS allocation might increase transplants by rescuing kidneys from non-use but risks the loss of public trust because IT skips hundreds or thousands of candidates with higher priority. Identifying why OOS was increasing through detailed process maps would inform targeted changes to policy and practice that would both reduce non-use and strengthen trust in the transplant system. For each OPO, and indeed for each available kidney, this process is realized in such a variety of ways that even OPO leadership might not have a comprehensive view of bottlenecks and opportunities for improvement. Granular timing data exist, but are sequestered in opaque OPO-specific software, and must be converted to time-focused process maps to enable forecasting how changes to optn directives and to center and OPO practices could improve kidney placement. We propose to use process mining to discover actual kidney placement workflow processes from data logs at several OPOS. Our aims are: 1) to add a qualitative dimension to help understand existing quantitative kidney placement data logs through discussions with both OPO leadership and organ allocation coordinators and participant observations over 10 days at each OPO, 2) to analyze internal OPO allocation event logs to characterize the durations and dependencies in all steps of deceased donor kidney placement and find critical paths to identify which steps in the workflow are most likely to cause delays, and 3) to simulate kidney placement under potential interventions to project time saved and increases in the number of transplant centers receiving and responding to kidney offers pre-recovery or within 12 hours of recovery. Our findings will finally establish a detailed, evidence-based understanding of bottlenecks, process failures, and best practices and policies for kidney allocation. Our process maps and simulations will guide the optn and OPOS to implement targeted allocation changes that can reduce discards while safeguarding trust in the transplant system.