OPO Perspectives on AI Adoption

Between December 2023 and May 2024, we have interviewed 10 OPO leaders from across the country. One observation has been the existence of conflicting visions for AI adoption.

Some leaders believe that there needs to be a coordinated, top-down approach to AI adoption to ensure fairness and equity. This would likely be led by government regulators. However, this is likely to be a slower process.

Others see a need for a more bottom-up approach to facilitate experimentation and innovation. This can support the identification of best practices, but not all OPOs are equally able to participate. OPOs that are bigger or have more resources will be better able to benefit from AI.

The bottom line is that the exact impact of AI on OPO practices is unknown. We are hoping to generate evidence on how much AI can help as part of our planned field experiments using UNOS’s SimUNet. There are pros and cons of both a top-down and bottom-up approach in the adoption of AI. We are currently working on drafting a paper to summarize our findings and encourage more dialogue on this subject. In the meantime, we want to know what you think!

Developing Deep Learning Models for OPOs and Transplant Centers

So far, we have created 2 deep learning models that are focused on the OPO perspective:

  1. Deceased Donor Kidney Assessment – which evaluates the likelihood that a donated kidney will be transplanted based on up to 18 characteristics (including biopsy information, if available). This can help OPOs determine whether a kidney is hard-to-place based on historical behavior. You can play with this model here: https://ddoa.mst.hekademeia.org/#/
  2. Final Acceptance – which improves the estimate of the likelihood of transplant by incorporating recipient characteristics. This can be used to make an estimate for each recipient on the Match Run. For hard-to-place kidneys, this can help determine where to go for expedited placement. OPOs could save time by not sending offers that are very unlikely to be accepted.

Both models are trained from the OPTN Deceased Donor Dataset. We are planning to test the impact of these models using UNOS’s SimUNet, which is a research platform that is currently being expanded to include the OPO perspective as part of this project. To date, SimUNet studies have only focused on transplant surgeon decision-making.

To develop models that support transplant surgeons, we believe a more tailored approach is needed. Professor Cihan Dagli and Rachel Dzieran are working on a new model called Transplant Surgeon Fuzzy Associate Memory (TSFAM). The intent is for the model to use deep learning network model interfaces to capture individualized transplant surgeon practices and assessments through fuzzy associative memory. Fuzzy logic accounts for imperfect data and ambiguity, which is more consistent with how humans make decisions. We are identifying decision rules used by an individual transplant surgeon and then tailoring the AI-based decision-making model to support individual decision-making. Case studies are currently being reviewed for building the structure to collect the individualized transplant surgeon policies. The primary goal of this work is to support transplant surgeons by using their own policies when assessing deceased donor organs. Dr. Dagli has two new PhD students joining in Fall 2024 to continue to develop this new model.

Contributor: Rachel Dzieran (S&T), Cihan Dagli (S&T)

CHIL 2024

We had a great time at the the Conference on Health, Inference, and Learning (CHIL) this year. CHIL aims to include clinicians and researchers from both industry and academia who specialize in machine learning, health policy, causality, fairness, and other related fields. The conference aims to foster insightful discussions on innovative and emerging ideas, fostering collaboration and dialogue. Different presentations on fairness in regards to algorithms were found to be very helpful toward our project.

Dr. Nadendla (Associate Professor) and Mukund Telukunta (PhD Student) presented “Learning Social Fairness Preferences from Non-Expert Stakeholder Opinions in Kidney Placement”.

Mukund Telekunta and Dr. Nadendla at CHIL.

Abstract: Modern kidney placement incorporates several intelligent recommendation systems which exhibit social discrimination due to biases inherited from training data. Although initial attempts were made in the literature to study biases in kidney placement, these methods replace true outcomes with surgeons’ decisions due to the long delays involved in recording such outcomes reliably. However, the replacement of true outcomes with surgeons’ decisions disregards expert stakeholders’ biases as well as social opinions of other stakeholders who do not possess medical expertise. This paper alleviates the latter concern and designs a novel fairness feedback survey to evaluate an acceptance rate predictor (ARP) that predicts a kidney’s acceptance rate in a given kidney-match pair. The survey is launched on Prolific, a crowdsourcing platform, and public opinions are collected from 85 anonymous crowd participants. Our results show that the public participants deem “accuracy equality” as the preferred notion of fairness across all sensitive features. Moreover, the specific ARP tested in the Prolific survey has been deemed fair by the participants.