# Organ Procurement and Information Process Optimization

> **NIH AHRQ R03** · UNIVERSITY OF TEXAS AT AUSTIN · 2021 · $58,019

## Abstract

Project Summary: Organ Procurement and Information Process Optimization
A recent White House initiative titled Advancing American Kidney Health outlines the current
administration's plans to increase the supply of kidneys in the US, along with other measures that
attempt to correct the imbalance between supply and demand for kidneys. In alignment with this
White House initiative, the principal investigator (PI) of this project will develop algorithms to
assist Organ Procurement Organizations (OPOs) make donor disposition decisions with the goal of
increasing the supply of transplantable organs. The PI will harness the vast amount of data collected
by OPOs, and customize as well as develop new Artificial Intelligence (AI) algorithms to identify
good donors. These algorithms will reduce case coordinators workload and improve the accuracy
of their decisions. The PI's objective is to quantity the impact of AI-assisted decision-making on
increasing the supply of kidneys by finding missed opportunities on account of the current manual
processes. Even a few more potential donors per OPO will help reduce the organ shortage problem
when scaled to all 58 OPOs across the country.
 This project will leverage unique data sets to generate and test hypotheses concerning which fac-
tors affect donor disposition decisions, and which information processing protocols produce greater
referral-to-donor conversion rate. It will also customize classification algorithms to assist case co-
ordinators make donor disposition decisions. The focus will be on improving both the accuracy
and speed of such decisions. As a follow up of this project, the PI will conduct a broader study
involving at least five additional OPOs. The five OPOs will be selected to represent the diversity
in size, population, and geography among the 58 OPOs across the country. Results of this and the
follow-up study will be shared freely with all OPOs. The proposed research has the potential to
increase the number of donors, free up staff time, and lower OPO labor costs.
 The potential impact of this project lies in the formulation and testing of hypotheses that can
benefit OPO decision-making and people on transplant wait list, customization of existing machine
learning algorithms and computational techniques for sequential decision-making in a novel setting,
and laying the groundwork for the development of new methods in the future.
 The PI has the necessary disciplinary expertise, and management and leadership experience to
succeed in the proposed efforts. He has access to advanced computing resources and administrative
help from the University of Texas. Finally, he has obtained the necessary IRB approval to proceed
with this project.
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## Key facts

- **NIH application ID:** 10246800
- **Project number:** 5R03HS027671-02
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** DIWAKAR GUPTA
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2021
- **Award amount:** $58,019
- **Award type:** 5
- **Project period:** 2020-09-01 → 2022-08-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10246800

## Citation

> US National Institutes of Health, RePORTER application 10246800, Organ Procurement and Information Process Optimization (5R03HS027671-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10246800. Licensed CC0.

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