# Personalized Provider Selection to Reduce Surgical Disparities

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2022 · $664,358

## Abstract

Colorectal cancer (CRC), the second leading cause of death in older adults in 2019, was diagnosed in 145,600
patients and was responsible for 51,020 deaths. In the absence of metastatic disease, surgery is the standard
of care for more than 90% of CRC patients. Insight from existing literature and our preliminary studies suggest
that the most essential surgical disparities in CRC are related to race effects in surgical risk and strong hospital-
associated differences in mortality and morbidity. Significant variation in CRC surgical outcomes exists across
hospitals (e.g. mortality rates 0.6%-14.7%) with known disparities adversely affecting black patients. Black
patients have lower surgical utilization rates, worse surgical outcomes, and lower survival rates compared to
White patients. Black patients are more likely to use lower quality, lower volume hospitals for surgery, even when
a higher quality choice can be found closer to home. These disparities extend beyond race to residential setting
(e.g. rural) and other patient characteristics. Access to higher quality hospitals is a critical barrier to achieving
surgical equity across the population. Data to drive hospital selection is limited. Our preliminary studies
demonstrate that most Black patients (86%) have a higher quality hospital located within close proximity of their
home and the potential to reduce disparities by >30% with data driven referrals while improving outcomes across
populations. Existing risk stratification tools to assist in the hospital selection process lack the requisite
combination of factors to facilitate rational decision-making including: 1) disease specificity, 2) attention to
complex patient-provider interactions, 3) information on hospital quality, and 4) comparative statistics. Our
preliminary data suggest that accurate risk prediction can be performed that meet these criteria. In the proposed
study, we will refine the personalized prediction models, scale them to the national level, and develop the tools
to make statistical comparisons possible. As disparities are no longer a problem for the vulnerable alone, we
demonstrate the gains in Societal Welfare of data driven referrals using counterfactual simulation. Further, we
will use scenario testing to simulate the effects of data driven referrals on the willingness of referring providers
to trade-off convenience and reputation for enhanced quality. This information is critical to drive policy reform to
advance surgical equity. Our goal is to reduce disparities by referring older, black CRC patients to higher quality
hospitals by 1) developing personalized risk models to differentiate across hospitals (or surgeons), 2) providing
evidence to inform policies designed to incentivize data driven referrals, and 3) setting strategies to promote data
driven referrals for CRC. This pioneering work will provide 1) new methods of risk stratification, 2) an estimate
of the Societal Welfare benefits of data driven referrals for...

## Key facts

- **NIH application ID:** 10445916
- **Project number:** 1R01MD016088-01A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Rachel Kelz
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $664,358
- **Award type:** 1
- **Project period:** 2022-05-19 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10445916

## Citation

> US National Institutes of Health, RePORTER application 10445916, Personalized Provider Selection to Reduce Surgical Disparities (1R01MD016088-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10445916. Licensed CC0.

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