Talk to the Veterans Crisis Line now
U.S. flag
An official website of the United States government

VA Health Systems Research

Go to the VA ORD website
Go to the QUERI website

SDR 20-361 – HSR Study

SDR 20-361
Racial Bias in a VA Algorithm for High-Risk Veterans
Amol S. Navathe, MD PhD
Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA
Philadelphia, PA
Funding Period: February 2021 - January 2025


PROJECT SUMMARY African-American Veterans are at particular risk of adverse outcomes, including mortality and hospitalization, due to adverse social determinants of health (SDoH) including poor transportation access and housing instability. Identifying individuals at risk of adverse outcomes has been a priority at the Veterans Health Administration (VA), which has implemented novel predictive analytic tools in clinical care settings to target care resources efficiently and equitably. The VA has invested an average of 5% of total VA spending towards health information technology to support such algorithms. One predictive algorithm implemented nationwide and commonly used by VA clinicians is the Care Assessment Needs (CAN) score, which predicts risk of future hospitalization and/or death for over 5 million Veterans receiving primary care. The CAN score is currently used by patient-aligned care teams (PACTs) and nurse care navigators to direct clinical programs and resources, including telehealth, palliative care, and home-based primary care, to high-risk Veterans. The CAN score is primarily based on laboratory, demographic, utilization, and other administrative data. Recent studies have shown that similar algorithms used in non-VA settings may mischaracterize risk for vulnerable patient subgroups – including African-Americans – whose health is heavily influenced by disproportionate exposure to adverse SDoH. Importantly, race and SDoH are not routine inputs into the CAN score. There is a growing concern that algorithms like the CAN score could generate “algorithmically unfair” predictions that systematically mischaracterize risk for subgroups – particularly African-Americans – whose care is heavily influenced by SDoH. However, there has been no systematic investigation into unfairness of the CAN score between African-American and White Veterans. In this project, we will systematically examine algorithmic unfairness in the VA CAN algorithm and develop approaches to mitigate it, including testing the incorporation of SDoH metrics. Our preliminary investigations into the CAN score show that it underestimates risk for African-Americans compared to White Veterans, which may lead to fewer referrals of high risk African-American Veterans to clinical programs. In Aim 1, we will develop methods to mitigate algorithmic unfairness in the CAN score using its existing variables. In Aim 2, we will incorporate race and select metrics of SDoH that are available through VA screening efforts into the CAN score to improve algorithmic unfairness. In Aim 3, we will use the “Fair” CAN score generated in Aim 2 to investigate how mitigating unfairness would change the racial composition of Veterans enrolled in clinical programs targeted at high-risk Veterans.

External Links for this Project

NIH Reporter

Grant Number: I01HX003371-01

Dimensions for VA

Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects.

Learn more about Dimensions for VA.

VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address.
    Search Dimensions for this project


Journal Articles

  1. Parikh RB, Manz CR, Nelson MN, Evans CN, Regli SH, O'Connor N, Schuchter LM, Shulman LN, Patel MS, Paladino J, Shea JA. Clinician perspectives on machine learning prognostic algorithms in the routine care of patients with cancer: a qualitative study. Supportive Care in Cancer : Official Journal of The Multinational Association of Supportive Care in Cancer. 2022 May 1; 30(5):4363-4372. [view]
  2. Shrank WH, Chernew ME, Navathe AS. Hierarchical Payment Models-A Path for Coordinating Population- and Episode-Based Payment Models. JAMA. 2022 Feb 1; 327(5):423-424. [view]
  3. Bange EM, Courtright KR, Parikh RB. Implementing automated prognostic models to inform palliative care: more than just the algorithm. BMJ quality & safety. 2021 Oct 1; 30(10):775-778. [view]
  4. Parikh RB, Zhang Y, Kolla L, Chivers C, Courtright KR, Zhu J, Navathe AS, Chen J. Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic. Journal of the American Medical Informatics Association : JAMIA. 2023 Jan 18; 30(2):348-354. [view]

  1. Parikh RB, Zhang Y, Chivers C, Courtright KR, Zhu J, Hearn CM, Navathe AS, Chen J. Performance Drift in a Mortality Prediction Algorithm during the SARS-CoV-2 Pandemic. medRxiv : the preprint server for health sciences [Preprint]. 2022 Mar 1; doi: [view]

DRA: Health Systems
DRE: TRL - Applied/Translational
Keywords: Socioeconomic Factors
MeSH Terms: None at this time.

Questions about the HSR website? Email the Web Team

Any health information on this website is strictly for informational purposes and is not intended as medical advice. It should not be used to diagnose or treat any condition.