# Expansion of Percutaneous Coronary Intervention in Outpatient and Inpatient Settings: Quantifying the Differential Impact Between Disadvantaged and Non-Disadvantaged Communities

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $572,097

## Abstract

Project Summary/Abstract
Expansion of Percutaneous Coronary Intervention in Outpatient and Inpatient Settings: Quantifying the
Differential Impact Between Disadvantaged and Non-Disadvantaged Communities
 Technology in medicine has often been deployed without explicit attention to disadvantaged
populations, with the idea that “a rising tide lifts all boats.” Yet it is now clear that this assumption has not held
true, particularly in the field of cardiac care and availability of percutaneous coronary intervention (PCI). Racial
and ethnic minorities, low-income individuals, and the uninsured experience persistent and even widening
inequalities in access, treatment, and outcomes. Unfortunately, the literature has focused primarily on
discrimination at the individual or institutional level. Our long-term goal is to identify system-level pathways in
cardiac care that widen disparities between disadvantaged and non-disadvantaged communities and patients.
 Our central hypothesis is that that the current pattern of PCI growth has differentially affected
disadvantaged versus non-disadvantaged communities, and that studying these communities separately
allows us to unmask differences that could otherwise be undetected when looking at the average effect. Using
PCI expansion as an identification strategy along with 10 years of patient-level data from California, we
propose to test the hypothesis that the growth of outpatient and inpatient PCI has: (Aim 1) increased the
likelihood of higher-risk or inappropriate patients receiving PCI in non-disadvantaged relative to disadvantaged
communities; (Aim 2) changed patient distribution to low-quality facilities in disadvantaged versus advantaged
communities; and (Aim 3) widened the health disparity gap between patients in disadvantaged and non-
disadvantaged communities through changes in patient profile and/or the quality of PCI facility.
 This study is innovative because it proposes: a) a longitudinal, population-based approach to study all
PCIs done in both outpatient and inpatient settings, not relying solely on hospital-based PCI or PCI registries;
b) a multi-level (individual, hospital, and community) approach to understand differential experiences of
technology expansion for patients, facilities, and communities; and c) use a structural racism and
discrimination lens to focus on the built environment, which present an intervenable target.
 Findings from our research could inform interventions such as local, state, or even federal policy
changes. The current trend appears to be toward allowing as many hospitals as possible to become PCI-
capable, with active legislation (e.g., California AB 370, New Jersey A1176). Current policies and legislation do
not consider impact on equity nor do they incorporate explicit quality metrics. Examples of potential policy
changes include reforms to legal and regulatory changes regarding licensing, credentialing, reimbursement,
and liability; revising definitional requirements...

## Key facts

- **NIH application ID:** 10896966
- **Project number:** 5R01HL134182-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Renee Yuen-Jan Hsia
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $572,097
- **Award type:** 5
- **Project period:** 2016-07-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10896966

## Citation

> US National Institutes of Health, RePORTER application 10896966, Expansion of Percutaneous Coronary Intervention in Outpatient and Inpatient Settings: Quantifying the Differential Impact Between Disadvantaged and Non-Disadvantaged Communities (5R01HL134182-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10896966. Licensed CC0.

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