Oct 24, 2023

A Multi-Factored Mechanism to 

Distribute Health Emergency Funds

Stacy Chen (rchen@mitre.org)

Timothy Champney (tchampney@mitre.org)

Rob Hartman (rhartman@mitre.org)

Surya Menon (smenon@mitre.org)

Mary Munro (mmunro@mitre.org)

Hongxun Qin (hqin@mitre.org) 

Yueh Quach (yquach@mitre.org)

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Background

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 The Coronavirus Aid, Relief, and Economic Security 

(CARES) Act was passed by Congress on March 25, 2020 

and signed into law on March 27, 2020.

 Under the law, the Provider Relief Fund was created.

 The early rounds of the Fund were distributed mainly based 

on revenues of the hospitals, not community characteristics

 The news media (Wall Street Journal) criticized that 

profitability of hospital was not considered. 

(https://www.wsj.com/articles/billions-in-covid-aid-went-to-hospitals-that-didnt-need-it-11670164570) 

 In April 2023, the House Committee on Oversight and 

Accountability launched an investigation into how the funds 

were allocated to hospitals.
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 Research Question: How to distribute federal financial 

support to hospitals quickly at the beginning of an 

emergency? 

 Should the mechanism consider community characteristics 

in addition to hospital revenue? 

 We conduct analysis on characteristics of health providers 

and communities they serve to suggest the need for a multifactor modeling approach to distribute future emergency 

funding 

 We use data from Centers for Medicaid/Medicare Services 

(CMS), Census American Community Survey (ACS) to 

create hospital groupings

Introduction
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What to Consider

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 Hospital Characteristics

 Type

 Size

 Finance

 Patients

 Community-level 

Characteristics

 Demographics

 Socioeconomics

 Medical Insurance
Hospital Characteristics

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 Revenue

 Income

 Number of Employees, Beds

 Hospital Type

 Rural/Urban

 Discharges/Days

 Inpatient/Outpatient 

 Medicare/Medicaid

 More
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American Community Survey Data on People

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 Demographics

 Age

 Race

 Sex

 Socioeconomics

 Median Income

 Poverty Rate

 Medical Insurance Coverage

 Not Insured

 Medicare/Medicaid
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Cluster Analysis

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 Aggregation of Census Data by Hospital Service Areas

 Merging Hospital and Census Data by Hospital

 Missing Data

 Principal Component Analysis (PCA)

 Variables Construction

 Clustering Models
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What’s Next?

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 Analyze the clusters and the relationships among important 

variables.

 Get Subject-Matter Expertise from Policy Research and 

Medical Industry.

 Compare redesigned payment mechanism to provider fund 

mechanism. 

 Use findings to suggest a redesigned payment mechanism 

that considers both community level and hospital factors. 
Other Factors to Consider in Future

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 Pandemic 

Characteristics

 Weather

 Vulnerable Population

 Geographic Data

 Climate/Weather

 Terrain

 Population Density