Home
GSC Advanced Research and Reviews
Peer-reviewed | Multidisciplinary Journal | Impact factor 8.3 | ISSN: 2582-4597 | Crossref DOI

Main navigation

  • Home
    • Journal Information
    • Editorial Board Members
    • Reviewer Panel
    • Abstracting and Indexing
    • Journal Policies
    • Our CrossMark Policy
    • Publication Ethics
    • Issue in Progress
    • Current Issue
    • Past Issues
    • Instructions for Authors
    • Article processing fee
    • Track Manuscript Status
    • Get Publication Certificate
    • Join Editorial Board
    • Join Reviewer Panel
  • Contact us
  • Downloads

Pandemic profiteering at a time of crisis: Using python to detect fraud in covid-19 testing and treatment payments

Breadcrumb

  • Home
  • Pandemic Profiteering At a Time of Crisis: Using Python To Detect Fraud In Covid-19 Testing and Treatment Payments
  • Pandemic profiteering at a time of crisis: Using python to detect fraud in covid-19 testing and treatment payments

Isaac Asamoah Amponsah *

Public Administration, School of Public Management and Policy, University of Illinois Springfield, United States of America.
 
Research Article
GSC Advanced Research and Reviews, 2024, 19(02), 208–218.
Article DOI: 10.30574/gscarr.2024.19.2.0183
DOI url: https://doi.org/10.30574/gscarr.2024.19.2.0183
 
Received on 10 April 2024; revised on 18 May 2024; accepted on 20 May 2024
 
During the pandemic, the Centre for Medicare and Medicaid Services (CMS) introduced blanket waivers and rule flexibilities to address rising COVID-19 cases. This included expanding telehealth services to urban areas and waiving certain reporting requirements, along with various testing options such as surveillance testing, school and workplace testing, self-tests, and testing in more inpatient settings such as nursing homes. The federal and state governments also covered COVID-19 testing, vaccination and treatment for the uninsured population, creating opportunities for fraud and unnecessary testing, double billing, kickbacks, and deceased billing, mainly for monetary gain, by unscrupulous healthcare providers. Using Python programming, the study adopted an unsupervised learning approach by employing Isolation Forest to detect healthcare providers who were anomalies in the payment for COVID-19, treatment and vaccination by the Health Resources and Services Administration (HRSA). Additionally, using official search enquiry into official U.S. government websites such as the FBI, USDOJ, and HHS-OIG, this study identified eight (8) fraud, waste and abuse schemes related to laboratory testing and treatment. The isolation forest algorithm, set at a 5% contamination level, identified 1,890 healthcare providers (7.64% of total claims) as being anomalies. These results support the recommendations given to the HRSA by the Office of Inspector General of the Department of Health and Human Services (HHS-OIG), emphasizing the need for identifying and addressing improper payments. Protecting public health resources requires preventing fraud in the healthcare industry. Strong education programs for healthcare workers are crucial, as are vigilant oversight and collaboration between federal and state agencies. Additionally, this study emphasizes how crucial it is to use official government resources—such as the FBI, HHS-OIG, USDOJ, and CDC—to efficiently detect and prevent fraudulent activities. In the wake of information asymmetry, calls for private‒public partnerships are needed to address fraud, waste and abuse in the healthcare industry.
 
Medical Information; Anomaly Detection; COVID-19 Testing; Fraud; Waste and Abuse; Healthcare Fraud.
 
https://gscarr.gsconlinepress.com/sites/default/files/fulltext_pdf/GSCARR-2024-…

Preview Article PDF

Isaac Asamoah Amponsah. Pandemic profiteering at a time of crisis: Using python to detect fraud in covid-19 testing and treatment payments. GSC Advanced Research and Reviews, 2024, 19(2), 208-218. Article DOI: https://doi.org/10.30574/gscarr.2024.19.2.0183

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


All statements, opinions, and data contained in this publication are solely those of the individual author(s) and contributor(s). The journal, editors, reviewers, and publisher disclaim any responsibility or liability for the content, including accuracy, completeness, or any consequences arising from its use.

Get Certificates

Get Publication Certificate

Download LoA

Check Corssref DOI details

Issue details

Issue Cover Page

Editorial Board

Table of content

Copyright © 2026 GSC Advanced Research and Reviews - All rights reserved

Developed & Designed by VS Infosolution