The number of clinical research investigators that the US Food and Drug Administration has disqualified or totally restricted has been increasing since 1964. In addition, several public polls and surveys indicate a major dilemma in clinical trial participation and public perceptions of clinical research. This study investigates how clinical investigator fraud or misconduct influences public perceptions of participation in clinical trials. An electronic survey was developed for the faculty of Eastern Michigan University. The survey results (11.2% response rate) indicated that 81% of respondents were willing to consider participation in a clinical trial or had participated. However, when the respondents were told of a case of investigator fraud, approximately 25% of willing respondents were now discouraged from participation. The influence of the knowledge of investigator fraud did not seem to be greatly correlated with the geographic location of the event relative to the location of the respondents. While it seems that news of investigator fraud would therefore significantly affect enrollment efforts in ongoing clinical studies, these results reflect only a select group of highly educated people, and more definitive studies are recommended to understand the impact of investigator fraud and the duration of this impact on patient recruitment into clinical studies.
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http://dx.doi.org/10.1177/0092861512457776 | DOI Listing |
United States and European Union laws demand separate clinical studies in children as a condition for drugs' marketing approval. Justified by carefully framed pseudo-scientific wordings, more so the European Medicines Agency than the United States Food and Drug Administration, "Pediatric Drug Development" is probably the largest abuse in medical research in history. Preterm newborns are immature and vulnerable, but they grow.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Department of Electrical Engineering, ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.
Background: Waste and fraud are important problems for health insurers to deal with. With the advent of big data, these insurers are looking more and more towards data mining and machine learning methods to help in detecting waste and fraud. However, labeled data is costly and difficult to acquire as it requires expert investigators and known care providers with atypical behavior.
View Article and Find Full Text PDFArtif Intell Med
December 2024
AI Center of Excellence, Deloitte & Touche LLP. New York, NY, United States of America. Electronic address:
Objective: Identifying fraud in healthcare programs is crucial, as an estimated 3%-10% of the total healthcare expenditures are lost to fraudulent activities. This study presents a systematic literature review of machine learning techniques applied to fraud detection in health insurance claims. We aim to analyze the data and methodologies documented in the literature over the past two decades, providing insights into research challenges and opportunities.
View Article and Find Full Text PDFSensors (Basel)
November 2024
School of Computing and Information Sciences, Anglia Ruskin University, Cambridge CB1 1PT, UK.
Today, many businesses use near-field communications (NFC) payment solutions, which allow them to receive payments from customers quickly and smoothly. However, this technology comes with cyber security risks which must be analyzed and mitigated. This study explores the cyber risks associated with NFC transactions and examines strategies for mitigating these risks, focusing on payment devices.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Institute for Global Tobacco Control, Department of Health, Behavior and Society, Johns Hopkins University, Baltimore, MD, United States.
In 2019, we launched a web-based longitudinal survey of adults who frequently use e-cigarettes, called the Vaping and Patterns of E-cigarette Use Research (VAPER) Study. The initial attempt to collect survey data failed due to fraudulent survey submissions, likely submitted by survey bots and other survey takers. This paper chronicles the journey from that setback to the successful completion of 5 waves of data collection.
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