Medical corruption is a significant obstacle to achieving health-related Sustainable Development Goals. However, the understanding of medical corruption is limited, especially in developing countries. As the largest developing country, China is also plagued by medical corruption. By employing a mixed-methods design and combining data from three resources, this study attempts to examine patterns of medical corruption in China, explore its key drivers and investigate the perceived effectiveness of recent anti-corruption interventions. Using extracted data from 3546 cases on the China Judgments Online website between 2013 and 2019, we found that bribery, embezzlement and insurance fraud accounted for 68.1%, 22.8% and 9.1% of all medical corruption cases, respectively. Bribery was the major form of medical corruption. Approximately 80% of bribe-takers were healthcare providers, and most bribe-givers were suppliers of pharmaceuticals, medical equipment and consumables. Using a nationally representative household survey, we further found that the prevalence of informal payments from patients remained at a low level between 2011 and 2018. In 2018, only 0.4% of outpatients and 1.4% of inpatients reported that they had ever given 'red envelopes' to physicians in the past. Finally, we conducted interviews with 17 key informants to explore drivers of medical corruption and investigated the perceived effectiveness of recent anti-corruption interventions in China. Interview results showed that financial pressure and weak oversight were two main reasons for corrupt behaviours. Interview results also suggested that the anti-corruption campaign since 2012, the national volume-based procurement, and the special campaign against medical insurance fraud had reduced opportunities for medical corruption, implying China's positive progress in combating medical corruption. These findings hold lessons for anti-corruption interventions in China as well as other developing countries.
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http://dx.doi.org/10.1093/heapol/czad015 | DOI Listing |
Hum Brain Mapp
February 2025
Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
Irregular and unpredictable fetal movement is the most common cause of artifacts in in utero functional magnetic resonance imaging (fMRI), affecting analysis and limiting our understanding of early functional brain development. The accurate detection of corrupted functional connectivity (FC) resulting from motion artifacts or preprocessing, instead of neural activity, is a prerequisite for reliable and valid analysis of FC and early brain development. Approaches to address this problem in adult data are of limited utility in fetal fMRI.
View Article and Find Full Text PDFZ Med Phys
January 2025
Aix-Marseille Univ, CNRS, CRMBM, Marseille, France; APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Purpose: To develop an improved post-processing pipeline for noise-robust accelerated phase-cycled Cartesian Single (SQ) and Triple Quantum (TQ) sodium (Na) Magnetic Resonance Imaging (MRI) of in vivo human brain at 7 T.
Theory And Methods: Our pipeline aims to tackle the challenges of Na Multi-Quantum Coherences (MQC) MRI including low Signal-to-Noise Ratio (SNR) and time-consuming Radiofrequency (RF) phase-cycling. Our method combines low-rank k-space denoising for SNR enhancement with Dynamic Mode Decomposition (DMD) to robustly separate SQ and TQ signal components.
Nat Med
January 2025
Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
The adoption of large language models (LLMs) in healthcare demands a careful analysis of their potential to spread false medical knowledge. Because LLMs ingest massive volumes of data from the open Internet during training, they are potentially exposed to unverified medical knowledge that may include deliberately planted misinformation. Here, we perform a threat assessment that simulates a data-poisoning attack against The Pile, a popular dataset used for LLM development.
View Article and Find Full Text PDFNatl Sci Rev
January 2025
Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
The long-term preservation of large volumes of infrequently accessed cold data poses challenges to the storage community. Deoxyribonucleic acid (DNA) is considered a promising solution due to its inherent physical stability and significant storage density. The information density and decoding sequence coverage are two important metrics that influence the efficiency of DNA data storage.
View Article and Find Full Text PDFPhys Imaging Radiat Oncol
October 2024
Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Radiation Oncology, De Boelelaan 1117, Amsterdam, the Netherlands.
Background And Purpose: Segmentation imperfections (noise) in radiotherapy organ-at-risk segmentation naturally arise from specialist experience and image quality. Using clinical contours can result in sub-optimal convolutional neural network (CNN) training and performance, but manual curation is costly. We address the impact of simulated and clinical segmentation noise on CNN parotid gland (PG) segmentation performance and provide proof-of-concept for an easily implemented auto-curation countermeasure.
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