Objective: To segregate imaging expenditures from claims data by resource utilization bands (RUBs) and underlying conditions to create an "expenditure map" of pediatric imaging costs.
Methods: A Claims data for children enrolled in a commercial value-based plan were categorized by RUB 0 non-user, 1 healthy user, 2 low morbidity, 3 moderate morbidity, 4 high morbidity, & 5 very high morbidity. The per member per year (PMPY) expense, total imaging spend, and imaging modality with the highest spend were assessed for each RUB. Diagnosis categories associated with high imaging costs were also evaluated.
Results: There were 40,022 pediatric plan members. 14% had imaging-related claims accounting for approximately $2.8 million in expenditures. Member distribution and mean PMPY expenditure RUB was respectively: RUB 0 (3,037, $0), RUB 1 (6,604, $7), RUB 2 - 13,698, $27), RUB 3 - 13,341, $87), RUB 4 (2,810, $268), RUB 5 (532, $841). RUB 3 had the largest total imaging costs at $1,159,523. The imaging modality with the greatest mean PMPY expense varied by RUB with radiography highest in lower RUBs and MRI highest in higher RUBs. The top 3 diagnoses associated with the highest total imaging costs were developmental disorders ($443,980), asthma ($388,797), and congenital heart disease ($294,977) and greatest mean PMPY imaging expenditures malignancy/leukemia ($3,100), transplant ($2,639), and tracheostomy ($1,661).
Discussion: Expense mapping using claims data allows for a better understanding of the distribution of imaging costs across a covered pediatric population. This tool may assist organizations in planning effective cost-reduction initiatives and learning how imaging utilization varies by patient complexity in their system.
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http://dx.doi.org/10.1067/j.cpradiol.2024.07.018 | DOI Listing |
PLoS One
January 2025
Kansai Research Center, Forestry and Forest Products Research Institute, Kyoto City, Kyoto Prefecture, Japan.
Soil imaging in the field and laboratory has greatly advanced our understanding of plant root systems. Soil fungi function as important plant symbionts and decomposers of complex organic material in soil environments. For fungal hyphae, however, the application of soil imaging remains scarce, limiting our understanding of hyphal systems in soil.
View Article and Find Full Text PDFJ Magn Reson Imaging
January 2025
Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Women with extremely dense breasts are at a higher risk of breast cancer, and the sensitivity of mammography in this group is reduced due to the masking effect of overlapping tissue. This review examines supplemental screening methods to improve detection in this population, with a focus on MRI. Morphologic techniques offer limited benefits, digital breast tomosynthesis (DBT) shows inconsistent results, and ultrasound (US), while improving cancer detection rates (CDR), results in a higher rate of false positives.
View Article and Find Full Text PDFJ Imaging
January 2025
Department of Food Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan.
Pneumonia, a leading cause of mortality in children under five, is usually diagnosed through chest X-ray (CXR) images due to its efficiency and cost-effectiveness. However, the shortage of radiologists in the Least Developed Countries (LDCs) emphasizes the need for automated pneumonia diagnostic systems. This article presents a Deep Learning model, Zero-Order Optimized Convolutional Neural Network (ZooCNN), a Zero-Order Optimization (Zoo)-based CNN model for classifying CXR images into three classes, Normal Lungs (NL), Bacterial Pneumonia (BP), and Viral Pneumonia (VP); this model utilizes the Adaptive Synthetic Sampling (ADASYN) approach to ensure class balance in the Kaggle CXR Images (Pneumonia) dataset.
View Article and Find Full Text PDFBiosensors (Basel)
January 2025
Department of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain.
In recent years, the wine industry has been researching how to improve wine quality along the production value chain. In this scenario, we present here a new tool, MicroVi, a cost-effective chip-sized microscopy solution to detect and count yeast cells in wine samples. We demonstrate that this novel microscopy setup is able to measure the same type of samples as an optical microscopy system, but with smaller size equipment and with automated cell count configuration.
View Article and Find Full Text PDFBiosensors (Basel)
January 2025
State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Si Pai Lou 2, Nanjing 210096, China.
We present a cost-effective and simple multiplex nucleic acid quantification method using droplet digital PCR (ddPCR) with digital melting curve analysis (MCA). This approach eliminates the need for complex fluorescent probe design, reducing both costs and dependence on fluorescence channels. We developed a convolutional neighborhood search algorithm to correct droplet displacement during heating, ensuring precise tracking and accurate extraction of melting curves.
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