Usability of Health Care Price Transparency Data in the United States: Mixed Methods Study.

BACKGROUND: Increasing health care expenditure in the United States has put policy makers under enormous pressure to find ways to curtail costs. Starting January 1, 2021, hospitals operating in the United States were mandated to publish transparent, accessible pricing information online about the items and services in a consumer-friendly format within comprehensive machine-readable files on their websites. OBJECTIVE: The aims of this study are to analyze the available files on hospitals' websites, answering the question-is price transparency (PT) information as provided usable for patients or for machines?-and to provide a solution. METHODS: We analyzed 39 main hospitals in Florida that have published machine-readable files on their website, including commercial carriers. We created an Excel (Microsoft) file that included those 39 hospitals along with the 4 most popular services-Current Procedural Terminology (CPT) 45380, 29827, and 70553 and Diagnosis-Related Group (DRG) 807-for the 4 most popular commercial carriers (Health Maintenance Organization [HMO] or Preferred Provider Organization [PPO] plans)-Aetna, Florida Blue, Cigna, and UnitedHealthcare. We conducted an A/B test using 67 MTurkers (randomly selected from US residents), investigating the level of awareness about PT legislation and the usability of available files. We also suggested format standardization, such as master field names using schema integration, to make machine-readable files consistent and usable for machines. RESULTS: The poor usability and inconsistent formats of the current PT information yielded no evidence of its usefulness for patients or its quality for machines. This indicates that the information does not meet the requirements for being consumer-friendly or machine readable as mandated by legislation. Based on the responses to the first part of the experiment (PT awareness), it was evident that participants need to be made aware of the PT legislation. However, they believe it is important to know the service price before receiving it. Based on the responses to the second part of the experiment (human usability of PT information), the average number of correct responses was not equal between the 2 groups, that is, the treatment group (mean 1.23, SD 1.30) found more correct answers than the control group (mean 2.76, SD 0.58; t65=6.46; P<.001; d=1.52). CONCLUSIONS: Consistent machine-readable files across all health systems facilitate the development of tools for estimating customer out-of-pocket costs, aligning with the PT rule's main objective-providing patients with valuable information and reducing health care expenditures.

Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.

BACKGROUND: Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughput. However, LC-PET is characterized by reduced photon-count levels resulting in low signal-to-noise ratio (SNR), segmentation difficulties, and quantification uncertainties. PURPOSE: We developed and evaluated a novel deep-learning (DL) architecture-Attention based Residual-Dilated Net (ARD-Net)-to generate standard-count PET (SC-PET) images from LC-PET images. The performance of the ARD-Net framework was evaluated for numerous low count realizations using fidelity-based qualitative metrics, task-based segmentation, and quantitative metrics. METHOD: Patient Derived tumor Xenograft (PDX) with tumors implanted in the mammary fat-pad were subjected to preclinical [18F]-Fluorodeoxyglucose (FDG)-PET/CT imaging. SC-PET images were derived from a 10 min static FDG-PET acquisition, 50 min post administration of FDG, and were resampled to generate four distinct LC-PET realizations corresponding to 10%, 5%, 1.6%, and 0.8% of SC-PET count-level. ARD-Net was trained and optimized using 48 preclinical FDG-PET datasets, while 16 datasets were utilized to assess performance. Further, the performance of ARD-Net was benchmarked against two leading DL-based methods (Residual UNet, RU-Net; and Dilated Network, D-Net) and non-DL methods (Non-Local Means, NLM; and Block Matching 3D Filtering, BM3D). The performance of the framework was evaluated using traditional fidelity-based image quality metrics such as Structural Similarity Index Metric (SSIM) and Normalized Root Mean Square Error (NRMSE), as well as human observer-based tumor segmentation performance (Dice Score and volume bias) and quantitative analysis of Standardized Uptake Value (SUV) measurements. Additionally, radiomics-derived features were utilized as a measure of quality assurance (QA) in comparison to true SC-PET. Finally, a performance ensemble score (EPS) was developed by integrating fidelity-based and task-based metrics. Concordance Correlation Coefficient (CCC) was utilized to determine concordance between measures. The non-parametric Friedman Test with Bonferroni correction was used to compare the performance of ARD-Net against benchmarked methods with significance at adjusted p-value ≤0.01. RESULTS: ARD-Net-generated SC-PET images exhibited significantly better (p ≤ 0.01 post Bonferroni correction) overall image fidelity scores in terms of SSIM and NRMSE at majority of photon-count levels compared to benchmarked DL and non-DL methods. In terms of task-based quantitative accuracy evaluated by SUVMean and SUVPeak, ARD-Net exhibited less than 5% median absolute bias for SUVMean compared to true SC-PET and lower degree of variability compared to benchmarked DL and non-DL based methods in generating SC-PET. Additionally, ARD-Net-generated SC-PET images displayed higher degree of concordance to SC-PET images in terms of radiomics features compared to non-DL and other DL approaches. Finally, the ensemble score suggested that ARD-Net exhibited significantly superior performance compared to benchmarked algorithms (p ≤ 0.01 post Bonferroni correction). CONCLUSION: ARD-Net provides a robust framework to generate SC-PET from LC-PET images. ARD-Net generated SC-PET images exhibited superior performance compared other DL and non-DL approaches in terms of image-fidelity based metrics, task-based segmentation metrics, and minimal bias in terms of task-based quantification performance for preclinical PET imaging.

FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool.

Objective.Simulation of positron emission tomography (PET) images is an essential tool in the development and validation of quantitative imaging workflows and advanced image processing pipelines. Existing Monte Carlo or analytical PET simulators often compromise on either efficiency or accuracy. We aim to develop and validate fast analytical simulator of tracer (FAST)-PET, a novel analytical framework, to simulate PET images accurately and efficiently.Approach. FAST-PET simulates PET images by performing precise forward projection, scatter, and random estimation that match the scanner geometry and statistics. Although the same process should be applicable to other scanner models, we focus on the Siemens Biograph Vision-600 in this work. Calibration and validation of FAST-PET were performed through comparison with an experimental scan of a National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Further validation was conducted between FAST-PET and Geant4 Application for Tomographic Emission (GATE) quantitatively in clinical image simulations in terms of intensity-based and texture-based features and task-based tumor segmentation.Main results.According to the NEMA IQ phantom simulation, FAST-PET's simulated images exhibited partial volume effects and noise levels comparable to experimental images, with a relative bias of the recovery coefficient RC within 10% for all spheres and a coefficient of variation for the background region within 6% across various acquisition times. FAST-PET generated clinical PET images exhibit high quantitative accuracy and texture comparable to GATE (correlation coefficients of all features over 0.95) but with ∼100-fold lower computation time. The tumor segmentation masks comparison between both methods exhibited significant overlap and shape similarity with high concordance CCC > 0.97 across measures.Significance.FAST-PET generated PET images with high quantitative accuracy comparable to GATE, making it ideal for applications requiring extensive PET image simulations such as virtual imaging trials, and the development and validation of image processing pipelines.

Preclinical validation of an Escherichia coli O-antigen glycoconjugate for the prevention of serotype O1 invasive disease.

A US collection of invasive Escherichia coli serotype O1 bloodstream infection (BSI) isolates were assessed for genotypic and phenotypic diversity as the basis for designing a broadly protective O-antigen vaccine. Eighty percent of the BSI isolate serotype O1 strains were genotypically ST95 O1:K1:H7. The carbohydrate repeat unit structure of the O1a subtype was conserved in the three strains tested representing core genome multi-locus sequence types (MLST) sequence types ST95, ST38, and ST59. A long-chain O1a CRM197 lattice glycoconjugate antigen was generated using oxidized polysaccharide and reductive amination chemistry. Two ST95 strains were investigated for use in opsonophagocytic assays (OPA) with immune sera from vaccinated animals and in murine lethal challenge models. Both strains were susceptible to OPA killing with O1a glycoconjugate post-immune sera. One of these, a neonatal sepsis strain, was found to be highly lethal in the murine challenge model for which virulence was shown to be dependent on the presence of the K1 capsule. Mice immunized with the O1a glycoconjugate were protected from challenges with this strain or a second, genotypically related, and similarly virulent neonatal isolate. This long-chain O1a CRM197 lattice glycoconjugate shows promise as a component of a multi-valent vaccine to prevent invasive E. coli infections. IMPORTANCE: The Escherichia coli serotype O1 O-antigen serogroup is a common cause of invasive bloodstream infections (BSI) in populations at risk such as newborns and the elderly. Sequencing of US BSI isolates and structural analysis of O polysaccharide antigens purified from strains that are representative of genotypic sub-groups confirmed the relevance of the O1a subtype as a vaccine antigen. O polysaccharide was purified from a strain engineered to produce long-chain O1a O-antigen and was chemically conjugated to CRM197 carrier protein. The resulting glycoconjugate elicited functional antibodies and was protective in mice against lethal challenges with virulent K1-encapsulated O1a isolates.