Background: Deep learning has the potential to augment the use of chest radiography in clinical radiology, but challenges include poor generalizability, spectrum bias, and difficulty comparing across studies.Purpose: To develop and evaluate deep learning models for chest radiograph interpretation by using radiologist-adjudicated reference standards. Materials and Methods:Deep learning models were developed to detect four findings (pneumothorax, opacity, nodule or mass, and fracture) on frontal chest radiographs. This retrospective study used two data sets. Data set 1 (DS1) consisted of 759 611 images from a multicity hospital network and ChestX-ray14 is a publicly available data set with 112 120 images. Natural language processing and expert review of a subset of images provided labels for 657 954 training images. Test sets consisted of 1818 and 1962 images from DS1 and ChestX-ray14, respectively. Reference standards were defined by radiologist-adjudicated image review. Performance was evaluated by area under the receiver operating characteristic curve analysis, sensitivity, specificity, and positive predictive value. Four radiologists reviewed test set images for performance comparison. Inverse probability weighting was applied to DS1 to account for positive radiograph enrichment and estimate population-level performance.

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Federal healthcare spending has been a subject of intense concern as the US Congress continues to search for ways to reduce the budget deficit. The Congressional Budget Office (CBO) estimated that, even though it is growing more slowly than previously projected, federal spending on Medicare, Medicaid and the State Children's Health Insurance Program (SCHIP) will reach nearly $900 billion in 2013. In 2011 the Medicare program paid $68 billion for physicians and other health professional services, 12% of total Medicare spending. Since 2002 the sustainable growth rate (SGR) correction has called for reductions to physician reimbursements; however, Congress has typically staved off these reductions, although the situation remains precarious for physicians who accept Medicare. The fiscal cliff agreement that came into focus at the end of 2012 averted a 26.5% reduction to physician reimbursements related to the SGR correction. Nonetheless, the threat of these devastating cuts continues to loom. The Administration, Congress and others have devised many options to fix this unsustainable situation. This review explores the historical development of the SGR, touches on elements of the formula itself and outlines current proposals for fixing the SGR problem. A recent CBO estimate reduces the potential cost of a 10-year fix of SGR system to $138 billion. This has provided new hope for resolution of this long-standing issue.

BackgroundThe number of health-related wearable devices is growing but it is not clear if Americans are willing to adopt health insurance wellness programs based on wearables and the incentives with which they would be more willing to adopt.MethodsIn this cross-sectional study we used a survey methodology, usage vignettes and a dichotomous scale to examine U.S. residents’ willingness to adopt wearables (WTAW) in six use-cases where it was mandatory to use a wearable device and share the resulting data with a health insurance company. Each use-case was tested also for the influence of additional economic incentives on WTAW.ResultsA total of 997 Americans across 46 states participated in the study. Most of them were 25 to 34 years old (40.22%), 57.27% were female, and 74.52% were white. On average, 69.5% of the respondents were willing to adopt health-insurance use-cases based on wearable devices, though 77.8% of them were concerned about issues related to economic benefits, data privacy and to a lesser extent, technological accuracy. WTAW was 11–18% higher among consumers in use-cases involving health promotion and disease prevention. Furthermore, additional economic incentives combined with wearables increased WTAW overall. Notably, financial incentives involving providing healthcare credits, insurance premium discount, and/or wellness product discounts had particularly greater effectiveness for increasing WTAW in the consumer use-cases involving participation: for health promotion (RR = 1.06 for financial incentive, 95% CI: 1.01–1.11; P = 0.018); for personalized products and services (RR = 1.11 for financial incentive, 95% CI: 1.01–1.21; P = 0.018); and for automated underwriting discount at annual renewal (RR = 1.28 for financial incentive, 95% CI: 1.20–1.37; P < 0.001).ConclusionsUnder the adequate economic, data privacy and technical conditions, 2 out of 3 Americans would be willing to adopt health insurance wellness programs based on wearable devices, particularly if they have benefits related to health promotion and disease prevention, and particularly with financial incentives.

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