Estimates and measurements of radiofrequency exposures in smart-connected homes.

The aim of this research was to quantify the levels of radiofrequency electromagnetic energy (RF-EME) in a residential home/apartment equipped with a range of wireless devices, often referred to as internet of things (IoT) devices or smart devices and subsequently develop a tool that could be useful for estimating the levels of RF-EME in a domestic environment. Over the course of 3 years measurements were performed in peoples' homes on a total of 43 devices across 16 device categories. Another 12 devices were measured in detail in a laboratory setup. In all a total of 55 individual devices across 23 device categories were measured. Based on this measurement data we developed predictive software that showed that even with a single device in 23 device categories operating near maximum they would, in total, produce exposures at a distance of 1 m of 0.17% of the ICNIRP (2020) public exposure limits. Measurements were also made in two separate smart apartments-one contained over 50 IoT devices and a second with over 100 IoT devices with the devices driven as hard as could reasonably be achieved. The respective 6-min average exposure level recorded were 0.0077% and 0.44% of the ICNIRP (2020) 30-min average public exposure limit.

Assessment of Price Variation in Coronary Artery Bypass Surgery at US Hospitals.

BACKGROUND: Little is known about hospital pricing for coronary artery bypass grafting (CABG). Using new price transparency data, we assessed variation in CABG prices across US hospitals and the association between higher prices and hospital characteristics, including quality of care. METHODS AND RESULTS: Prices for diagnosis related group code 236 were obtained from the Turquoise database and linked by Medicare Facility ID to publicly available hospital characteristics. Univariate and multivariable analyses were performed to assess factors predictive of higher prices. Across 544 hospitals, median commercial and self-pay rates were 2.01 and 2.64 times the Medicare rate ($57 240 and $75 047, respectively, versus $28 398). Within hospitals, the 90th percentile insurer-negotiated price was 1.83 times the 10th percentile price. Across hospitals, the 90th percentile commercial rate was 2.91 times the 10th percentile hospital rate. Regional median hospital prices ranged from $35 624 in the East South Central to $84 080 in the Pacific. In univariate analysis, higher inpatient revenue, greater annual discharges, and major teaching status were significantly associated with higher prices. In multivariable analysis, major teaching and investor-owned status were associated with significantly higher prices (+$8653 and +$12 200, respectively). CABG prices were not related to death, readmissions, patient ratings, or overall Centers for Medicare and Medicaid Services hospital rating. CONCLUSIONS: There is significant variation in CABG pricing, with certain characteristics associated with higher rates, including major teaching status and investor ownership. Notably, higher CABG prices were not associated with better-quality care, suggesting a need for further investigation into drivers of pricing variation and the implications for health care spending and access.

Practice Consolidation Among US Medical Oncologists, 2015-2022.

PURPOSE: Health care consolidation has significantly affected cancer care delivery, with oncology practices undergoing substantial consolidation over the past two decades. This study investigates practice consolidation trends among medical oncologists (MOs), factors associated with consolidation, and changes in MO geographic distribution. METHODS: Medicare data from 2015 to 2022 were used to assess MO practice consolidation in hospital referral regions (HRRs), linked with regional health care market data and physician demographics. The Herfindahl-Hirschman Index (HHI) was used to measure consolidation, and the Gini coefficient was used to measure MO distribution across counties. Multivariable linear regression explored factors associated with MO practice consolidation. RESULTS: Between 2015 and 2022, the number of MOs increased by 14.5% (11,727-13,433), whereas the number of MO practices decreased by 18.0% (2,774-2,276). The mean number of MOs per practice increased by 40% (4.26-5.95; P < .001). The percentage of MOs in small practices decreased, whereas larger practices saw an increase. MO consolidation, as indicated by the HHI, increased by 9% (median HHI, 0.3204-0.3480). HRRs with higher baseline hospital consolidation and more hospital beds per capita were more likely to have MO practice consolidation. Despite MO practice consolidation, the county-level distribution of MOs did not change substantially. CONCLUSION: On the basis of Federal Trade Commission classifications, MO practices were highly concentrated in 2015 and consolidated even further by 2022. While distribution of MOs at the county level remained stable, further research is needed to assess the effects of rapid consolidation on cancer care cost, quality, and access. These data have important implications for policymakers and payers as they design programs that ensure high-quality, affordable cancer care.