Search

Background: Patients with limited English proficiency frequently receive substandard health care. Asynchronous telepsychiatry (ATP) has been established as a clinically valid method for psychiatric assessments. The addition of automated speech recognition (ASR) and automated machine translation (AMT) technologies to asynchronous telepsychiatry may be a viable artificial intelligence (AI)-language interpretation option. Objective: This project measures the frequency and accuracy of the translation of figurative language devices (FLDs) and patient word count per minute, in a subset of psychiatric interviews from a larger trial, as an approximation to patient speech complexity and quantity in clinical encounters that require interpretation. Methods: A total of 6 patients were selected from the original trial, where they had undergone 2 assessments, once by an English-speaking psychiatrist through a Spanish-speaking human interpreter and once in Spanish by a trained mental health interviewer-researcher with AI interpretation. 3 (50%) of the 6 selected patients were interviewed via videoconferencing because of the COVID-19 pandemic. Interview transcripts were created by automated speech recognition with manual corrections for transcriptional accuracy and assessment for translational accuracy of FLDs. Results: AI-interpreted interviews were found to have a significant increase in the use of FLDs and patient word count per minute. Both human and AI-interpreted FLDs were frequently translated inaccurately, however FLD translation may be more accurate on videoconferencing. Conclusions: AI interpretation is currently not sufficiently accurate for use in clinical settings. However, this study suggests that alternatives to human interpretation are needed to circumvent modifications to patients' speech. While AI interpretation technologies are being further developed, using videoconferencing for human interpreting may be more accurate than in-person interpreting. [ABSTRACT FROM AUTHOR]

A detailed analysis is performed to better understand the interannual and subseasonal variability of moisture sources of major recent dry (1980, 1990, and 2000) and wet (1994, 2003, and 2005) June–August (JJA) seasons in the southeastern United States. Wet (dry) JJAs show an increased (decreased) standard deviation of daily precipitation. Whereas most days during dry JJAs have little or no precipitation, wet JJAs contain more days with significant precipitation and a large increase of heavy (+10 mm) precipitation days. At least two tropical cyclone/depression landfalls occur in the southeastern United States during wet JJAs, whereas none occur during dry JJAs. The trajectory analysis suggests significant local recycling of moisture, implying that land surface feedback has the potential to enhance (suppress) precipitation anomalies during a wet (dry) JJA. Remote moisture sources during heavy precipitation events are very similar between wet and dry JJAs. The distinction between wet and dry JJAs lies in the frequency of heavy precipitation events. During the wet JJAs, heavy precipitation events contribute to more than half of the JJA precipitation total. [ABSTRACT FROM AUTHOR]

Based on 1-km land surface model geophysical predictions within the United States Southern Great Plains (Red-Arkansas River basin), an observing system simulation experiment (OSSE) is carried out to assess the impact of land surface heterogeneity, instrument error, and parameter uncertainty on soil moisture products derived from the National Aeronautics and Space Administration Hydrosphere State (Hydros) mission. Simulated retrieved soil moisture products are created using three distinct retrieval algorithms based on the characteristics of passive microwave measurements expected from Hydros. The accuracy of retrieval products is evaluated through comparisons with benchmark soil moisture fields obtained from direct aggregation of the original simulated soil moisture fields. The analysis provides a quantitative description of how land surface heterogeneity, instrument error, and inversion parameter uncertainty impacts propagate through the measurement and retrieval process to degrade the accuracy of Hydros soil moisture products. Results demonstrate that the discrete set of error sources captured by the OSSE induce root mean squared errors of between 2.0% and 4.5 % volumetric in soil moisture retrievals within the basin. Algorithm robustness is also evaluated for the case of artificially enhanced vegetation water content (W) values within the basin. For large W(> 3 kg . m-2), a distinct positive bias, attributable to the impact of sub-footprint-scale landcover heterogeneity, is identified in soil moisture retrievals. Prospects for the removal of this bias via a correction strategy for inland water and/or the implementation of an alternative aggregation strategy for surface vegetation and roughness parameters are discussed. [ABSTRACT FROM AUTHOR]

The uncertainty of surface soil moisture (SM) retrievals from satellite brightness temperature (TB) observations depends primarily on the choice of radiative transfer model (RTM) parameters, prior SM information and TB inputs. This paper studies the sensitivity of several established and experimental SM retrieval products from the Soil Moisture Ocean Salinity (SMOS) mission to these choices at 11 reference sites, located in 7 watersheds across the United States (US). Different RTM parameter sets cause large biases between retrievals. Whereas typical RTM parameter sets are calibrated for SM retrievals, it is shown that a parameter set carefully optimized for TB forward modeling can also be used for retrieving SM. It is also shown that the inclusion of dynamic prior SM estimates in a Bayesian retrieval scheme can strongly improve SM retrievals, regardless of the choice of RTM parameters. The second part of this paper evaluates the ensemble uncertainty metrics for SM retrievals obtained by propagating a wide range of RTM parameters through the RTM, and the relationship with time series metrics obtained by comparing SM retrievals with in situ data. As expected for bounded variables, the total spread in the ensemble SM retrievals is smallest for wet and dry SM values and highest for intermediate SM values. After removal of the strong long-term SM bias associated with the RTM parameter values for individual ensemble members, the remaining anomaly ensemble SM spread shows higher values when SM deviates further from its long-term mean SM. This reveals higher-order biases (e.g. differences in variances) in the retrieval error, which should be considered when characterizing retrieval error. The time-average anomaly ensemble SM spread of 0.037 m3/m3 approximates the actual time series unbiased root-mean-square-difference of 0.042 m3/m3 between ensemble mean retrievals and in situ data across the reference sites. • Wetter SM retrievals for higher roughness and vegetation parameter values • Good SM retrievals possible with RTM parameters calibrated for TB forward modeling • Improved SM retrievals with inclusion of model-based prior SM estimates • Similar SM mean - variance relationship sampled in time, space, or across an ensemble • Higher-order biases in SM retrieval error after long-term mean removal [ABSTRACT FROM AUTHOR]

Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Recent satellite missions measuring soil moisture from space continue to improve the availability of soil moisture information. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. This study presents a merging framework that combines a hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution remotely sensed hydrological variables to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). The framework was demonstrated for soil moisture by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission. The brightness temperature from the HydroBlocks-RTM and SMAP L3 were merged to obtain updated 30-m soil moisture. We validated the downscaled soil moisture estimates at four experimental watersheds with dense in-situ soil moisture networks in the United States and obtained overall high correlations (> 0.81) and good mean KGE score (0.56). The downscaled product captures the spatial and temporal soil moisture dynamics better than SMAP L3 and L4 product alone at both field and watershed scales. Our results highlight the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications. • Hyper-resolution land surface model improves field-scale soil moisture estimates • Hyper-resolution heterogeneity leverages the soil moisture spatial variability • HRUs allow for computationally efficient merging of remote sensing observations • The merging skill is sensitive to biases in the model and satellite estimates [ABSTRACT FROM AUTHOR]

The app evaluation framework of the American Psychiatric Association (APA) has emerged as an adaptable tool for those seeking to navigate the ever-growing space of mental health apps. The authors describe a meeting convened in December 2019 to refine the APA framework. The expert panel comprised 16 individuals across health care fields, with representation from psychiatry, psychology, social work, nursing, clinical informatics, peer support specialists, and individuals with lived mental health experience. This meeting resulted in an update to the APA framework with the addition of clearer question descriptions and the release of an eight-question screener that may be useful in clinical settings.

What is at stake for dentists in the world of social media? Because it is unrealistic to completely avoid the new network, dentists should master some of these skills: risk management, crises management, and reputation management, as well as understanding that the playing field is not even. Guidelines for professional use of media are presented, along with some suggestions for effective participation.

Study Objective: Previous studies have suggested that QTc prolongation may lead to significant morbidity and mortality. The prevalence of QTc prolongation among emergency department (ED) patients is unknown. The purpose of this study is to determine the prevalence of QTc prolongation among ED patients., Methods: This was a retrospective review of ED and inpatient data for all patients with an ECG conducted for any reason at a tertiary care university ED during a 3-month period. QTc prolongation was defined as computer-generated QTc intervals greater than or equal to 450 ms for men and greater than or equal to 460 ms for women., Results: Of the 1,558 eligible cases, 544 patients had QTc prolongation (35%; 95% confidence interval [CI] 32% to 37%). The prevalence of QTc intervals greater than or equal to 500 ms was 8% (120/1,558; 95% CI 6% to 9%). The most common comorbidities were structural heart disease, renal failure, and stroke. Forty-four percent (239/544; 95% CI 40% to 48%) of patients with any degree of QTc prolongation were discharged from the ED. Furthermore, 23% (28/120; 95% CI 16% to 32%) of patients with QTc intervals greater than or equal to 500 ms were discharged from the ED, including 16 patients with QTc intervals greater than or equal to 500 ms and QRS durations less than 120 ms (16/60; 27%; 95% CI 16% to 40%). Five percent of the patients with QTc prolongation died in the ED or during hospitalization (27/544; 95% CI 3% to 7%); none had QTc prolongation or torsades de pointes listed as a cause of death., Conclusion: QTc prolongation occurred frequently among ED patients who had an ECG study for any reason. Nearly half of all patients with QTc prolongation were discharged from the ED.