Speech recognition for medical documentation: an analysis of time, cost efficiency and acceptance in a clinical setting

Background/Aims Medical documentation is an important and unavoidable part of a health professional's working day. However, the time required for medical documentation is often viewed negatively, particularly by clinicians with heavy workloads. Digital speech recognition has become more prevalent and is being used to optimise working time. This study evaluated the time and cost savings associated with speech recognition technology, and its potential for improving healthcare processes. Methods Clinicians were directly observed while completing medical documentation. A total of 313 samples were collected, of which 163 used speech recognition and 150 used typing methods. The time taken to complete the medical form, the error rate and error correction time were recorded. A survey was also completed by 31 clinicians to gauge their level of acceptance of speech recognition software for medical documentation. Two-sample t-tests and Mann–Whitney U tests were performed to determine statistical trends and significance. Results On average, medical documentation using speech recognition software took just 5.11 minutes to complete the form, compared to 8.9 minutes typing, representing significant time savings. The error rate was also found to be lower for speech recognition software. However, 55% of clinicians surveyed stated that they would prefer to type their notes rather than use speech recognition software and perceived the error rate of this software to be higher than typing. Conclusions The results showed that there are both temporal and financial advantages of speech recognition technology over text input for medical documentation. However, this technology had low levels of acceptance among staff, which could have implications for the uptake of this method.

Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction

The climate change may be mitigated, and intra air quality assessment and local human well-being can benefit from a decrease in emission of pollutant content in the air. Monitoring the quality of the air around us is one way to do this. However, a location with various emission sources and short-term fluctuations in emissions in both time and space, and changes in winds, temperature, and precipitation creates a complex and variable pollution concentration field in the atmosphere. Therefore, based on the time and location where the sample is obtained, the measurement conducted are reflected in the monitoring results. This study aims to investigate one of India's most polluted cities' air quality measurements by greenhouse gas emissions. Using the Mann-Kendall and Sen's slope estimators, the research piece gives a statistical trend analysis of several air contaminants based on previous pollution data from Mumbai, India's air quality index station. In addition, future levels of air pollution may be correctly forecasted using an autoregressive integrated moving average model. This is followed by comparing different air quality standards and forecasts for future air pollution levels.

Depression with Mixed Features (for DSM-5): Distinguishing Indicators Imaginary Coherence EEG Rest

Background: The neuronal correlates of depression with mixed traits (according to DSM-5) at rest have not been studied. Objective: to determine the indicators of imaginary coherence of EEG-rest, which distinguish patients with depression with mixed features (according to DSM-5) from patients with depression without mixed features and healthy subjects, and also to trace the dependence of the identified neurophysiological characteristics on the diagnostic belonging of the symptom complex to bipolar II type or recurrent depressive disorder. Patients and methods: on a background free from drug therapy, 80 patients with depression with mixed features (XD; n = 40 — with bipolar II disorder (XB) and n = 40 — with recurrent depressive disorder (XR)), 80 patients with depression without mixed traits (TD; n = 40 — for bipolar II type (TB) and n = 40 — for recurrent depressive disorder (TR), as well as 80 healthy subjects (N). The study groups were matched by sex and age. The study used clinical-psychopathological, psychometric, neurophysiological and statistical research methods. According to the Kruskal–Wallis criterion for independent samples the parameters of imaginary coherence (modulo) of standard frequency ranges (delta (δ) — 0.5–4 Hz, theta (θ) — 4–8 Hz, alpha (α) — 8–13 Hz, beta-1 (β1) — 13–20 Hz, beta-2 (β2) — 20–30 Hz, gamma (γ) — 30–45 Hz) between pairs of 14 cutaneous standard EEG derivations (according to the “10–20” system) in three (XD, TD and N), and then in five (XB, XR, TB, TR and N) comparison groups. Post-hoc analysis was performed using the U-test. The significance level was adjusted according to the Bonferroni correction. Results: three indicators were identified: α-ICoh(C3–P4), β1-ICoh(C3–P3) and β2-ICoh(F3–C4). For all three parameters, the H-test values for the “Group” factor (n = 3 and n = 5) were highly significant. In this case, α-ICoh(C3– P4) — XD = TD, XD < N, TD < N; β1-ICoh(C3–P3) — XD < TD, XD < N; TD < N; β2-ICoh(F3–C4) — XD > TD; XD > N, TD > N. The groups of patients with XD within the framework of recurrent depressive and bipolar II disorders significantly differed in terms of β1- ICoh(C3–P3) — XR > XB. At the level of statistical trends, in type II bipolar disorder — XB > TB according to α-ICoh(C3–P4), and in recurrent depressive disorder — XR > TR according to β2-ICoh(F3–C4). Conclusion. Thus, depression with mixed features can be considered in terms of dysfunctional interactions of the left frontal, bilateral central and parietal cortical zones, depending on the diagnostic affiliation of the depressive symptom complex and reflecting violations of automatic and voluntary regulation of affect, cognitive and behavioral changes.

Two-Stage Hybrid Machine Learning Model for High-Frequency Intraday Bitcoin Price Prediction Based on Technical Indicators, Variational Mode Decomposition, and Support Vector Regression

Due to the inherent chaotic and fractal dynamics in the price series of Bitcoin, this paper proposes a two-stage Bitcoin price prediction model by combining the advantage of variational mode decomposition (VMD) and technical analysis. VMD eliminates the noise signals and stochastic volatility in the price data by decomposing the data into variational mode functions, while technical analysis uses statistical trends obtained from past trading activity and price changes to construct technical indicators. The support vector regression (SVR) accepts input from a hybrid of technical indicators (TI) and reconstructed variational mode functions (rVMF). The model is trained, validated, and tested in a period characterized by unprecedented economic turmoil due to the COVID-19 pandemic, allowing the evaluation of the model in the presence of the pandemic. The constructed hybrid model outperforms the single SVR model that uses only TI and rVMF as features. The ability to predict a minute intraday Bitcoin price has a huge propensity to reduce investors’ exposure to risk and provides better assurances of annualized returns.

Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters

In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day- and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although predictive ability declined when sequences of missing data occurred. Precision also depended on the availability of other water quality covariates. When covariates were available, even a sudden, event-based peak in nitrate concentration was reconstructed well. By providing a promising method for accurate prediction of missing data, the utility and confidence in summary statistics and statistical trends will increase, thereby assisting the effective monitoring and management of fresh waters and other at-risk ecosystems.

Ion-driven Instabilities in the Inner Heliosphere. I. Statistical Trends

Instabilities described by linear theory characterize an important form of wave–particle interaction in the solar wind. We diagnose unstable behavior of solar wind plasma between 0.3 and 1 au via the Nyquist criterion, applying it to fits of ∼1.5M proton and α particle Velocity Distribution Functions (VDFs) observed by Helios I and II. The variation of the fraction of unstable intervals with radial distance from the Sun is linear, signaling a gradual decline in the activity of unstable modes. When calculated as functions of the solar wind velocity and Coulomb number, we obtain more extreme, exponential trends in the regions where collisions appear to have a notable influence on the VDF. Instability growth rates demonstrate similar behavior, and significantly decrease with Coulomb number. We find that for a nonnegligible fraction of observations, the proton beam or secondary component might not be detected, due to instrument resolution limitations, and demonstrate that the impact of this issue does not affect the main conclusions of this work.

The influence of climate change on discharge fluctuations in Slovenian rivers

In recent decades, an increase in the number of extreme flood events as well as extreme drought events has been observed in Slovenia. This rise the need for a comprehensive analysis of trends in discharge data series. In the study, statistical trends in seasonal and annual mean, maximum, extreme and low discharge values were investigated using the Mann Kendall test. The results show a temporal and spatial variability of trends in discharge. In general, a decreasing trend in water quantities in the rivers was observed. However, results at some gauging stations indicate statistically significant increasing trends, especially for maximum and extreme discharges. Additional analyses show that the discharge trends depend on the location of the gauging station.

Factors influencing performance on the Board Certified Psychiatric Pharmacist Examination: Passing rates and domain-level scores

The Board Certified Psychiatric Pharmacist (BCPP) specialty certification was launched by the Board of Pharmacy Specialties in 1994. Candidates for the BCPP can qualify for the examination through 3 possible pathways: practice experience (4 years) in the specialty, completion of a PGY-1 residency plus an additional 2 years of practice experience, or completion of a PGY-2 specialty residency in psychiatric pharmacy. Recent fluctuations in the passing rate raised questions as to explanatory factors. This article represents the first published comprehensive study of candidate performance on the BCPP Examination. It describes a retrospective, observational study presenting (a) statistical trends of examination passing rates for biannual cohorts over the past 5 years, as well as (b) score distributions on the 3 performance domains of the certification. Pass-rate trend analyses suggest that variation in the proportion of eligibility pathway cohorts in the respective testing samples explains some of the fluctuation in passing rates. An analysis of variance of domain-level scores, using groups defined by eligibility pathway, yielded significant differences for nearly all group comparisons. Evaluation of the effect sizes suggest that the most disparate performance was observed on the core clinical domain, Patient-Centered Care. The results of this study are consistent with previously published research and will inform the upcoming role delineation study for the Psychiatric Pharmacy Certification.

Architecture of Hierarchical Stellar Systems and Their Formation

Accumulation of new data on stellar hierarchical systems and the progress in numerical simulations of their formation open the door to genetic classification of these systems, where properties of a certain group (family) of objects are tentatively related to their formation mechanisms and early evolution. A short review of the structure and statistical trends of known stellar hierarchies is given. Like binaries, they can be formed by the disk and core fragmentation events happening sequentially or simultaneously and followed by the evolution of masses and orbits driven by continuing accretion of gas and dynamical interactions between stars. Several basic formation scenarios are proposed and associated qualitatively with the architecture of real systems, although quantitative predictions for these scenarios are still pending. The general trend of increasing orbit alignment with decreasing system size points to the critical role of the accretion-driven orbit migration, which also explains the typically comparable masses of stars belonging to the same system. The architecture of some hierarchies bears imprints of chaotic dynamical interactions. Characteristic features of each family are illustrated by several real systems.

Assessment of Meteorological and Agricultural Drought Occurrence in Central Poland in 1961–2020 as an Element of the Climatic Risk to Crop Production

The results of numerous studies concerning meteorological drought show that there is a considerable impact of this phenomenon on several regions in Europe. On the other hand, statistical trends of dry spell occurrences in some areas of the continent are unclear or even negative. Therefore, further research should be directed towards a better understanding of this hazard, particularly the seasonal changes, in order to elaborate adequate strategies to prevent and mitigate its undesirable effects. The main goal of the work, conducted as part of the research strategy on contemporary climate change, was to confirm the hypothesis of increasing frequency and intensity of droughts during the period of active plant growth and development (May–August) in central Poland in 1961–2020. The prevailing rainfall conditions in this period determine the production and economic effects of agricultural output. The analysis covered a multiannual period, including two separate climate normals: 1961–1990 and 1991–2020. The work is also aimed at detecting relationships between indicators characterizing meteorological drought (the Standardized Precipitation Index—SPI) and agricultural drought (the actual precipitation deficiency—PAdef). It was found that the frequency of meteorological droughts in the studied period amounts to 30.0% (severe and extreme constitute 6.7%). No significant increase in the frequency and intensity of meteorological droughts over time was observed. Relationships between meteorological and agricultural drought indicators were significant, so the SPI can be considered an indicator of plant irrigation needs in the studied area.