RWD-Cockpit: Application for Quality Assessment of Real-world Data.

BACKGROUND: Digital technologies are transforming the health care system. A large part of information is generated as real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient-stratification principles, expose unknown disease correlations, and inform on preventive measures. The impact for health care payers and providers, the biopharmaceutical industry, and governments is massive in terms of health outcomes, quality of care, and cost. However, a framework to assess the preliminary quality of RWD is missing, thus hindering the conduct of population-based observational studies to support regulatory decision-making and real-world evidence. OBJECTIVE: To address the need to qualify RWD, we aimed to build a web application as a tool to translate characterization of some quality parameters of RWD into a metric and propose a standard framework for evaluating the quality of the RWD. METHODS: The RWD-Cockpit systematically scores data sets based on proposed quality metrics and customizable variables chosen by the user. Sleep RWD generated de novo and publicly available data sets were used to validate the usability and applicability of the web application. The RWD quality score is based on the evaluation of 7 variables: manageability specifies access and publication status; complexity defines univariate, multivariate, and longitudinal data; sample size indicates the size of the sample or samples; privacy and liability stipulates privacy rules; accessibility specifies how the data set can be accessed and to what granularity; periodicity specifies how often the data set is updated; and standardization specifies whether the data set adheres to any specific technical or metadata standard. These variables are associated with several descriptors that define specific characteristics of the data set. RESULTS: To address the need to qualify RWD, we built the RWD-Cockpit web application, which proposes a framework and applies a common standard for a preliminary evaluation of RWD quality across data sets-molecular, phenotypical, and social-and proposes a standard that can be further personalized by the community retaining an internal standard. Applied to 2 different case studies-de novo-generated sleep data and publicly available data sets-the RWD-Cockpit could identify and provide researchers with variables that might increase quality. CONCLUSIONS: The results from the application of the framework of RWD metrics implemented in the RWD-Cockpit application suggests that multiple data sets can be preliminarily evaluated in terms of quality using the proposed metrics. The output scores-quality identifiers-provide a first quality assessment for the use of RWD. Although extensive challenges remain to be addressed to set RWD quality standards, our proposal can serve as an initial blueprint for community efforts in the characterization of RWD quality for regulated settings.

Evaluation of the Potential of Automatic Naming Latency Detection for Different Initial Phonemes during Picture Naming Task.

Naming latency (NL) represents the speech onset time after the presentation of an image. We recently developed an extended threshold-based algorithm for automatic NL (aNL) detection considering the envelope of the speech wave. The present study aims at exploring the influence of different manners (e.g., "m" and "p") and positions (e.g., "t" and "p") of articulation on the differences between manual NL (mNL) and aNL detection.Speech samples were collected from 123 healthy participants. They named 118 pictures in German, including different initial phonemes. NLs were manually (Praat, waveform and spectrogram) and automatically (developed algorithm) determined. To investigate the accuracy of automatic detections, correlations between mNLs and aNLs were analyzed for different initial phonemes.ANLs and mNLs showed a strong positive correlation and similar tendencies in initial phoneme groups. ANL mean values were shorter than the ones of mNLs. Nasal sounds (e.g., /m/) showed the largest and those for fricatives (e.g., /s/) the smallest difference. However, in fricatives, 39% of NLs were detected later by automatic detections than by manual detections, which led to a reduced mean difference with mNLs. The signal energy of the initial phonemes, i.e., if they are voiced or voiceless, influences the form of the speech envelope: initial high signal energy is often responsible for an early detection by the algorithm.Our study provides evidence of a similar tendency in mNL and aNL according to different positions of articulation in each initial phoneme group. ANLs are highly sensitive to detection of speech onsets across different initial phonemes. The dependency of the NL differences on the initial phonemes will lose importance during progress evaluations in aphasia patients if the relative changes for each picture are considered separately. Nevertheless, the algorithm will be further optimized by adapting its parameters for each initial phoneme group individually.Clinical Relevance- This underlines the feasibility to use automatic naming latency detection for the evaluation of patients with aphasia in a clinical setting as well as for practices at home during picture naming.

The Energy Consumption of Radiology: Energy- and Cost-saving Opportunities for CT and MRI Operation.

Background Awareness of energy efficiency has been rising in the industrial and residential sectors but only recently in the health care sector. Purpose To measure the energy consumption of modern CT and MRI scanners in a university hospital radiology department and to estimate energy- and cost-saving potential during clinical operation. Materials and Methods Three CT scanners, four MRI scanners, and cooling systems were equipped with kilowatt-hour energy measurement sensors (2-Hz sampling rate). Energy measurements, the scanners' log files, and the radiology information system from the entire year 2015 were analyzed and segmented into scan modes, as follows: net scan (actual imaging), active (room time), idle, and system-on and system-off states (no standby mode was available). Per-examination and peak energy consumption were calculated. Results The aggregated energy consumption imaging 40 276 patients amounted to 614 825 kWh, dedicated cooling systems to 492 624 kWh, representing 44.5% of the combined consumption of 1 107 450 kWh (at a cost of U.S. $199 341). This is equivalent to the usage in a town of 852 people and constituted 4.0% of the total yearly energy consumption at the authors' hospital. Mean consumption per CT examination over 1 year was 1.2 kWh, with a mean energy cost (±standard deviation) of $0.22 ± 0.13. The total energy consumption of one CT scanner for 1 year was 26 226 kWh ($4721 in energy cost). The net consumption per CT examination over 1 year was 3580 kWh, which is comparable to the usage of a two-person household in Switzerland; however, idle state consumption was fourfold that of net consumption (14 289 kWh). Mean MRI consumption over 1 year was 19.9 kWh per examination, with a mean energy cost of $3.57 ± 0.96. The mean consumption for a year in the system-on state was 82 174 kWh per MRI examination and 134 037 kWh for total consumption, for an energy cost of $24 127. Conclusion CT and MRI energy consumption is substantial. Considerable energy- and cost-saving potential is present during nonproductive idle and system-off modes, and this realization could decrease total cost of ownership while increasing energy efficiency. © RSNA, 2020.

Comparison of a Low-Cost Miniature Inertial Sensor Module and a Fiber-Optic Gyroscope for Clinical Balance and Gait Assessments.

Objective: To investigate whether a microelectromechanical system (MEMS) inertial sensor module is as accurate as fiber-optic gyroscopes when classifying subjects as normal for clinical stance and gait balance tasks. Methods: Data of ten healthy subjects were recorded simultaneously with a fiber-optic gyroscope (FOG) system of SwayStar™ and a MEMS sensor system incorporated in the Valedo® system. Data from a sequence of clinical balance tasks with different angle and angular velocity ranges were assessed. Paired t-tests were performed to determine significant differences between measurement systems. Cohen's kappa test was used to determine the classification of normal balance control between the two sensor systems when comparing the results to a reference database recorded with the FOG system. Potential cross-talk errors in roll and pitch angles when neglecting yaw axis rotations were evaluated by comparing 2D FOG and 3D MEMS recordings. Results: Statistically significant (α=0.05) differences were found in some balance tasks, for example, "walking eight tandem steps" and various angular measures (p < 0.03). However, these differences were within a few percent (<2.7%) of the reference values. Tasks with high dynamic velocity ranges showed significant differences (p=0.002) between 2D FOG and 3D MEMS roll angles but no difference between 2D FOG and 2D MEMS roll angles. An almost perfect agreement could be obtained for both 2D FOG and 2D MEMS (κ=0.97) and 2D FOG and 3D MEMS measures (κ=0.87) when comparing measurements of all subjects and tasks. Conclusion: MEMS motion sensors can be used for assessing balance during clinical stance and gait tasks. MEMS provides measurements comparable to values obtained with a highly accurate FOG. When assessing pitch and roll trunk sway measures without accounting for the effect of yaw, it is recommended to use angle and angular velocity measures for stance, and only angular velocity measures for gait because roll and pitch velocity measurements are not influenced by yaw rotations, and angle errors are low for stance.

Making the Radiology Workflow Visible in Order to Inform Optimization Strategies.

Medical imaging is undergoing rapid change, induced by the increasing amount of image data, and advances in fields such as artificial intelligence. In order for a radiology service provider to respond to these challenges, it needs to adapt its workflow. To inform optimization strategies, the way that processes and resources interact in the real world must be understood. We report on our experiences with an approach that consists of merging a variety of data sources into a data model that allows efficient interactive queries, and then providing highly interactive visualizations to explore the data. Two examples are discussed: animation of patient flow through the radiology workflow, and the use of energy consumption patterns to characterize operational modalities.