Measuring Value in Primary Care: Enhancing Quality or Checking the Box?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109581/1/hesr12256-sup-0001-AuthorMatrix.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109581/2/hesr12256.pd

Association between health insurance literacy and avoidance of health care services owing to cost

Importance: Navigating health insurance and health care choices requires considerable health insurance literacy. Although recommended preventive services are exempt from out-of-pocket costs under the Affordable Care Act, many people may remain unaware of this provision and its effect on their required payment. Little is known about the association between individuals\u27 health insurance literacy and their use of preventive or nonpreventive health care services. Objective: To assess the association between health insurance literacy and self-reported avoidance of health care services owing to cost. Design, Setting, and Participants: In this survey study, a US national, geographically diverse, nonprobability sample of 506 US residents aged 18 years or older with current health insurance coverage was recruited to participate in an online survey between February 22 and 23, 2016. Main Outcomes and Measures: The validated 21-item Health Insurance Literacy Measure (HILM) assessed individuals\u27 self-rated confidence in selecting and using health insurance (score range, 0-84, with higher scores indicating greater levels of health insurance literacy). Dependent variables included delayed or foregone preventive and nonpreventive services in the past 12 months owing to perceived costs, and preventive and nonpreventive use of services. Covariates included age, sex, race/ethnicity, income, educational level, high-deductible health insurance plan, health literacy, numeracy, and chronic health conditions. Analyses included descriptive statistics and bivariate and multivariable logistic regression. Results: A total of 506 of 511 participants who began the survey completed it (participation rate, 99.0%). Of the 506 participants, 339 (67.0%) were younger than 35 years (mean [SD] age, 34 [10.4] years), 228 (45.1%) were women, 406 of 504 who reported race (80.6%) were white, and 245 (48.4%) attended college for 4 or more years. A total of 228 participants (45.1%) had 1 or more chronic health condition, 361 of 500 (72.2%) who responded to the survey item had seen a physician in the outpatient setting in the past 12 months, and 446 of the 501 (89.0%) who responded to the survey item had their health insurance plan for 12 or more months. One hundred fifty respondents (29.6%) reported having delayed or foregone care because of cost. The mean (SD) HILM score was 63.5 (12.3). In multivariable logistic regression, each 12-point increase in HILM score was associated with a lower likelihood of both delayed or foregone preventive care (adjusted odds ratio [aOR], 0.61; 95% CI, 0.48-0.78) and delayed or foregone nonpreventive care (aOR, 0.71; 95% CI, 0.55-0.91). Conclusions and Relevance: This study\u27s findings suggest that lower health insurance literacy may be associated with greater avoidance of both preventive and nonpreventive services. It appears that to improve appropriate use of recommended health care services, including preventive health services, clinicians, health plans, and policymakers may need to communicate health insurance concepts in accessible ways regardless of individuals\u27 health insurance literacy. Plain language communication may be able to improve patients\u27 understanding of services exempt from out-of-pocket costs

AN EMPIRICAL STUDY OF CONCURRENT FEATURE USAGE IN GO

The Go language includes support for running functions or methods concurrently as goroutines, which are lightweight threads managed directly by the Go language runtime. Go is probably best known for the use of a channel-based, message-passing concurrency mechanism, based on Hoare's Communicating Sequential Processes (CSP), for inter-thread communication. However, Go also includes support for traditional concurrency features, such as mutexes and condition variables, that are commonly used in other languages. In this paper, we analyze the use of these traditional concurrency features, using a corpus of Go programs used in earlier work to study the use of message-passing concurrency features in Go. The goal of this work is to better support developers in using traditional concurrency features, or a combination of traditional and message-passing features, in Go

Execution-based Code Generation using Deep Reinforcement Learning

The utilization of programming language (PL) models, pretrained on large-scale code corpora, as a means of automating software engineering processes has demonstrated considerable potential in streamlining various code generation tasks such as code completion, code translation, and program synthesis. However, current approaches mainly rely on supervised fine-tuning objectives borrowed from text generation, neglecting specific sequence-level features of code, including but not limited to compilability as well as syntactic and functional correctness. To address this limitation, we propose PPOCoder, a new framework for code generation that combines pretrained PL models with Proximal Policy Optimization (PPO) deep reinforcement learning and employs execution feedback as the external source of knowledge into the model optimization. PPOCoder is transferable across different code generation tasks and PLs. Extensive experiments on three code generation tasks demonstrate the effectiveness of our proposed approach compared to SOTA methods, improving the success rate of compilation and functional correctness over different PLs. Our code can be found at https://github.com/reddy-lab-code-research/PPOCoder

Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data

Determining clinically relevant physiological states from multivariate time series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.Comment: 10 pages, 7 figures, 2 table

Health Insurance Decision-Making Near Retirement

https://deepblue.lib.umich.edu/bitstream/2027.42/146766/1/NPHA-Health-Insurance-Report_FINAL_v2-122018.pd

The 1.6-Kv AlGaN/GaN HFETs

The breakdown voltages in unpassivated nonfield-plated AlGan/GaN HFETs on sapphire substrates were studied. These studies reveal that the breakdown is limited by the surface flashover rather than by the AlGan/GaN channel. after elimination of the surface flashover in air, the breakdown voltage scaled linearly with the gate-drain spacing reaching 1.6 kV at 20 mu m. The corresponding static ON-resistance was as low as 3.4 m Omega(.)cm(2). This translates to a power device figure-of-merit V-BR(2)/R-ON = 7.5 x 10(8) V-2 . n(-1) cm(-2), which, to date, is among the best reported values for an AlGan/GaN HFET

A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping

Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, and the existing approaches require imputing missing values before clustering, which may cause extensive computations and noise and result in invalid interpretations. To address these challenges, we present a Self-supervised Learning-based Approach to Clustering multivariate Time-series data with missing values (SLAC-Time). SLAC-Time is a Transformer-based clustering method that uses time-series forecasting as a proxy task for leveraging unlabeled data and learning more robust time-series representations. This method jointly learns the neural network parameters and the cluster assignments of the learned representations. It iteratively clusters the learned representations with the K-means method and then utilizes the subsequent cluster assignments as pseudo-labels to update the model parameters. To evaluate our proposed approach, we applied it to clustering and phenotyping Traumatic Brain Injury (TBI) patients in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study. Our experiments demonstrate that SLAC-Time outperforms the baseline K-means clustering algorithm in terms of silhouette coefficient, Calinski Harabasz index, Dunn index, and Davies Bouldin index. We identified three TBI phenotypes that are distinct from one another in terms of clinically significant variables as well as clinical outcomes, including the Extended Glasgow Outcome Scale (GOSE) score, Intensive Care Unit (ICU) length of stay, and mortality rate. The experiments show that the TBI phenotypes identified by SLAC-Time can be potentially used for developing targeted clinical trials and therapeutic strategies.Comment: Submitted to the Journal of Biomedical Informatic

Digital Oxide Deposition of SiO\u3csub\u3e2\u3c/sub\u3e Layers for III-Nitride Metal-Oxide-Semiconductor Heterostructure Field-Effect Transistors

We present a digital-oxide-deposition (DOD) technique to deposit high quality SiO2dielectric layers by plasma-enhanced chemical vapor deposition using alternate pulses of silicon and oxygen precursors. The DOD procedure allows for a precise thickness control and results in extremely smooth insulating SiO2 layers. An insulating gate AlGaN∕GaNheterostructurefield-effect transistor(HFET) with 8nm thick DOD SiO2dielectric layer had a threshold voltage of −6V (only 1V higher than that of regular HFET), very low threshold voltage dispersion, and output continuous wave rf power of 15W∕mm at 55V drain bias

Silicon Dioxide-Encapsulated High-Voltage AlGaN/GaN HFETs for Power-Switching Applications

In this letter, new approach in achieving high breakdown voltages in AlGan/GaN heterostructure field-effect transistors (HFETs) by suppressing surface flashover using solid encapsulation material is presented. Surface flashover in III-Nitride-based HFETs limits the operating voltages at levels well below breakdown voltages of GaN. This premature gate-drain breakdown can be suppressed by immersing devices in high-dielectric-strength liquids (e.g., Fluorinert); however, such a technique is not practical. In this letter, AlGan/GaN HFETs encapsulated with PECVD-deposited SiO2 films demonstrated breakdown voltage of 900 V, very similar to that of devices immersed in Fluorinert liquid. Simultaneously, low dynamic ON-resistance of 2.43 m Omega. cm(2) has been achieved, making the developed AlGan/GaN HFETs practical high-voltage high-power switches for power-electronics applications