The effects of L1 literacy and education on point-gain in an adult Esl course

This study examined factors affecting learners\u27 progress as measured by positive point gains between pre- and posttest reading proficiency scores for an intermediate/advanced adult ESL course. Background and self-assessment information, gathered through individual student interviews, was analyzed with reference to point gains to determine the influence of individual factors on students\u27 progress. Due to limitations of the study, no correlation was found between education and L1 literacy and student\u27s progress. However, the statistical results indicate slight positive correlations between students\u27 point gains and their length of residence in the U.S. as well as the total number of instructional hours attended. Additionally, further analysis found that teachers who provided interactive classroom environments positively affected students\u27 progress and that students who set goals relating to more immediate needs such as work, education, or participation in the community, had higher point gains than those whose goals were less specific or immediate

Decoding information in the human hippocampus: a user's guide

Multi-voxel pattern analysis (MVPA), or 'decoding', of fMRI activity has gained popularity in the neuroimaging community in recent years. MVPA differs from standard fMRI analyses by focusing on whether information relating to specific stimuli is encoded in patterns of activity across multiple voxels. If a stimulus can be predicted, or decoded, solely from the pattern of fMRI activity, it must mean there is information about that stimulus represented in the brain region where the pattern across voxels was identified. This ability to examine the representation of information relating to specific stimuli (e.g., memories) in particular brain areas makes MVPA an especially suitable method for investigating memory representations in brain structures such as the hippocampus. This approach could open up new opportunities to examine hippocampal representations in terms of their content, and how they might change over time, with aging, and pathology. Here we consider published MVPA studies that specifically focused on the hippocampus, and use them to illustrate the kinds of novel questions that can be addressed using MVPA. We then discuss some of the conceptual and methodological challenges that can arise when implementing MVPA in this context. Overall, we hope to highlight the potential utility of MVPA, when appropriately deployed, and provide some initial guidance to those considering MVPA as a means to investigate the hippocampus

A Clinical Decision Support System Promotes Shared Decision-Making and Cardiovascular Risk Factor Management

Background: CV Wizard is a web-based electronic health record-integrated point-of-care clinical decision support (CDS) system that presents personalized cardiovascular (CV) risk information to providers and patients in both a low numeracy visual format and a high numeracy quantitative format. Herein we report primary care provider perspectives on how this CDS system affected shared decision-making and CV risk factor management. Methods: Twenty clinics were randomized to either usual care or use of the CDS system for adults with diabetes, heart disease or high reversible CV risk. The CDS system targeted 20% of office visits and was used at 70%–80% of targeted visits over a 2-year period. Consented providers (n = 102) were surveyed at baseline and 18 months after implementation. Corrected survey response rates were 90% at baseline and 82% at follow-up. Generalized linear mixed models were used to compare usual care and CDS responses to common questions at baseline and follow-up, and CDS users were queried on their perceptions of the CDS system at follow-up only. Results: Compared to usual care providers, those in the CDS group reported increased follow-up rates of CV risk calculations while seeing patients (73% vs 28%, P = 0.006), being better prepared to discuss CV risk reduction priorities with patients (98% vs 78%, P = 0.03), providing accurate advice on aspirin for primary prevention (75% vs 48%, P = 0.02), and more often discussing CV risk reduction (60% vs 30%, P = 0.06). CDS users reported that the CDS system improved CV risk factor control (98%), saved time when talking to patients about CV risk reduction (93%), efficiently elicited patient treatment preferences (90%), was useful for shared decision-making (95%), influenced treatment recommendations (89%) and helped initiate CV risk discussions (94%); 85% of providers reported that their patients liked CV Wizard. Conclusion: The CV Wizard CDS system was successfully integrated into the workflow of primary care visits with high sustained use rates, high primary care provider satisfaction, high patient satisfaction, and positive impacts on provider-reported clinical processes related to CV risk factor management

C-B1-02: Strategies Used by Physicians to Treat Hypertension

Background and Aims: There is high variation in the way hypertension is managed by primary care physicians (PCP’s). The aim of this study was to evaluate correlations between the frequency of different treatment strategies and overall achievement of blood pressure goals

Primary Care Provider Use Rates of a Clinical Decision Support Tool and Change in Diabetes Performance Measures

Background: A previously published randomized controlled trial demonstrated that implementation of an electronic health record-linked personalized clinical decision support (PCDS) tool within primary care clinics improved mean A1c and blood pressure control. We subsequently implemented a modification of our CDS tool for expanded use with high cardiovascular risk adults (CV-PCDS) that also retained the decision support for glycemic control for patients with diabetes. Herein we analyze the association between primary care provider use rates of CV-PCDS with diabetes performance measures in patients with diabetes. Methods: Using data from a cluster randomized trial in 2012–2014, we analyzed the association of CV-PCDS provider-specific use rates in March 2014 with diabetes performance measures 6 months later, using Pearson correlation coefficients. Performance measures included the proportion of a provider’s diabetes patients who (a) achieved A1c \u3c 8%, and (b) achieved a composite measure of optimal diabetes care that required simultaneous achievement of A1c \u3c 8%, systolic blood pressure \u3c 140 mm Hg, low-density lipoprotein cholesterol \u3c 100 mg/dl, non-tobacco user, and ASA (aspirin) use for secondary prevention. Results: Providers (N = 43) used the CV-PCDS tool at a mean of 82.1% of targeted encounters of adults with high cardiovascular risk (range across providers: 36.0% to 100% of encounters). The mean percentage of the diabetes subgroup who achieved A1c \u3c 8% was 73.7%, and the percentage of patients who achieved the optimal diabetes care goal was 46.8%. Pearson correlation coefficients between CV-PCDS provider use rates in March 2014 and A1c and optimal diabetes care performance measures in August 2014 were 0.16 (P = 0.31) and 0.24 (P = 0.12), respectively. Conclusion: In this high-performing health care system with high CV-PCDS use rates, there was a positive but nonsignificant association of provider use of the CV-PCDS tool and provider-level quality of diabetes care 6 months later. The generalizability of this finding to lower-performing care systems, and to providers with lower baseline quality of diabetes care remains to be determined

Impact of Improving Diabetes Care on Quality-Adjusted Life Expectancy and Costs: A 40-Year Perspective

Background: There has been a trend toward better management of glucose, lipids, blood pressure, smoking and other aspects of diabetes care in the last decade. The goals of this study are to assess changes in quality of diabetes care over a 14-month period of time in a large, multispecialty U.S. medical group, to quantify treatment costs associated with the improved care, and to estimate the impact of care improvement on long-term costs and quality-adjusted life expectancy. Methods: Study subjects included 7,054 persons with diabetes, age 40–75 years, who at baseline had one or more of: systolic blood pressure \u3e 140 mmHg, low-density lipoprotein cholesterol \u3e 129 mg/dL, or current smoking status. We quantified their clinical status including A1c, blood pressure, lipids and smoking status both at baseline and after a median 14-month follow-up period. We similarly quantified visit frequency and medication use and associated costs in the year prior and year following the baseline visit. We employed these clinical risk factors and observed costs as data inputs into a log-term simulation model of diabetes outcomes –– the United Kingdom Prospective Diabetes Study Outcomes Model (Version 2) –– in order to estimate changes in quality-adjusted life years (QALYs) and costs associated with changes in clinical care, projected over a 40-year time period. We applied costs of complications that were derived from a previous study within this health system. We then estimated the cost per QALY gain for these adult diabetes subjects who are experiencing better clinical care over time. Results: Observed improvements in clinical care significantly increased expected QALY from 10.83 to 11.06, for a gain in 0.22 QALY. Incremental costs associated with outpatient visits and intensification of pharmacotherapy were $167 per year and$2,323 over the study period. Total costs increased by $4,453. Cost per QALY was estimated to be$19,866. Sensitivity analysis indicated that estimates of cost per QALY were more favorable in simulations with longer follow-up periods and in simulations that more narrowly targeted blood pressure control among those with high blood pressure at baseline. Conclusion: Observed improvements in diabetes care over a recent 14-month period of time are sufficient to significantly improve clinical and health outcomes. The cost-effectiveness of the slightly more intensive diabetes care provided appears to be satisfactory using standard thresholds for cost per QALY, both in the base case and across a range of sensitivity analysis scenarios. However, improvements in diabetes care are not cost saving from the point of view of the payer

Clinical Decision Support Impact on Overuse and Underuse of Aspirin for Primary Prevention of Cardiovascular Events

Background: The U.S. Preventive Services Task Force (USPSTF) recommends aspirin for primary prevention of atherosclerotic vascular disease (ASCVD) when the ASCVD benefit outweighs the risk of gastrointestinal hemorrhage. The complexity and time required to assess aspirin risks and benefits can result in overuse and underuse of aspirin. Methods: As part of a National Institutes of Health-funded study to lower ASCVD risk, we implemented electronic clinical decision support (CDS) algorithms to guide aspirin use based on USPSTF criteria and major bleeding risks. Baseline data was collected for whether aspirin was algorithmically recommended for all patients at their first eligible primary care encounter in 20 clinics over 2012–2014. The analysis excluded patients with congenital heart disease and included 6,651 adults with diabetes (mean age: 55.6 years; mean 10-year ASCVD risk: 27.8%) and 11,682 adults meeting prespecified criteria for high ASCVD risk without diabetes (mean age: 58.4 years; mean 10-year ASCVD risk: 24.7%). Overuse and underuse was determined by comparing concordance with (a) aspirin recommendations, and (b) documented aspirin use. Results: The CDS recommended aspirin for 4,139 (63.1%) patients with diabetes and 8,722 (74.7%) without diabetes. Among patients with aspirin recommended, aspirin was not used in 829 of 4,139 (20%) with diabetes and 6,493 of 8,722 (74.4%) without diabetes (underuse). Among patients for whom the CDS did not recommend aspirin, aspirin was used in 1,448 of 2,969 (59.8%) with diabetes and 1,021 of 2,960 (34.4%) without diabetes (overuse). Conclusion: Those with diabetes who were likely to benefit from aspirin use had higher aspirin use rates (less underuse) than similar high-cardiovascular-risk patients without diabetes. However, those with diabetes who were unlikely to benefit from aspirin based on USPSTF criteria and bleeding risks also had higher aspirin use rates (more overuse) than patients without diabetes. Strategies to ensure greater evidence-based use of aspirin, such as providing electronic clinical decision support, may help providers more accurately assess individualized risks and benefits of aspirin

Aspirin for Primary Prevention of Atherosclerotic Cardiovascular Disease: Challenges to Appropriate Use

Background: Aspirin use for primary prevention of atherosclerotic cardiovascular disease (ASCVD) should be highly individualized in order to accurately balance benefits and risks. There are now practical approaches for clinicians to calculate ASCVD and bleeding risks using web-based tools and mobile apps to facilitate good decision-making, but their lack of integration with the electronic health record (EHR) and need for extensive data input are barriers to use by busy clinicians. Methods: As part of a clinic randomized trial with 20 primary care clinics, we developed and tested an EHR-integrated web-based clinical decision support (CDS) system that provided individualized aspirin recommendations to patients and clinicians using risk-benefit calculations. During the 18-month intervention, aspirin recommendations were printed for patients and providers at 75% of eligible encounters. We evaluated the effects of the intervention on rates of appropriate primary prevention aspirin use among 3,958 patients with diabetes and 7,000 patients without diabetes, aged 40–75 years, with uncontrolled cardiovascular disease risk factors. Results: At baseline, among patients using aspirin, it was not recommended (overused) for 840 of 1,474 (57%) patients with diabetes and 564 of 1,659 (34%) without diabetes. Of patients not using aspirin, it was recommended (underused) by 522 of 2,484 (21%) patients with diabetes and 4,006 of 5,371 (75%) without diabetes. At the last follow-up visit, no significant differences were noted in aspirin use patterns for patients with diabetes. However, among patients without diabetes who were “underusing” aspirin at baseline, 12.9% were using aspirin in CDS clinics compared to 10.4% in control clinics (P = 0.03). Among patients who were “overusing” aspirin at baseline, 4.1% had discontinued using aspirin in CDS system clinics compared to 7.9% in control clinics (P = 0.06). Conclusion: Patterns of appropriate aspirin use are different among patients with and without diabetes, with overuse being more common in diabetes and underuse more common in those without diabetes. Our study results suggest that the use of accurate CDS by clinicians and patients improve overall concordance with aspirin. A better understanding of how best to present understandable risk-benefit information to providers and patients is needed