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      Enhancing Electronic Health Record Data with Geospatial Information

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          Abstract

          Electronic Health Record (EHR)-derived data is a valuable resource for research, and efforts are underway to overcome some of its limitations by using data from external sources to gain a fuller picture of patient characteristics, symptoms, and exposures. Our goal was to assess the utility of augmenting EHR data with geocoded patient addresses to identify geospatial variation of disease that is not explained by EHR-derived demographic factors. Using 2011-2014 encounter data from 27,604 University of Pennsylvania Hospital System asthma patients, we identified factors associated with asthma exacerbations: risk was higher in female, black, middle aged to elderly, and obese patients, as well as those with positive smoking history and with Medicare or Medicaid vs. private insurance. Significant geospatial variability of asthma exacerbations was found using generalized additive models, even after adjusting for demographic factors. Our work shows that geospatial data can be used to cost-effectively enhance EHR data.

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          The Geisinger MyCode Community Health Initiative: an electronic health record-linked biobank for Precision Medicine research

          Purpose Geisinger Health System (GHS) provides an ideal platform for Precision Medicine. Key elements are the integrated health system, stable patient population, and electronic health record (EHR) infrastructure. In 2007 Geisinger launched MyCode®, a system-wide biobanking program to link samples and EHR data for broad research use. Methods Patient-centered input into MyCode® was obtained using participant focus groups. Participation in MyCode® is based on opt-in informed consent and allows recontact, which facilitates collection of data not in the EHR, and, since 2013, the return of clinically actionable results to participants. MyCode® leverages Geisinger’s technology and clinical infrastructure for participant tracking and sample collection. Results MyCode® has a consent rate of >85% with more than 90,000 participants currently, with ongoing enrollment of ~4,000 per month. MyCode® samples have been used to generate molecular data, including high-density genotype and exome sequence data. Genotype and EHR-derived phenotype data replicate previously reported genetic associations. Conclusion The MyCode® project has created resources that enable a new model for translational research that is faster, more flexible, and more cost effective than traditional clinical research approaches. The new model is scalable, and will increase in value as these resources grow and are adopted across multiple research platforms.
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            Inequality in quality: addressing socioeconomic, racial, and ethnic disparities in health care.

            Socioeconomic and racial/ethnic disparities in health care quality have been extensively documented. Recently, the elimination of disparities in health care has become the focus of a national initiative. Yet, there is little effort to monitor and address disparities in health care through organizational quality improvement. After reviewing literature on disparities in health care, we discuss the limitations in existing quality assessment for identifying and addressing these disparities. We propose 5 principles to address these disparities through modifications in quality performance measures: disparities represent a significant quality problem; current data collection efforts are inadequate to identify and address disparities; clinical performance measures should be stratified by race/ethnicity and socioeconomic position for public reporting; population-wide monitoring should incorporate adjustment for race/ethnicity and socioeconomic position; and strategies to adjust payment for race/ethnicity and socioeconomic position should be considered to reflect the known effects of both on morbidity. JAMA. 2000;283:2579-2584
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              National surveillance for asthma--United States, 1980-2004.

              Asthma, a chronic respiratory disease with episodic symptoms, increased in prevalence during 1980-1996 in the United States. Asthma has been the focus of numerous provider interventions (e.g., improving adherence to asthma guidelines) and public health interventions during recent years. Although the etiology of asthma is unknown, adherence to medical treatment regimen and environmental management should reduce the occurrence of exacerbations and lessen the hardship of this disease. CDC has outlined a public health approach to asthma that includes comprehensive analyses of national surveillance data on prevalence, health-care use and mortality, and a strategy to improve the timeliness and geographic specificity of asthma surveillance data. This report presents national data on asthma for self-reported prevalence (1980-1996 and 2001-2004); self-reported attacks (1997-2004); visits to physicians' offices (1980-2004), hospital outpatient departments (1992-2004), and emergency departments (1992-2004); hospitalizations (1980-2004); and deaths (1980-2004). The National Health Interview Survey includes questions about asthma prevalence and asthma attacks. Physicians' office visit data are collected in the National Ambulatory Medical Care Survey, emergency department and hospital outpatient data in the National Hospital Ambulatory Medical Care Survey, hospitalization data in the National Hospital Discharge Survey, and death data in the Mortality component of the National Vital Statistics System. From 1980 to 1996, 12-month asthma prevalence increased both in counts and rates, but no discernable change was identified in asthma attack estimates since 1997 or in current asthma prevalence from 2001 to 2004. During the period of increasing prevalence, patient encounters (office visits, emergency department visits, outpatient visits, and hospitalizations) for asthma increased. However, rates for these encounters, when based on the population with asthma, did not increase. Although the rate of asthma deaths increased during 1980-1995, the rate of deaths has decreased each year since 2000. During 2001-2003, current asthma prevalence was higher in children (8.5%) compared with adults (6.7%), females (8.1%) compared with males (6.2%), blacks (9.2%) compared with whites (6.9%), those of Puerto Rican descent (14.5%) compared with those of Mexican descent (3.9%), those below the federal poverty level (10.3%) compared with those at or above the federal poverty level (6.4% to 7.9%), and those residing in the Northeast (8.1%) compared with those residing in other regions (6.7% to 7.5%). Among persons with current asthma, whites and blacks were equally likely to report an attack during the preceding 12 months. Women with current asthma were more likely to report asthma attacks than men, and children were more likely than adults. The rate for asthma health-care encounters, regardless of place (physician office, emergency department, outpatient department, or hospital), when based on the population with asthma, did not differ by race. However, whites with current asthma had higher rates for physician offices, and blacks had higher rates for hospital-based sites (e.g., outpatient clinics and emergency departments). The findings in this report suggest that from 1980 through the mid-1990s, increases in asthma prevalence played a substantial role in the increases in patient encounter measures used in asthma surveillance. Because no primary strategies for preventing asthma have been identified, efforts to control asthma exacerbations through interventions that promote adhering to proper medical regimens and reducing exposures to causes of asthma exacerbations should continue to be pursued.
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                Author and article information

                Journal
                AMIA Jt Summits Transl Sci Proc
                AMIA Jt Summits Transl Sci Proc
                AMIA Summits on Translational Science Proceedings
                American Medical Informatics Association
                2153-4063
                2017
                26 July 2017
                : 2017
                : 123-132
                Affiliations
                [1 ]Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
                Article
                2611235
                5543367
                28815121
                bc1dd510-1047-4aa6-8487-9d73a17ec048
                ©2017 AMIA - All rights reserved.

                This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose

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