The Strategic Health IT Advanced Research Projects (SHARP) Program, established by the Office of the National Coordinator for Health Information Technology in 2010 supports research findings that remove barriers for increased adoption of health IT. The improvements envisioned by the SHARP Area 4 Consortium (SHARPn) will enable the use of the electronic health record (EHR) for secondary purposes, such as care process and outcomes improvement, biomedical research and epidemiologic monitoring of the nation’s health. One of the primary informatics problem areas in this endeavor is the standardization of disparate health data from the nation’s many health care organizations and providers. The SHARPn team is developing open source services and components to support the ubiquitous exchange, sharing and reuse or ‘liquidity’ of operational clinical data stored in electronic health records. One year into the design and development of the SHARPn framework, we demonstrated end to end data flow and a prototype SHARPn platform, using thousands of patient electronic records sourced from two large healthcare organizations: Mayo Clinic and Intermountain Healthcare. The platform was deployed to (1) receive source EHR data in several formats, (2) generate structured data from EHR narrative text, and (3) normalize the EHR data using common detailed clinical models and Consolidated Health Informatics standard terminologies, which were (4) accessed by a phenotyping service using normalized data specifications. The architecture of this prototype SHARPn platform is presented. The EHR data throughput demonstration showed success in normalizing native EHR data, both structured and narrative, from two independent organizations and EHR systems. Based on the demonstration, observed challenges for standardization of EHR data for interoperable secondary use are discussed.

BackgroundA better understanding of the epidemiology and clinical features of invasive fungal infection (IFI) is integral to improving outcomes. We describe a novel case-finding methodology, reporting incidence, clinical features, and outcomes of IFI in a large US health care network.MethodsAll available records in the Intermountain Healthcare Enterprise Data Warehouse from 2006 to 2015 were queried for clinical data associated with IFI. The resulting data were overlaid in 124 different combinations to identify high-probability IFI cases. The cohort was manually reviewed, and exclusions were applied. European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group Consensus Group definitions were adapted to categorize IFI in a broad patient population. Linear regression was used to model variation in incidence over time.ResultsA total of 3374 IFI episodes occurred in 3154 patients. The mean incidence was 27.2 cases/100 000 patients per year, and there was a mean annual increase of 0.24 cases/100 000 patients (P = .21). Candidiasis was the most common (55%). Dimorphic fungi, primarily Coccidioides spp., comprised 25.1% of cases, followed by Aspergillus spp. (8.9%). The median age was 55 years, and pediatric cases accounted for 13%; 26.1% of patients were on immunosuppression, 14.9% had autoimmunity or immunodeficiency, 13.3% had active malignancy, and 5.9% were transplant recipients. Lymphopenia preceded IFI in 22.1% of patients. Hospital admission occurred in 76.2%. The median length of stay was 16 days. All-cause mortality was 17.0% at 42 days and 28.8% at 1 year. Forty-two-day mortality was highest in Aspergillus spp. (27.5%), 20.5% for Candida, and lowest for dimorphic fungi (7.5%).ConclusionsIn this population, IFI was not uncommon, affected a broad spectrum of patients, and was associated with high crude mortality.

End-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.

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wireless networking is one of the current attractive fields for researchers in current era. The ad hoc network is a separable part of wireless networking which facilitate the users to perform communication among mobile terminals with limited bandwidth. But as these networks did not follow any physical topology for installation therefore the risk of security attacks an threats are higher in ad hoc networks. Various attacks such a eavesdropping, spoofing, denial of service are difficult to detect in wireless ad hoc networks. So many researchers have been conducted in this field till now but most of them are not capable to achieve high level security of data plane in ad hoc wireless networks. A novel approach for securing data plane in AHNs has been developed under this study. The PRO-OLSR is a combination of fuzzy based system with OLSR routing protocol. The essential parameters like average delay, packet delivery ratio, direct trust and attack suspected ratio are used as an input membership function to fuzzy system. And on the Basis of the output of FIS the next hop for data delivery is elected. The simulation is done in MATLAB and results are generated to proves the proficiency of PRO-OLSR technique.

Activation of the MDR1 (ABCB1) gene is a common event conferring multidrug resistance (MDR) in human cancers. We investigated MDR1 activation in MDR variants of a human sarcoma line, some of which express a mutant MDR1, which facilitated the study of allelic gene expression. Structural alterations of MDR1, gene copy numbers, and allelic expression were analyzed by cytogenetic karyotyping, oligonucleotide hybridization, Southern blotting, polymerase chain reaction, and DNA heteroduplex assays. Both chromosome 7 alterations and several cytogenetic changes involving the 7q21 locus are associated with the development of MDR in these sarcoma cells. Multistep-selected cells and their revertants contain three- to six-fold MDR1 gene amplification compared with that of the drug-sensitive parental cell line MES-SA and single-step doxorubicin-selected mutants. MDR1 gene amplification precedes the emergence of a mutant allele in cells that were coselected with doxorubicin and a cyclosporin inhibitor of P-glycoprotein (P-gp). Allele-specific oligonucleotide hybridization showed that the endogenous mutant allele was present as a single copy, with multiple copies of the normal allele. Reselection of revertant cells with doxorubicin in either the presence or the absence of the P-gp inhibitor resulted in exclusive reexpression of the mutant MDR1 allele, regardless of the presence of multiple wild-type MDR1 alleles. These data provide new insights into how multiple alleles are regulated in the amplicon of drug-resistant cancer cells and indicate that increased expression of an amplified gene can result from selective transcription of a single mutant allele of the gene.

Background: Identifying COPD patients at high risk for mortality or healthcare utilization remains a challenge. A robust system for identifying high-risk COPD patients using Electronic Health Record (EHR) data would empower targeting interventions aimed at ensuring guideline compliance and multimorbidity management. The purpose of this study was to empirically derive, validate, and characterize subgroups of COPD patients based on routinely collected clinical data widely available within the EHR.Methods: Cluster analysis was used in 5,006 patients with COPD at Intermountain to identify clusters based on a large collection of clinical variables. Recursive Partitioning (RP) was then used to determine a preferred tree that assigned patients to clusters based on a parsimonious variable subset. The mortality, COPD exacerbations, and comorbidity profile of the identified groups were examined. The findings were validated in an independent Intermountain cohort and in external cohorts from the United States Veterans Affairs (VA) and University of Chicago Medicine systems.Measurements and Main Results: The RP algorithm identified five LIVE Scores based on laboratory values: albumin, creatinine, chloride, potassium, and hemoglobin. The groups were characterized by increasing risk of mortality. The lowest risk, LIVE Score 5 had 8% 4-year mortality vs. 56% in the highest risk LIVE Score 1 (p < 0.001). These findings were validated in the VA cohort (n = 83,134), an expanded Intermountain cohort (n = 48,871) and in the University of Chicago system (n = 3,236). Higher mortality groups also had higher COPD exacerbation rates and comorbidity rates.Conclusions: In large clinical datasets across different organizations, the LIVE Score utilizes existing laboratory data for COPD patients, and may be used to stratify risk for mortality and COPD exacerbations.