ACI open最新文献

{"title":"Data-Driven Diabetes Education Guided by a Personalized Report for Patients on Insulin Pump Therapy.","authors":"Danielle Groat,&nbsp;Krystal Corrette,&nbsp;Adela Grando,&nbsp;Vaishak Vellore,&nbsp;Mike Bayuk,&nbsp;George Karway,&nbsp;Mary Boyle,&nbsp;Rozalina McCoy,&nbsp;Kevin Grimm,&nbsp;Bithika Thompson","doi":"10.1055/s-0039-1701022","DOIUrl":"https://doi.org/10.1055/s-0039-1701022","url":null,"abstract":"<p><strong>Objective: </strong>It is difficult to assess self-management behaviors (SMBs) and incorporate them into a personalized self-care plan. We aimed to develop and apply SMB phenotyping algorithms from data collected by diabetes devices and a mobile health (mHealth) application to create patient-specific SMBs reports to guide individualized interventions. Follow-up interventions aimed to understand patient's reasoning behind discovered SMB choices.</p><p><strong>Methods: </strong>This study deals with adults on continuous subcutaneous insulin infusion using a continuous glucose monitor (CGM) who self-tracked SMBs with an mHealth application for 1 month. Patient-generated data were quantified and an SMB report was designed and populated for each participant. A diabetes educator used the report to conduct personalized, data-driven educational interventions. Thematic analysis of the intervention was conducted.</p><p><strong>Results: </strong>Twenty-two participants recorded 118 alcohol, 251 exercise, 2,661 meal events, and 1,900 photos. A patient-specific SMB report was created from this data and used to conduct the educational intervention. High variability of SMB was observed between patients. There was variability in the percentage of alcohol events accompanied by a blood glucose check, median 79% (38-100% range), and frequency of changing the bolus waveform, median 11 (7-95 range). Interventions confirmed variability of SMBs. Main emerging themes from thematic analysis were: challenges and barriers, motivators, current SMB techniques, and future plans to improve glycemic control.</p><p><strong>Conclusion: </strong>The ability to quantify SMBs and understand patients' rationale may help improve diabetes self-care and related outcomes. This study describes our first steps in piloting a patient-specific diabetes educational intervention, as opposed to the current \"one size fits all\" approach.</p>","PeriodicalId":72041,"journal":{"name":"ACI open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0039-1701022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39037632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishing a Data-Sharing Environment for a 21st-Century Academic Health Center","authors":"Anjum Khurshid, Justin F. Rousseau, Steven B. Andrews, W. Tierney","doi":"10.1055/s-0040-1709652","DOIUrl":"https://doi.org/10.1055/s-0040-1709652","url":null,"abstract":"Abstract Objective The main purpose of this study was to establish a seamless clinical data sharing system in a new medical school in partnership with community health systems. Methods We developed a Data Request Management System (DRMS) and a data request process to streamline access to and management of data for quality improvement, population health, and research. We utilized a four-pronged methodology in implementing our clinical data sharing system: data governance, data extraction, external relationships, and internal engagement. Results The Data Core team of honest data brokers through the established relationships, data use agreements, data request processes, and the DRMS processed more than 50 data requests from all the departments during its first year of operation. The DRMS application and the supporting governance and relationships provided a platform for improved process and accuracy of data sharing environment by facilitating trust, transparency, standardization, and service provisioning. Conclusion Developing a seamless data ecosystem that forms the basis of a learning health system between an academic health center and community health systems requires a combination of people (the Data Core team), processes (common data request process policies and procedures), and technology (an effective online DRMS). Future work is needed to measure the impact of the clinical data sharing system on efficiency and accuracy of data sharing.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1709652","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49526833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualization of Electronic Health Record Data for Decision-Making in Diabetes and Congestive Heart Failure","authors":"S. Fischer, S. Fischer, S. Fischer, C. Safran, Krzysztof Z Gajos, A. Wright","doi":"10.1055/s-0040-1702213","DOIUrl":"https://doi.org/10.1055/s-0040-1702213","url":null,"abstract":"Abstract Objective The aim of this study is to study the impact of graphical representation of health record data on physician decision-making to inform the design of health information technology. Materials and Methods We conducted a within participants crossover design study using a simulated electronic health record (EHR) in which we presented cases with and without visualized data designed to highlight important clinical trends or relationships, followed by assessment of the impact on decision-making about next steps for patients with chronic diseases. We then asked whether trends were observed and about usability and satisfaction using validated usability questions and asked open-ended questions as well. Time to answer questions was also collected. Results Twenty-one primary care providers participated in the study, including five for testing only and sixteen for the full study. Questions about clinical assessment or next actions were answered correctly 55% of the time. Regarding objective trends in the data, participants described noticing the trends 85% of the time. Differences in noticing trends or difficulty level of questions were not statistically significant. Satisfaction with the tool was high and participants agreed strongly that it helped them make better decisions without adding to the time it took. Discussion The simulation allowed us to test the impact of a visualization on clinician practice in a realistic setting. Designers of EHRs should consider the ways information presentation can affect decision-making. Conclusion Testing visualization tools can be done in a clinically realistic context. Providers desire visualizations and believe that they help them make better and faster decisions.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1702213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42705824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variation in Electronic Health Record Workflow Patterns: A Multisite study","authors":"Swaminathan Kandaswamy, Jiajun Wei, Amy Will, Erica L. Savage, R. Ratwani, A. Z. Hettinger, K. Miller","doi":"10.1055/s-0040-1713102","DOIUrl":"https://doi.org/10.1055/s-0040-1713102","url":null,"abstract":"Abstract Objectives Electronic health records (EHRs) continue to have significant usability challenges in part due to differences in workflow. The objective of this study was to examine workflow pattern variations for one specific task: emergency physicians placing a magnetic resonance imaging (MRI) order. Methods A between-subjects usability study was conducted using two different major EHR vendor products across four different provider sites (n = 55). A clinical scenario concerning for spinal cord compression was read to participants who then completed an ordering task using a training environment representative of their native EHR. The primary outcome measures were accuracy, time on task, and number of clicks. Results We identified four different workflows to complete the same order. One workflow required two steps (enabled at one site), one workflow required four steps (enabled at two sites), and two workflows required six steps to complete the task (available at all sites). Of the 12 physicians who employed the two-step workflow, 8 (67%) had the correct order and correct indication, the average time on task was 29.65 (standard deviation [SD] = 13.77), and the mean number of clicks was 13.5 (SD = 18.87). In contrast, for the 43 physicians who employed other workflows, 7 (21%) had the correct order and correct indication, with the average time on task of 73.1 (SD = 30.12) and mean clicks of 27.64 (SD = 13.25) (p < 0.01 for all three comparisons). Discussion These different approaches were made possible by technical specifications leading to multiple workflow options available to physicians in the EHR environment. EHR design maximizing usability can reduce the work effort and improve the accuracy of physician ordering.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1713102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43582652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oncologists' Perceptions of a Digital Tool to Improve Cancer Survivors' Cardiovascular Health.","authors":"Marjorie Kelley, Randi Foraker, En-Ju Deborah Lin, Manjusha Kulkarni, Maryam Lustberg, Kathryn E Weaver","doi":"10.1055/s-0039-1696732","DOIUrl":"10.1055/s-0039-1696732","url":null,"abstract":"<p><strong>Background: </strong>Cardiovascular (CV) disease continues to be a leading cause of morbidity and mortality with higher rates among cancer survivors than in the general population.</p><p><strong>Objective: </strong>This study was aimed to understand oncology providers' attitudes toward a digital CV health tool, delivered via a tablet, to promote CV health in cancer survivors.</p><p><strong>Methods: </strong>Using qualitative methods, 14 oncologists, from community and academic practice sites, were interviewed while they used the tool. Interviews were videotaped then analyzed using NVivo 11 software. Themes were inductively developed from the interviews.</p><p><strong>Results: </strong>Three major themes emerged from the interviews as follows: (1) system functionality, (2) facilitators and barriers to integration, and (3) appropriate end-users. Oncologists recognized the critical role of CV health promotion among cancer survivors and identified features about the tool that would be helpful for CV health promotion. Workflow (subtheme) was a barrier to tool use. This feedback enabled tool redesign for further testing in the context of survivorship care.</p><p><strong>Conclusion: </strong>Our findings emphasized the importance of identifying appropriate End-users which may include other survivorship care providers, patients, and primary care providers.</p><p><strong>Implications: </strong>Our research addresses the knowledge gap in the use of digital tools in cancer survivorship care, specifically digital tools to promote CV health. Future research is needed to evaluate digital tools in cancer survivorship care. Research investigating patients as users of digital tools may provide additional insight.</p>","PeriodicalId":72041,"journal":{"name":"ACI open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44189170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Evaluation of Record Linkage Rules in a Safety-Net Health System Serving Disadvantaged Communities","authors":"W. Trick, K. Doshi, Michael J Ray, F. Angulo","doi":"10.1055/S-0039-1693129","DOIUrl":"https://doi.org/10.1055/S-0039-1693129","url":null,"abstract":"Abstract Background There is a need for flexible, accurate record-linkage systems with transparent rules that work across diverse populations. Objectives We developed rules responsive to challenges in linking records for an urban safety-net health system; we calculated performance characteristics for our algorithm. Methods We evaluated encounters during January 1, 2012 through September 30, 2018. We compared our algorithm, using name (first-last), date-of-birth (DOB), and last four of social security number to our electronic health record (EHR) system's reconciliation process. We applied our algorithm to unreconciled real-time Admission-Discharge-Transfer registration data, and compared match results to reconciled identities from our enterprise data warehouse. We manually validated matches for randomly sampled discordant pairs; we calculated sensitivity/specificity. We evaluated predictors of discordance, including census tract information. Results Of 771,477 unique medical record numbers, most (95%) were concordant between systems; a substantial minority (5%) was discordant. Of 38,993 discordant pairs, most (n = 36,539; 94%) were detected by our local algorithm. The sensitivity of our algorithm was higher than the EHR process (99% vs. 81%), but with lower specificity (98.6% vs. 99.9%). Our highest-yield rules, beyond full first and last name plus complete DOB match, were first three initials of first name, transposed first-last names, and DOB offsets (+1 and +365 days). Factors associated with discordance were homelessness (adjusted odds ratio [aOR] = 2.4; 95% confidence interval [CI], 2.2–2.6) and living in a census tract with high levels of poverty (aOR = 1.4; 95% CI, 1.3–1.4). Conclusion Our algorithm had superior sensitivity compared to our EHR process. Homelessness and poverty were associated with unmatched records. Improved sensitivity was attributable to several critical input-variable processing steps useful for similar difficult-to-link populations.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/S-0039-1693129","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48523472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient-Specific Explanations for Predictions of Clinical Outcomes.","authors":"Mohammadamin Tajgardoon,&nbsp;Malarkodi J Samayamuthu,&nbsp;Luca Calzoni,&nbsp;Shyam Visweswaran","doi":"10.1055/s-0039-1697907","DOIUrl":"https://doi.org/10.1055/s-0039-1697907","url":null,"abstract":"<p><strong>Background: </strong>Machine learning models that are used for predicting clinical outcomes can be made more useful by augmenting predictions with simple and reliable patient-specific explanations for each prediction.</p><p><strong>Objectives: </strong>This article evaluates the quality of explanations of predictions using physician reviewers. The predictions are obtained from a machine learning model that is developed to predict dire outcomes (severe complications including death) in patients with community acquired pneumonia (CAP).</p><p><strong>Methods: </strong>Using a dataset of patients diagnosed with CAP, we developed a predictive model to predict dire outcomes. On a set of 40 patients, who were predicted to be either at very high risk or at very low risk of developing a dire outcome, we applied an explanation method to generate patient-specific explanations. Three physician reviewers independently evaluated each explanatory feature in the context of the patient's data and were instructed to disagree with a feature if they did not agree with the magnitude of support, the direction of support (supportive versus contradictory), or both.</p><p><strong>Results: </strong>The model used for generating predictions achieved a F1 score of 0.43 and area under the receiver operating characteristic curve (AUROC) of 0.84 (95% confidence interval [CI]: 0.81-0.87). Interreviewer agreement between two reviewers was strong (Cohen's kappa coefficient = 0.87) and fair to moderate between the third reviewer and others (Cohen's kappa coefficient = 0.49 and 0.33). Agreement rates between reviewers and generated explanations-defined as the proportion of explanatory features with which majority of reviewers agreed-were 0.78 for actual explanations and 0.52 for fabricated explanations, and the difference between the two agreement rates was statistically significant (Chi-square = 19.76, <i>p</i>-value < 0.01).</p><p><strong>Conclusion: </strong>There was good agreement among physician reviewers on patient-specific explanations that were generated to augment predictions of clinical outcomes. Such explanations can be useful in interpreting predictions of clinical outcomes.</p>","PeriodicalId":72041,"journal":{"name":"ACI open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0039-1697907","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39069466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Visualization of <i>Clostridium difficile</i> Infections in Intensive Care Units.","authors":"Sean C Yu,&nbsp;Albert M Lai,&nbsp;Justin Smyer,&nbsp;Jennifer Flaherty,&nbsp;Julie E Mangino,&nbsp;Ann Scheck McAlearney,&nbsp;Po-Yin Yen,&nbsp;Susan Moffatt-Bruce,&nbsp;Courtney L Hebert","doi":"10.1055/s-0039-1693651","DOIUrl":"https://doi.org/10.1055/s-0039-1693651","url":null,"abstract":"<p><strong>Background: </strong>Accurate and timely surveillance and diagnosis of healthcare-facility onset <i>Clostridium difficile</i> infection (HO-CDI) is vital to controlling infections within the hospital, but there are limited tools to assist with timely outbreak investigations.</p><p><strong>Objectives: </strong>To integrate spatiotemporal factors with HO-CDI cases and develop a map-based dashboard to support infection preventionists (IPs) in performing surveillance and outbreak investigations for HO-CDI.</p><p><strong>Methods: </strong>Clinical laboratory results and Admit-Transfer-Discharge data for admitted patients over two years were extracted from the Information Warehouse of a large academic medical center and processed according to Center for Disease Control (CDC) National Healthcare Safety Network (NHSN) definitions to classify <i>Clostridium difficile</i> infection (CDI) cases by onset date. Results were validated against the internal infection surveillance database maintained by IPs in Clinical Epidemiology of this Academic Medical Center (AMC). Hospital floor plans were combined with HO-CDI case data, to create a dashboard of intensive care units. Usability testing was performed with a think-aloud session and a survey.</p><p><strong>Results: </strong>The simple classification algorithm identified all 265 HO-CDI cases from 1/1/15-11/30/15 with a positive predictive value (PPV) of 96.3%. When applied to data from 2014, the PPV was 94.6% All users \"strongly agreed\" that the dashboard would be a positive addition to Clinical Epidemiology and would enable them to present Hospital Acquired Infection (HAI) information to others more efficiently.</p><p><strong>Conclusions: </strong>The CDI dashboard demonstrates the feasibility of mapping clinical data to hospital patient care units for more efficient surveillance and potential outbreak investigations.</p>","PeriodicalId":72041,"journal":{"name":"ACI open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0039-1693651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25377590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Halyos: A patient-facing visual EHR interface for longitudinal risk awareness","authors":"S. Mataraso, V. Socrates, Fritz Lekschas, N. Gehlenborg","doi":"10.1101/597583","DOIUrl":"https://doi.org/10.1101/597583","url":null,"abstract":"We have developed Halyos (http://halyos.gehlenborglab.org), a visual EHR web application that complements the functionality of existing patient portals. Halyos is designed to integrate with existing EHR systems to help patients interpret their health data. The Halyos application utilizes the SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) platform to create an interoperable interface that provides interactive visualizations of clinically validated risk scores and longitudinal data derived from a patient’s clinical measurements. These visualizations allow patients to investigate the relationships between clinical measurements and risk over time. By enabling patients to set hypothetical future values for these clinical measurements, patients can see how changes in their health will impact their risks. Using Halyos, patients are provided with the opportunity to actively improve their health based on increased understanding of longitudinal information available in EHRs and to begin a dialogue with their providers.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62368919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Implementation of a Novel Electronic Health Record Tool to Enhance the Care of Individuals with Cystic Fibrosis: The Cystic Fibrosis Note Template","authors":"David Leander, A. Gifford, John N. Mecchella, K. Sabadosa, A. V. Van Citters, Jennifer A. Snide, E. Nelson","doi":"10.1055/s-0039-1688804","DOIUrl":"https://doi.org/10.1055/s-0039-1688804","url":null,"abstract":"Abstract Cystic fibrosis (CF) is a genetic disease in which dysfunction of a single protein channel leads to organ damage, resulting in chronic health problems and premature death. In the United States, medical care of individuals living with CF is delivered by care centers accredited and subsidized by the CF Foundation. CF outcomes have improved significantly through the use of collaborative networks, registry data, and research. CF clinicians are perpetually challenged to assimilate and act upon large quantities of data generated by the care of these individuals. CF Foundation accreditation also requires care centers to enter patient-level data from clinical encounters into the CF Foundation Patient Registry (CFFPR). Commercially available electronic health record systems often lack tools with sufficient context specificity and ease of use to facilitate productive interactions between clinicians and patients. We describe a CF-specific NoteWriter template built and implemented in Epic, which captures discrete data and simultaneously generates clinical documentation during ambulatory encounters. Unlike other examples of note templates in CF, this project involves SmartData Elements (SDEs) using the NoteWriter tool in Epic, which enables data to be entered in the exact way in which the CFFPR captures data. By conducting a pre-/poststudy of its use in our health system, we found that the template can expedite note completion when clinicians have adequate time to become familiar with the tool. We anticipate that the NoteWriter template will become a vehicle for delivering standardized, structured patient data to the CFFPR.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0039-1688804","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48069787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}