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      Empirical advances with text mining of electronic health records

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          Abstract

          Background

          Korian is a private group specializing in medical accommodations for elderly and dependent people. A professional data warehouse (DWH) established in 2010 hosts all of the residents’ data. Inside this information system (IS), clinical narratives (CNs) were used only by medical staff as a residents’ care linking tool.

          The objective of this study was to show that, through qualitative and quantitative textual analysis of a relatively small physiotherapy and well-defined CN sample, it was possible to build a physiotherapy corpus and, through this process, generate a new body of knowledge by adding relevant information to describe the residents’ care and lives.

          Methods

          Meaningful words were extracted through Standard Query Language (SQL) with the LIKE function and wildcards to perform pattern matching, followed by text mining and a word cloud using R® packages. Another step involved principal components and multiple correspondence analyses, plus clustering on the same residents’ sample as well as on other health data using a health model measuring the residents’ care level needs.

          Results

          By combining these techniques, physiotherapy treatments could be characterized by a list of constructed keywords, and the residents’ health characteristics were built. Feeding defects or health outlier groups could be detected, physiotherapy residents’ data and their health data were matched, and differences in health situations showed qualitative and quantitative differences in physiotherapy narratives.

          Conclusions

          This textual experiment using a textual process in two stages showed that text mining and data mining techniques provide convenient tools to improve residents’ health and quality of care by adding new, simple, useable data to the electronic health record (EHR). When used with a normalized physiotherapy problem list, text mining through information extraction (IE), named entity recognition (NER) and data mining (DM) can provide a real advantage to describe health care, adding new medical material and helping to integrate the EHR system into the health staff work environment.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12911-017-0519-0) contains supplementary material, which is available to authorized users.

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          Most cited references21

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          Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA.

          In response to mounting evidence that use of electronic medical record systems may cause unintended consequences, and even patient harm, the AMIA Board of Directors convened a Task Force on Usability to examine evidence from the literature and make recommendations. This task force was composed of representatives from both academic settings and vendors of electronic health record (EHR) systems. After a careful review of the literature and of vendor experiences with EHR design and implementation, the task force developed 10 recommendations in four areas: (1) human factors health information technology (IT) research, (2) health IT policy, (3) industry recommendations, and (4) recommendations for the clinician end-user of EHR software. These AMIA recommendations are intended to stimulate informed debate, provide a plan to increase understanding of the impact of usability on the effective use of health IT, and lead to safer and higher quality care with the adoption of useful and usable EHR systems.
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            Electronic health records and quality of diabetes care.

            Available studies have shown few quality-related advantages of electronic health records (EHRs) over traditional paper records. We compared achievement of and improvement in quality standards for diabetes at practices using EHRs with those at practices using paper records. All practices, including many safety-net primary care practices, belonged to a regional quality collaborative and publicly reported performance. We used generalized estimating equations to calculate the percentage-point difference between EHR-based and paper-based practices with respect to achievement of composite standards for diabetes care (including four component standards) and outcomes (five standards), after adjusting for covariates and accounting for clustering. In addition to insurance type (Medicare, commercial, Medicaid, or uninsured), patient-level covariates included race or ethnic group (white, black, Hispanic, or other), age, sex, estimated household income, and level of education. Analyses were conducted separately for the overall sample and for safety-net practices. From July 2009 through June 2010, data were reported for 27,207 adults with diabetes seen at 46 practices; safety-net practices accounted for 38% of patients. After adjustment for covariates, achievement of composite standards for diabetes care was 35.1 percentage points higher at EHR sites than at paper-based sites (P<0.001), and achievement of composite standards for outcomes was 15.2 percentage points higher (P=0.005). EHR sites were associated with higher achievement on eight of nine component standards. Such sites were also associated with greater improvement in care (a difference of 10.2 percentage points in annual improvement, P<0.001) and outcomes (a difference of 4.1 percentage points in annual improvement, P=0.02). Across all insurance types, EHR sites were associated with significantly higher achievement of care and outcome standards and greater improvement in diabetes care. Results confined to safety-net practices were similar. These findings support the premise that federal policies encouraging the meaningful use of EHRs may improve the quality of care across insurance types.
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              Principal component analysis of socioeconomic factors and their association with malaria in children from the Ashanti Region, Ghana

              Background The socioeconomic and sociodemographic situation are important components for the design and assessment of malaria control measures. In malaria endemic areas, however, valid classification of socioeconomic factors is difficult due to the lack of standardized tax and income data. The objective of this study was to quantify household socioeconomic levels using principal component analyses (PCA) to a set of indicator variables and to use a classification scheme for the multivariate analysis of children < 15 years of age presented with and without malaria to an outpatient department of a rural hospital. Methods In total, 1,496 children presenting to the hospital were examined for malaria parasites and interviewed with a standardized questionnaire. The information of eleven indicators of the family's housing situation was reduced by PCA to a socioeconomic score, which was then classified into three socioeconomic status (poor, average and rich). Their influence on the malaria occurrence was analysed together with malaria risk co-factors, such as sex, parent's educational and ethnic background, number of children living in a household, applied malaria protection measures, place of residence and age of the child and the mother. Results The multivariate regression analysis demonstrated that the proportion of children with malaria decreased with increasing socioeconomic status as classified by PCA (p < 0.05). Other independent factors for malaria risk were the use of malaria protection measures (p < 0.05), the place of residence (p < 0.05), and the age of the child (p < 0.05). Conclusions The socioeconomic situation is significantly associated with malaria even in holoendemic rural areas where economic differences are not much pronounced. Valid classification of the socioeconomic level is crucial to be considered as confounder in intervention trials and in the planning of malaria control measures.
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                Author and article information

                Contributors
                33-06-58-37-65-03 , tiba.baroukh@gmail.com
                philippe.denormandie@mnhgroup.com
                avner.bar-hen@mi.parisdescartes.fr
                loic.josseran@aphp.fr
                Journal
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central (London )
                1472-6947
                22 August 2017
                22 August 2017
                2017
                : 17
                : 127
                Affiliations
                [1 ]Institut du Bien Vieillir Korian, 21-25 rue Balzac, 75008 Paris, France
                [2 ]Research lab: EA 4047, UFR des Sciences de la Santé Simone Veil, UVSQ Université Paris-Saclay, 2 Avenue de la Source de la Bièvre, Montigny le Bretonneux, 78180 France
                [3 ]MNH Group, 185 rue de Bercy, 75012 Paris, France
                [4 ]ISNI 0000 0001 2188 0914, GRID grid.10992.33, , UFR de Mathématiques et Informatique, Université de Paris Descartes, ; 45 rue des Saints-Pères, Paris, 75006 France
                Author information
                http://orcid.org/0000-0002-6433-6959
                Article
                519
                10.1186/s12911-017-0519-0
                5568397
                28830417
                36c8d06e-a527-4ae4-b320-ecd2701898fe
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 7 December 2016
                : 4 August 2017
                Funding
                Funded by: Institut du Bien Vieillir
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Bioinformatics & Computational biology
                nursing homes,sql query,information extraction,named entity recognition,data mining,text mining,word cloud,multiple component analysis,principal component analysis,hierarchical clustering

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