Estimates of Electronic Medical Records in U.S. Emergency Departments
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{"title"=>"Estimates of electronic medical records in U.S. emergency departments", "type"=>"journal", "authors"=>[{"first_name"=>"Benjamin P.", "last_name"=>"Geisler", "scopus_author_id"=>"6602502705"}, {"first_name"=>"Jeremiah D.", "last_name"=>"Schuur", "scopus_author_id"=>"24342325700"}, {"first_name"=>"Daniel J.", "last_name"=>"Pallin", "scopus_author_id"=>"7006022696"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"20174660", "doi"=>"10.1371/journal.pone.0009274", "sgr"=>"77949508900", "isbn"=>"19326203", "scopus"=>"2-s2.0-77949508900", "issn"=>"19326203", "pui"=>"358466892"}, "id"=>"82b0413f-2783-3330-9f5a-d33fecf292c2", "abstract"=>"BACKGROUND: Policymakers advocate universal electronic medical records (EMRs) and propose incentives for \"meaningful use\" of EMRs. Though emergency departments (EDs) are particularly sensitive to the benefits and unintended consequences of EMR adoption, surveillance has been limited. We analyze data from a nationally representative sample of US EDs to ascertain the adoption of various EMR functionalities. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed data from the National Hospital Ambulatory Medical Care Survey, after pooling data from 2005 and 2006, reporting proportions with 95% confidence intervals (95% CI). In addition to reporting adoption of various EMR functionalities, we used logistic regression to ascertain patient and hospital characteristics predicting \"meaningful use,\" defined as a \"basic\" system (managing demographic information, computerized provider order entry, and lab and imaging results). We found that 46% (95% CI 39-53%) of US EDs reported having adopted EMRs. Computerized provider order entry was present in 21% (95% CI 16-27%), and only 15% (95% CI 10-20%) had warnings for drug interactions or contraindications. The \"basic\" definition of \"meaningful use\" was met by 17% (95% CI 13-21%) of EDs. Rural EDs were substantially less likely to have a \"basic\" EMR system than urban EDs (odds ratio 0.19, 95% CI 0.06-0.57, p = 0.003), and Midwestern (odds ratio 0.37, 95% CI 0.16-0.84, p = 0.018) and Southern (odds ratio 0.47, 95% CI 0.26-0.84, p = 0.011) EDs were substantially less likely than Northeastern EDs to have a \"basic\" system. CONCLUSIONS/SIGNIFICANCE: EMRs are becoming more prevalent in US EDs, though only a minority use EMRs in a \"meaningful\" way, no matter how \"meaningful\" is defined. Rural EDs are less likely to have an EMR than metropolitan EDs, and Midwestern and Southern EDs are less likely to have an EMR than Northeastern EDs. We discuss the nuances of how to define \"meaningful use,\" and the importance of considering not only adoption, but also full implementation and consequences.", "link"=>"http://www.mendeley.com/research/estimates-electronic-medical-records-emergency-departments", "reader_count"=>43, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Librarian"=>3, "Researcher"=>3, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>6, "Student > Master"=>4, "Other"=>3, "Student > Bachelor"=>5, "Professor"=>2, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Librarian"=>3, "Researcher"=>3, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>6, "Student > Master"=>4, "Other"=>3, "Student > Bachelor"=>5, "Professor"=>2, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Nursing and Health Professions"=>5, "Medicine and Dentistry"=>17, "Business, Management and Accounting"=>4, "Social Sciences"=>6, "Computer Science"=>7, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>17}, "Social Sciences"=>{"Social Sciences"=>6}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Computer Science"=>{"Computer Science"=>7}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>5}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>4}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>5, "South Africa"=>1, "United Kingdom"=>1}, "group_count"=>5}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/862855"], "description"=>"†<p>Question only available in 2006 dataset.</p>‡<p>Question in 2005 survey asked for all test results, not broken out by lab vs. radiology.</p>*<p>Jha and DesRoches classified EMRs as: “basic” (including only demographic information, CPOE, lab and imaging results), “basic with clinical notes,” or “comprehensive” (including the above, plus electronic prescribing, radiographic image display, and decision support).</p>", "links"=>[], "tags"=>["functionalities", "departments"], "article_id"=>533317, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Benjamin P. Geisler", "Jeremiah D. Schuur", "Daniel J. Pallin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009274.t001", "stats"=>{"downloads"=>3, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Adoption_of_Individual_Electronic_Medical_Record_Functionalities_by_US_Emergency_Departments_by_2005_8211_2006_/533317", "title"=>"Adoption of Individual Electronic Medical Record Functionalities by US Emergency Departments by 2005–2006.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 02:38:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/862886"], "description"=>"*<p>In this analysis, we used adoption of a “basic” electronic medical record system as the outcome. This definition was taken from Jha and DesRoches, and required a system to include electronic management of demographic information, computerized provider order entry, and lab and imaging results <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009274#pone.0009274-Jha1\" target=\"_blank\">[3]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009274#pone.0009274-DesRoches1\" target=\"_blank\">[4]</a>. We began our analysis by conducing bivariate analyses, to determine which of a series of candidate predictors appeared to have a relationship with the outcome variable. We used the following candidate predictors: patient age, gender, race/ethnicity, and source of payment, and, at the hospital level, region, metropolitan vs. non-metropolitan (i.e. urban vs. rural), ownership, and teaching status. Candidate predictors were eliminated from further consideration if bivariate chi-squared testing resulted in a p-value≥0.20. Remaining candidate predictors were fitted to a multivariate logistic regression model, constructed via stepwise backward elimination until all remaining independent covariates had p<0.05 in their type 3 analyses of effects. Accordingly, the following predictors were eliminated from the model: patient-level variables, age, gender, race/ethnicity, and source of payment (insurance type); and hospital-level variables, ownership, teaching status. Only region and urban/rural status were significant predictors of adoption of at least a “basic” system.</p>", "links"=>[], "tags"=>["computer science/information technology", "critical care and emergency medicine/emergency medicine", "evidence-based healthcare/health services research and economics", "public health and epidemiology/health policy"], "article_id"=>533342, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Benjamin P. Geisler", "Jeremiah D. Schuur", "Daniel J. Pallin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009274.t002", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predictors_of_Adoption_of_at_least_a_Basic_Electronic_Medical_Record_System_/533342", "title"=>"Predictors of Adoption of at least a “Basic” Electronic Medical Record System.<sup>*</sup>", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 02:38:58"}

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Relative Metric

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