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ehr data extraction  (Allscripts)

 
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    Structured Review

    Allscripts ehr data extraction
    Conventional tobacco-use treatment specialist referral versus point-of-care treatment models. The conventional model relies on successful referrals by the clinical care team to a tobacco-use treatment specialist. <t>Electronic</t> <t>health</t> <t>record</t> <t>(EHR)</t> functionality enables the point-of-care model to efficiently deliver treatment.
    Ehr Data Extraction, supplied by Allscripts, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/ehr+data+extraction/ehr+data/pmc07796708-153-0-9
    Average 90 stars, based on 1 article reviews
    ehr data extraction - by Bioz Stars, 2026-11
    90/100 stars

    Images

    1) Product Images from "Care-paradigm shift promoting smoking cessation treatment among cancer center patients via a low-burden strategy, Electronic Health Record-Enabled Evidence-Based Smoking Cessation Treatment"

    Article Title: Care-paradigm shift promoting smoking cessation treatment among cancer center patients via a low-burden strategy, Electronic Health Record-Enabled Evidence-Based Smoking Cessation Treatment

    Journal: Translational Behavioral Medicine

    doi: 10.1093/tbm/ibz107

    Conventional tobacco-use treatment specialist referral versus point-of-care treatment models. The conventional model relies on successful referrals by the clinical care team to a tobacco-use treatment specialist. Electronic health record (EHR) functionality enables the point-of-care model to efficiently deliver treatment.
    Figure Legend Snippet: Conventional tobacco-use treatment specialist referral versus point-of-care treatment models. The conventional model relies on successful referrals by the clinical care team to a tobacco-use treatment specialist. Electronic health record (EHR) functionality enables the point-of-care model to efficiently deliver treatment.

    Techniques Used:

    ELEVATE patient care workflow: A point-of-care treatment model for smoking cessation via the “5 A’s.” ELEVATE facilitates “5 A’s” patient care by harnessing Epic’s Best Practice Advisory (BPA) functionality to prompt and integrate intervention actions by clinical care providers (e.g., nurses or medical assistants) and prescribing clinicians within the electronic health record (EHR).
    Figure Legend Snippet: ELEVATE patient care workflow: A point-of-care treatment model for smoking cessation via the “5 A’s.” ELEVATE facilitates “5 A’s” patient care by harnessing Epic’s Best Practice Advisory (BPA) functionality to prompt and integrate intervention actions by clinical care providers (e.g., nurses or medical assistants) and prescribing clinicians within the electronic health record (EHR).

    Techniques Used:

    Measure definitions of tobacco intervention rates, pre- and post-ELEVATE
    Figure Legend Snippet: Measure definitions of tobacco intervention rates, pre- and post-ELEVATE

    Techniques Used: Medications

    Pre- and post-ELEVATE comparisons of tobacco use assessment and treatment among Siteman Cancer Center outpatients. ELEVATE is associated with significantly increased tobacco use assessment and documented treatment
    Figure Legend Snippet: Pre- and post-ELEVATE comparisons of tobacco use assessment and treatment among Siteman Cancer Center outpatients. ELEVATE is associated with significantly increased tobacco use assessment and documented treatment

    Techniques Used:

    “5 A’s” implementation with ELEVATE. The patient flow diagram demonstrates the electronic health record (EHR)-facilitated implementation of the “5 A’s” tobacco intervention through the outpatient oncology clinic workflow alongside rates of completion for each of the “5 A’s.”
    Figure Legend Snippet: “5 A’s” implementation with ELEVATE. The patient flow diagram demonstrates the electronic health record (EHR)-facilitated implementation of the “5 A’s” tobacco intervention through the outpatient oncology clinic workflow alongside rates of completion for each of the “5 A’s.”

    Techniques Used:

    Related Articles

    other:

    Article Title: Real‐world use of tirzepatide among individuals without evidence of type 2 diabetes: Results from the Veradigm® database
    Article Snippet: The Veradigm linked database integrates data from Veradigm's EHR platforms, which serve primary care and specialist physicians (including components from Allscripts, Practice Fusion and NextGen EHR), with closed pharmacy and medical claims data, capturing all of a patient's healthcare that is covered by insurance (including prescriptions).

    Article Title: Association Between Socioeconomic Factors, Race, and Use of a Specialty Memory Clinic
    Article Snippet: Structured EHR data, including patient addresses, self-reported race, biological sex, insurance information, completed laboratory tests and procedures, and diagnoses, and unstructured clinical notes were extracted from outpatient records stored in the Allscripts (Chicago, IL) TouchWorks database.

    Article Title: Prevalence of renal and bone risk factors among individuals prescribed oral pre-exposure prophylaxis for HIV
    Article Snippet: This retrospective observational prevalence study analyzed patient-level EHR data (from January 1, 2015 through February 29, 2020) for individuals prescribed oral PrEP (including pharmacy and medical claims data) in the Veradigm Health Insights Database, which consists of United States-based Allscripts ambulatory hosted and on-premise EHR data.

    Article Title: Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record’s Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study
    Article Snippet: From the Allscripts UBMD practice EHR data, we found 2722 potential patients with NVAF using the structured and NLP method and 1849 cases using only ICD-9 codes.

    Comparison:




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    Conventional tobacco-use treatment specialist referral versus point-of-care treatment models. The conventional model relies on successful referrals by the clinical care team to a tobacco-use treatment specialist. <t>Electronic</t> <t>health</t> <t>record</t> <t>(EHR)</t> functionality enables the point-of-care model to efficiently deliver treatment.
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    data extracted from french primary care ehrs  (Cegedim Strategic Data Medical Research Ltd)
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    Conventional tobacco-use treatment specialist referral versus point-of-care treatment models. The conventional model relies on successful referrals by the clinical care team to a tobacco-use treatment specialist. <t>Electronic</t> <t>health</t> <t>record</t> <t>(EHR)</t> functionality enables the point-of-care model to efficiently deliver treatment.
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    Image Search Results


    Dataset creation for the ambulatory electrocardiogram-convolutional neural network model development. Schematic indicates the strategy to obtain robust and reliable dataset for model development. To avoid cross-contamination, no patient data are repeated among training, validation, and testing datasets. *Patient count ( n = 5829) selected based on several inclusion, exclusion criteria to ensure the quality of LVEF data as they are obtained from Optum ® EHR and the dataset was captured in Optum ® EHR via natural language processing of procedure/diagnostic notes and prone to natural language processing errors.

    Journal: European Heart Journal. Digital Health

    Article Title: Dynamic risk stratification of worsening heart failure using a deep learning-enabled implanted ambulatory single-lead electrocardiogram

    doi: 10.1093/ehjdh/ztae035

    Figure Lengend Snippet: Dataset creation for the ambulatory electrocardiogram-convolutional neural network model development. Schematic indicates the strategy to obtain robust and reliable dataset for model development. To avoid cross-contamination, no patient data are repeated among training, validation, and testing datasets. *Patient count ( n = 5829) selected based on several inclusion, exclusion criteria to ensure the quality of LVEF data as they are obtained from Optum ® EHR and the dataset was captured in Optum ® EHR via natural language processing of procedure/diagnostic notes and prone to natural language processing errors.

    Article Snippet: To ensure robust and reliable LVEF information and minimize errors introduced through automated data extraction from Optum ® EHR , we only included patients with prior HF diagnosis to create the low ejection fraction (EF) data (LVEF ≤ 40%).

    Techniques: Biomarker Discovery, Diagnostic Assay

    Conventional tobacco-use treatment specialist referral versus point-of-care treatment models. The conventional model relies on successful referrals by the clinical care team to a tobacco-use treatment specialist. Electronic health record (EHR) functionality enables the point-of-care model to efficiently deliver treatment.

    Journal: Translational Behavioral Medicine

    Article Title: Care-paradigm shift promoting smoking cessation treatment among cancer center patients via a low-burden strategy, Electronic Health Record-Enabled Evidence-Based Smoking Cessation Treatment

    doi: 10.1093/tbm/ibz107

    Figure Lengend Snippet: Conventional tobacco-use treatment specialist referral versus point-of-care treatment models. The conventional model relies on successful referrals by the clinical care team to a tobacco-use treatment specialist. Electronic health record (EHR) functionality enables the point-of-care model to efficiently deliver treatment.

    Article Snippet: EHR data extraction Pre-ELEVATE EHR data were extracted from Allscripts TouchWorks for oncology patients completing outpatient encounters from January 1, 2018 to June 1, 2018.

    Techniques:

    ELEVATE patient care workflow: A point-of-care treatment model for smoking cessation via the “5 A’s.” ELEVATE facilitates “5 A’s” patient care by harnessing Epic’s Best Practice Advisory (BPA) functionality to prompt and integrate intervention actions by clinical care providers (e.g., nurses or medical assistants) and prescribing clinicians within the electronic health record (EHR).

    Journal: Translational Behavioral Medicine

    Article Title: Care-paradigm shift promoting smoking cessation treatment among cancer center patients via a low-burden strategy, Electronic Health Record-Enabled Evidence-Based Smoking Cessation Treatment

    doi: 10.1093/tbm/ibz107

    Figure Lengend Snippet: ELEVATE patient care workflow: A point-of-care treatment model for smoking cessation via the “5 A’s.” ELEVATE facilitates “5 A’s” patient care by harnessing Epic’s Best Practice Advisory (BPA) functionality to prompt and integrate intervention actions by clinical care providers (e.g., nurses or medical assistants) and prescribing clinicians within the electronic health record (EHR).

    Article Snippet: EHR data extraction Pre-ELEVATE EHR data were extracted from Allscripts TouchWorks for oncology patients completing outpatient encounters from January 1, 2018 to June 1, 2018.

    Techniques:

    Measure definitions of tobacco intervention rates, pre- and post-ELEVATE

    Journal: Translational Behavioral Medicine

    Article Title: Care-paradigm shift promoting smoking cessation treatment among cancer center patients via a low-burden strategy, Electronic Health Record-Enabled Evidence-Based Smoking Cessation Treatment

    doi: 10.1093/tbm/ibz107

    Figure Lengend Snippet: Measure definitions of tobacco intervention rates, pre- and post-ELEVATE

    Article Snippet: EHR data extraction Pre-ELEVATE EHR data were extracted from Allscripts TouchWorks for oncology patients completing outpatient encounters from January 1, 2018 to June 1, 2018.

    Techniques: Medications

    Pre- and post-ELEVATE comparisons of tobacco use assessment and treatment among Siteman Cancer Center outpatients. ELEVATE is associated with significantly increased tobacco use assessment and documented treatment

    Journal: Translational Behavioral Medicine

    Article Title: Care-paradigm shift promoting smoking cessation treatment among cancer center patients via a low-burden strategy, Electronic Health Record-Enabled Evidence-Based Smoking Cessation Treatment

    doi: 10.1093/tbm/ibz107

    Figure Lengend Snippet: Pre- and post-ELEVATE comparisons of tobacco use assessment and treatment among Siteman Cancer Center outpatients. ELEVATE is associated with significantly increased tobacco use assessment and documented treatment

    Article Snippet: EHR data extraction Pre-ELEVATE EHR data were extracted from Allscripts TouchWorks for oncology patients completing outpatient encounters from January 1, 2018 to June 1, 2018.

    Techniques:

    “5 A’s” implementation with ELEVATE. The patient flow diagram demonstrates the electronic health record (EHR)-facilitated implementation of the “5 A’s” tobacco intervention through the outpatient oncology clinic workflow alongside rates of completion for each of the “5 A’s.”

    Journal: Translational Behavioral Medicine

    Article Title: Care-paradigm shift promoting smoking cessation treatment among cancer center patients via a low-burden strategy, Electronic Health Record-Enabled Evidence-Based Smoking Cessation Treatment

    doi: 10.1093/tbm/ibz107

    Figure Lengend Snippet: “5 A’s” implementation with ELEVATE. The patient flow diagram demonstrates the electronic health record (EHR)-facilitated implementation of the “5 A’s” tobacco intervention through the outpatient oncology clinic workflow alongside rates of completion for each of the “5 A’s.”

    Article Snippet: EHR data extraction Pre-ELEVATE EHR data were extracted from Allscripts TouchWorks for oncology patients completing outpatient encounters from January 1, 2018 to June 1, 2018.

    Techniques: