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115 results for “electronic health records”
OpenChart-SE: A corpus of artificial Swedish electronic health records for imagined emergency care patients written by physicians in a crowd-sourcing project
<p>Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>), where additional batches of quality-controlled EHRs will be released periodically. </p> <p> </p> <p><strong>Dataset content</strong></p> <p><em>OpenChart-SE, version 1 corpus (txt files and and dataset.csv)</em></p> <p>The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication.</p> <p> </p> <p><em>Codebook.xlsx</em></p> <p>The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection.</p> <p> </p> <p><em>suppl_data_1_openchart-se_form.pdf</em></p> <p>OpenChart-SE mock emergency care EHR form.</p> <p> </p> <p><em>suppl_data_3_openchart-se_dataexploration.ipynb</em></p> <p>This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus.</p> <p> </p> <p>More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>).</p>
Covid-19 Vaccine Monitoring project (CVM)-Electronic Health Record data sources Codelist
<p>This is the code list that was used to identify outcomes and covariates (those tagged as in narrow) in electronic health records of participating data sources in the the CVM study which was addressing the following questions</p> <p> </p> <p>1)<strong> To create and assess readiness of electronic health record data sources for rapid evaluation of safety signals by </strong></p> <ul> <li> <p>Providing an overview of the methods for identification of COVID-19 vaccine exposure in the data sources </p> </li> <li> <p>Monitoring the number of individuals exposed to any COVID-19 vaccine and to compare this to COVID-19 vaccine exposure (benchmark: ECDC vaccine tracker)1 </p> </li> <li> <p>Generation of updated background rates for AESIs </p> </li> </ul> <p><strong>2) To conduct rapid safety assessment studies using electronic healthcare records and support EMA safety assessments. </strong></p> <p>The protocol for this study is publicly available www.encepp.eu/encepp/viewResource.htm?id=42637. The report with results using the code list is publicly available on Zenodo as well. </p> <p> </p> <p> </p> <p> </p>
Antimicrobial Resistance Microbiological Dataset (ARMD-UTSW): A deidentified collection of electronic health records, from a quaternary, academic medical center, for antimicrobial resistance research
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Antimicrobial Resistance Microbiological Dataset (ARMD-ECUH): A deidentified collection of electronic health records from a rural academic health system for antimicrobial resistance research
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Software Product Quality Evaluation Guide for Electronic Health Record Systems
<p>Apresentação do artigo "Software Product Quality Evaluation Guide for Electronic Health Record Systems".</p> <p>SBES 2020 - Trilha de Ideias Inovadoras e Resultados Emergentes.</p>
An ordinal severity scale for COVID-19 retrospective studies using electronic health record data
<p><span>Objectives: </span><span>Although the World Health Organization (WHO) Clinical Progression Scale for COVID-19 is useful in prospective clinical trials, it cannot be effectively used with retrospective Electronic Health Record (EHR) datasets. Modifying the existing WHO Clinical Progression Scale, we developed an ordinal severity scale (OS) and assessed its usefulness in the analyses of COVID-19 patient outcomes using retrospective EHR data.</span></p> <p><span>Results: </span><span>The data set used in this analysis consists of 2,880,456</span> <span>patients</span><span>. PCA of the day-to-day variation in OS levels over the totality of the 28-day</span> <span>period revealed contrasting patterns of variation in disease severity within the first and second 14 days and illustrated the importance of evaluation over the full 28-day period.</span></p> <p><span>Discussion:</span><span> An OS with well-defined, robust </span><span>features, based on discrete EHR data elements, is </span><span>useful for assessments of COVID-19 patient outcomes, providing insights on progression of COVID-19 disease severity over time.</span></p> <p><span>Conclusion</span><span>: The </span><span>OS </span><span>provides a framework which can facilitate better understanding of the course of acute COVID-19, informing clinical decision-making and resource allocation.</span></p>
Patient-reported outcomes via electronic health record portal vs. telephone: process and retention data in a pilot trial of anxiety or depression symptoms in epilepsy
<p>Objective: To close gaps between research and clinical practice, tools are needed for efficient pragmatic trial recruitment and patient-reported outcome(PROM) collection. The objective was to assess feasibility and process measures for patient-reported outcome collection in a randomized trial comparing electronic health record(EHR) patient portal questionnaires to telephone interview among adults with epilepsy and anxiety or depression symptoms.</p> <p>Results: Participants were 60% women, 77% White/non-Hispanic, with mean age 42.5 years. Among 15 individuals randomized to EHR portal, 10(67%, CI 41.7-84.8%) met the 6-month retention endpoint, versus 100%(CI 79.6-100%) in the telephone group(p=0.04). EHR outcome collection at 6 months required 11.8 minutes less research staff time per participant than telephone (5.9, CI 3.3-7.7 vs. 17.7, CI 14.1-20.2). Subsequent telephone contact after unsuccessful EHR attempts enabled near complete data collection and still saved staff time.</p> <p>Discussion: Data from this randomized pilot study of pragmatic outcome collection methods for patients with anxiety or depression symptoms in epilepsy includes baseline participant characteristics, recruitment flow resulting from a novel EHR-based, care-embedded recruitment process, and data on retention along with various process measures at 6-months.</p>
An Electronic Health Record-based Approach to Increase PrEP Knowledge and Uptake: the EMC2 PrEP Strategy
ClinicalTrials.gov study NCT05709860. IPD Sharing: NO. Countries: 1. Publications: 1.
Emulation of the KEYNOTE-189 Trial Using Electronic Health Records
ClinicalTrials.gov study NCT05908799. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Assessing the Performance of Artificial Intelligence (AI)-Augmented Electronic Health Record (EHR) Data Abstraction for Clinical Trial Patient Screening
ClinicalTrials.gov study NCT06561217. IPD Sharing: NO. Countries: 1. Publications: 1.
Participatory Design of Electronic Health Record Tools for Problem Solving Therapy
ClinicalTrials.gov study NCT03516513. IPD Sharing: YES. Countries: 1. Publications: 1.
Electronic Health Record-leveraged, Patient-centered, Intensification of Chronic Care for HF
ClinicalTrials.gov study NCT03334188. IPD Sharing: NO. Countries: 1. Publications: 2.
Electronic Health Record-Based Clinical Decision Support to Improve Blood Pressure Management in Adolescents
ClinicalTrials.gov study NCT01760239. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Effectiveness of Electronic Health Record-Based Interventions for Improving Follow-Up in Primary Care
ClinicalTrials.gov study NCT01346839. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Optimizing Electronic Health Record Prompts With Behavioral Economics to Improve Prescribing for Older Adults
ClinicalTrials.gov study NCT04284553. IPD Sharing: Not stated. Countries: 1. Publications: 2.
PRescribing INterventions for Chronic Pain Via the Electronic Health Record Study - Primary Care Providers
ClinicalTrials.gov study NCT04601506. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: External validation of an electronic health record-based diagnostic model for histological acute tubulointerstitial nephritis
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An ordinal severity scale for COVID-19 retrospective studies using electronic health record data
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Feasibility of a contraceptive-specific electronic health record system to promote the adoption of pharmacist-prescribed contraceptive services in community pharmacies in the United States
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Using routinely available electronic health record data elements to develop and validate a digital divide risk score
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