Secure Messaging in Electronic Health Records and Its Impact on Diabetes Clinical Outcomes: A Systematic Review

2016 ◽  
Vol 22 (9) ◽  
pp. 769-777 ◽  
Author(s):  
Alyce Kuo ◽  
Stuti Dang
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Clinical Outcomes ◽  
Health Records ◽  
Secure Messaging ◽  
Electronic Health

scholarly journals Adoption of Electronic Health Records and Perceptions of Financial and Clinical Outcomes Among Ophthalmologists in the United States

2018 ◽  
Vol 136 (2) ◽  
pp. 164 ◽  
Author(s):  
Michele C. Lim ◽  
Michael V. Boland ◽  
Colin A. McCannel ◽  
Arvind Saini ◽  
Michael F. Chiang ◽  
...  
Keyword(s):  
United States ◽  
Electronic Health Records ◽  
Clinical Outcomes ◽  
The United States ◽  
Health Records ◽  
Electronic Health

scholarly journals The impact of electronic health records on healthcare quality: a systematic review and meta-analysis

2015 ◽  
Vol 26 (1) ◽  
pp. 60-64 ◽  
Author(s):  
Paolo Campanella ◽  
Emanuela Lovato ◽  
Claudio Marone ◽  
Lucia Fallacara ◽  
Agostino Mancuso ◽  
...  
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Meta Analysis ◽  
Healthcare Quality ◽  
Health Records ◽  
Electronic Health ◽  
The Impact

scholarly journals Validity of acute cardiovascular outcome diagnoses in European electronic health records: a systematic review protocol

BMJ Open ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. e031373 ◽  
Author(s):  
Jennifer Anne Davidson ◽  
Amitava Banerjee ◽  
Rutendo Muzambi ◽  
Liam Smeeth ◽  
Charlotte Warren-Gash
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Predictive Value ◽  
Grey Literature ◽  
Cochrane Library ◽  
Free Text ◽  
Health Records ◽  
Coronary Syndrome ◽  
Validation Measure ◽  
Electronic Health

IntroductionCardiovascular diseases (CVDs) are among the leading causes of death globally. Electronic health records (EHRs) provide a rich data source for research on CVD risk factors, treatments and outcomes. Researchers must be confident in the validity of diagnoses in EHRs, particularly when diagnosis definitions and use of EHRs change over time. Our systematic review provides an up-to-date appraisal of the validity of stroke, acute coronary syndrome (ACS) and heart failure (HF) diagnoses in European primary and secondary care EHRs.Methods and analysisWe will systematically review the published and grey literature to identify studies validating diagnoses of stroke, ACS and HF in European EHRs. MEDLINE, EMBASE, SCOPUS, Web of Science, Cochrane Library, OpenGrey and EThOS will be searched from the dates of inception to April 2019. A prespecified search strategy of subject headings and free-text terms in the title and abstract will be used. Two reviewers will independently screen titles and abstracts to identify eligible studies, followed by full-text review. We require studies to compare clinical codes with a suitable reference standard. Additionally, at least one validation measure (sensitivity, specificity, positive predictive value or negative predictive value) or raw data, for the calculation of a validation measure, is necessary. We will then extract data from the eligible studies using standardised tables and assess risk of bias in individual studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data will be synthesised into a narrative format and heterogeneity assessed. Meta-analysis will be considered when a sufficient number of homogeneous studies are available. The overall quality of evidence will be assessed using the Grading of Recommendations, Assessment, Development and Evaluation tool.Ethics and disseminationThis is a systematic review, so it does not require ethical approval. Our results will be submitted for peer-review publication.PROSPERO registration numberCRD42019123898

Data structuring of electronic health records: a systematic review

2021 ◽  
Author(s):  
Jezer Machado de Oliveira ◽  
Cristiano André da Costa ◽  
Rodolfo Stoffel Antunes
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Health Records ◽  
Data Structuring ◽  
Electronic Health

scholarly journals Predictive value of indicators for identifying child maltreatment and intimate partner violence in coded electronic health records: a systematic review and meta-analysis

2020 ◽  
Vol 106 (1) ◽  
pp. 44-53
Author(s):  
Shabeer Syed ◽  
Rachel Ashwick ◽  
Marco Schlosser ◽  
Arturo Gonzalez-Izquierdo ◽  
Leah Li ◽  
...  
Keyword(s):  
Systematic Review ◽  
Intimate Partner Violence ◽  
Child Maltreatment ◽  
Electronic Health Records ◽  
Partner Violence ◽  
Meta Analysis ◽  
Intimate Partner ◽  
Health Records ◽  
Predictive Values ◽  
Electronic Health

ObjectiveElectronic health records (EHRs) are routinely used to identify family violence, yet reliable evidence of their validity remains limited. We conducted a systematic review and meta-analysis to evaluate the positive predictive values (PPVs) of coded indicators in EHRs for identifying intimate partner violence (IPV) and child maltreatment (CM), including prenatal neglect.MethodsWe searched 18 electronic databases between January 1980 and May 2020 for studies comparing any coded indicator of IPV or CM including prenatal neglect defined as neonatal abstinence syndrome (NAS) or fetal alcohol syndrome (FAS), against an independent reference standard. We pooled PPVs for each indicator using random effects meta-analyses.ResultsWe included 88 studies (3 875 183 individuals) involving 15 indicators for identifying CM in the prenatal period and childhood (0–18 years) and five indicators for IPV among women of reproductive age (12–50 years). Based on the International Classification of Disease system, the pooled PPV was over 80% for NAS (16 studies) but lower for FAS (<40%; seven studies). For young children, primary diagnoses of CM, specific injury presentations (eg, rib fractures and retinal haemorrhages) and assaults showed a high PPV for CM (pooled PPVs: 55.9%–87.8%). Indicators of IPV in women had a high PPV, with primary diagnoses correctly identifying IPV in >85% of cases.ConclusionsCoded indicators in EHRs have a high likelihood of correctly classifying types of CM and IPV across the life course, providing a useful tool for assessment, support and monitoring of high-risk groups in health services and research.

scholarly journals Pathways, Outcomes, and Costs in Colon Cancer: Retrospective Evaluations in Two Distinct Databases

2011 ◽  
Vol 7 (3S) ◽  
pp. 52s-59s ◽  
Author(s):  
J. Russell Hoverman ◽  
Thomas H. Cartwright ◽  
Debra A. Patt ◽  
Janet L. Espirito ◽  
Matthew P. Clayton ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Electronic Health Records ◽  
Clinical Outcomes ◽  
Evidence Based ◽  
Health Records ◽  
Electronic Health

Retrospective evaluations of electronic health records and claims databases to assess clinical outcomes and costs associated with evidence-based pathways in colon cancer.

scholarly journals Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review

2016 ◽  
Vol 24 (1) ◽  
pp. 198-208 ◽  
Author(s):  
Benjamin A Goldstein ◽  
Ann Marie Navar ◽  
Michael J Pencina ◽  
John P A Ioannidis
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Risk Prediction ◽  
Model Building ◽  
Prediction Models ◽  
Clinical Prediction ◽  
Health Records ◽  
Clinical Risk ◽  
Electronic Health ◽  
And Performance

Objective: Electronic health records (EHRs) are an increasingly common data source for clinical risk prediction, presenting both unique analytic opportunities and challenges. We sought to evaluate the current state of EHR based risk prediction modeling through a systematic review of clinical prediction studies using EHR data. Methods: We searched PubMed for articles that reported on the use of an EHR to develop a risk prediction model from 2009 to 2014. Articles were extracted by two reviewers, and we abstracted information on study design, use of EHR data, model building, and performance from each publication and supplementary documentation. Results: We identified 107 articles from 15 different countries. Studies were generally very large (median sample size = 26 100) and utilized a diverse array of predictors. Most used validation techniques (n = 94 of 107) and reported model coefficients for reproducibility (n = 83). However, studies did not fully leverage the breadth of EHR data, as they uncommonly used longitudinal information (n = 37) and employed relatively few predictor variables (median = 27 variables). Less than half of the studies were multicenter (n = 50) and only 26 performed validation across sites. Many studies did not fully address biases of EHR data such as missing data or loss to follow-up. Average c-statistics for different outcomes were: mortality (0.84), clinical prediction (0.83), hospitalization (0.71), and service utilization (0.71). Conclusions: EHR data present both opportunities and challenges for clinical risk prediction. There is room for improvement in designing such studies.

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