scholarly journals Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review

2020 ◽  
pp. 103671
Author(s):  
Yuqi Si ◽  
Jingcheng Du ◽  
Zhao Li ◽  
Xiaoqian Jiang ◽  
Timothy Miller ◽  
...  
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Representation Learning ◽  
Patient Data ◽  
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

Privacy-Friendly Management of Electronic Health Records in the eHealth Context

2015 ◽  
pp. 251-264
Author(s):  
Milica Milutinovic ◽  
Bart De Decker
Keyword(s):  
Electronic Health Records ◽  
Large Scale ◽  
Patient Data ◽  
Health Records ◽  
Privacy Concerns ◽  
Ubiquitous Technology ◽  
Emergency Situations ◽  
User Centric ◽  
Electronic Health

Electronic Health Records (EHRs) are becoming the ubiquitous technology for managing patients' records in many countries. They allow for easier transfer and analysis of patient data on a large scale. However, privacy concerns linked to this technology are emerging. Namely, patients rarely fully understand how EHRs are managed. Additionally, the records are not necessarily stored within the organization where the patient is receiving her healthcare. This service may be delegated to a remote provider, and it is not always clear which health-provisioning entities have access to this data. Therefore, in this chapter the authors propose an alternative where users can keep and manage their records in their existing eHealth systems. The approach is user-centric and enables the patients to have better control over their data while still allowing for special measures to be taken in case of emergency situations with the goal of providing the required care to the patient.

scholarly journals Deep representation learning of electronic health records to unlock patient stratification at scale

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Isotta Landi ◽  
Benjamin S. Glicksberg ◽  
Hao-Chih Lee ◽  
Sarah Cherng ◽  
Giulia Landi ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Representation Learning ◽  
Patient Stratification ◽  
Health Records ◽  
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.

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