Which electronic health record system should we use? - a systematic review (Preprint)

2020 ◽  
Author(s):  
Vanash Patel ◽  
George Garas ◽  
James Hollingshead ◽  
Drostan Cheetham ◽  
Thanos Athanasiou ◽  
...  
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Research And Development ◽  
User Satisfaction ◽  
Electronic Records ◽  
Published Data ◽  
Health Records ◽  
Electronic Health ◽  
And Performance ◽  
The Uk

BACKGROUND Electronic health records are digital records of a patient’s health and care. At present in the UK, patients may have several paper and electronic records stored in various settings. The UK government, via NHS England, intends to introduce a comprehensive system of electronic health records in England by 2020. These electronic records will run across primary, secondary and social care linking all data in a single digital platform. OBJECTIVE This is the first systematic review to look at all published data on EHRs to determine which systems are advantageous. METHODS Design: A systematic review was performed by searching EMBASE and Ovid MEDLINE between 1974 and November 2019. Participants: All original studies that appraised EHR systems were included. Main outcome measures: EHR system comparison, implementation, user satisfaction, efficiency and performance, documentation, and research and development. RESULTS The search strategy identified 701 studies, which were filtered down to 46 relevant studies. Level of evidence ranged from 1 to 4 according to the Oxford Centre for Evidence-based Medicine. The majority of the studies were performed in the USA (n = 44). N=6 studies compared more than one EHR, and Epic followed by Cerner were the most favourable through direct comparison. N=17 studies evaluated implementation which highlighted that it was challenging, and productivity dipped in the early phase. N=5 studies reflected on user satisfaction, with women demonstrating higher satisfaction than men. Efficiency and performance issues were the driving force behind user dissatisfaction. N=26 studies addressed efficiency and performance, which improved with long-term use and familiarity. N=18 studies considered documentation and showed that EHRs had a positive impact with basic and speciality tasks. N=29 studies assessed research and development which revealed vast capabilities and positive implications. CONCLUSIONS Epic is the most studied EHR system and the most commonly used vendor on the market. There is limited comparative data between EHR vendors, so it is difficult to assess which is the most advantageous system.

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.

scholarly journals Which electronic health record system should we use? – a systematic review

2020 ◽  
Author(s):  
Mohammed Al Ani ◽  
George Garas ◽  
James Hollingshead ◽  
Drostan Cheetham ◽  
Thanos Athanasiou ◽  
...  
Keyword(s):  
Systematic Review ◽  
Research And Development ◽  
User Satisfaction ◽  
Positive Impact ◽  
Published Data ◽  
Level Of Evidence ◽  
Record System ◽  
Ovid Medline ◽  
The Usa ◽  
And Performance

ABSTRACTObjectivesThis is the first systematic review to look at all published data on EHRs to determine which systems are advantageous.DesignA systematic review was performed by searching EMBASE and Ovid MEDLINE between 1974 and November 2019.ParticipantsAll original studies that appraised EHR systems were included.Main outcome measuresEHR system comparison, implementation, user satisfaction, efficiency and performance, documentation, and research and development.ResultsThe search strategy identified 701 studies, which were filtered down to 46 relevant studies. Level of evidence ranged from 1 to 4 according to the Oxford Centre for Evidence-based Medicine. The majority of the studies were performed in the USA (n = 44). N=6 studies compared more than one EHR, and Epic followed by Cerner were the most favourable through direct comparison. N=17 studies evaluated implementation which highlighted that it was challenging, and productivity dipped in the early phase. N=5 studies reflected on user satisfaction, with women demonstrating higher satisfaction than men. Efficiency and performance issues were the driving force behind user dissatisfaction. N=26 studies addressed efficiency and performance, which improved with long-term use and familiarity. N=18 studies considered documentation and showed that EHRs had a positive impact with basic and speciality tasks. N=29 studies assessed research and development which revealed vast capabilities and positive implications.ConclusionEpic is the most studied EHR system and the most commonly used vendor on the market. There is limited comparative data between EHR vendors, so it is difficult to assess which is the most advantageous system.

Analysis of end-user satisfaction with electronic health records in college/university healthcare

2020 ◽  
pp. 1-7
Author(s):  
Pasquale G. Frisina ◽  
Esther N. Munene ◽  
Janet Finnie ◽  
Judith E. Oakley ◽  
Gayathri Ganesan
Keyword(s):  
Electronic Health Records ◽  
User Satisfaction ◽  
End User ◽  
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

scholarly journals case study in distributed team science in research using electronic health records

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Jiao Song ◽  
Elizabeth Elliot ◽  
Andrew D Morris ◽  
Joannes J Kerssens ◽  
Ashley Akbari ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Oral Anticoagulants ◽  
Cost Effective ◽  
Study Cohort ◽  
Team Science ◽  
Data Types ◽  
Health Records ◽  
Distributed Team ◽  
Electronic Health ◽  
The Uk

IntroductionDue to various regulatory barriers, it is increasingly difficult to move pseudonymised routine health data across platforms and among jurisdictions. To tackle this challenge, we summarized five approaches considered to support a scientific research project focused on the risk of the new non-vitamin K Target Specific Oral Anticoagulants (TSOACs) and collaborated between the Farr institute in Wales and Scotland. ApproachIn Wales, routinely collected health records held in the Secure Anonymous Information Linkage (SAIL) Databank were used to identify the study cohort. In Scotland, data was extracted from national dataset resources administered by the eData Research & Innovation Service (eDRIS) and stored in the Scottish National Data Safe Haven. We adopted a federated data and multiple analysts approach, but arranged simultaneous accesses for Welsh and Scottish analysts to generate study cohorts separately by implementing the same algorithm. Our study cohort across two countries was boosted to 6,829 patients towards risk analysis. Source datasets and data types applied to generate cohorts were reviewed and compared by analysts based on both sites to ensure the consistency and harmonised output.  DiscussionThis project used a fusion of two approaches among five considered. The approach we adopted is a simple, yet efficient and cost-effective method to ensure consistency in analysis and coherence with multiple governance systems. It has limitations and potentials of extending and scaling. It can also be considered as an initialisation of a developing infrastructure to support a distributed team science approach to research using Electronic Health Records (EHRs) across the UK and more widely. KeywordsTeam science, cross-jurisdictional data linkage, electronic health records

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
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