scholarly journals Open-Source Electronic Health Record Systems for Low-Resource Settings: Systematic Review

2017 ◽  
Vol 5 (4) ◽  
pp. e44 ◽  
Author(s):  
Assel Syzdykova ◽  
André Malta ◽  
Maria Zolfo ◽  
Ermias Diro ◽  
José Luis Oliveira
Keyword(s):  
Systematic Review ◽  
Electronic Health Record ◽  
Open Source ◽  
Health Record ◽  
Low Resource Settings ◽  
Low Resource ◽  
Electronic Health

scholarly journals Approaches for combining primary care electronic health record data from multiple sources: a systematic review of observational studies

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e037405
Author(s):  
Daniel Dedman ◽  
Melissa Cabecinha ◽  
Rachael Williams ◽  
Stephen J W Evans ◽  
Krishnan Bhaskaran ◽  
...  
Keyword(s):  
Systematic Review ◽  
Primary Care ◽  
Electronic Health Record ◽  
Observational Studies ◽  
Meta Analysis ◽  
Data Sources ◽  
Health Record ◽  
Two Stage ◽  
One Stage ◽  
Electronic Health

ObjectiveTo identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.DesignA systematic review of published studies.Data sourcesPubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.Study selectionObservational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.Results6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.ConclusionsComparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.

Assessing HIPAA Compliance of Open Source Electronic Health Record Applications

2021 ◽  
Vol 15 (2) ◽  
pp. 181-195
Author(s):  
Hossain Shahriar ◽  
Hisham M. Haddad ◽  
Maryam Farhadi
Keyword(s):  
Electronic Health Record ◽  
Open Source ◽  
Medical Decision ◽  
Patient Treatment ◽  
Health Record ◽  
Technical Requirements ◽  
Patient Health ◽  
Treatment Plans ◽  
Electronic Health ◽  
Quality In Healthcare

Electronic health record (EHR) applications are digital versions of paper-based patient health information. EHR applications are increasingly being adopted in many countries. They have resulted in improved quality in healthcare, convenient access to histories of patient medication and clinic visits, easier follow up of patient treatment plans, and precise medical decision-making process. The goal of this paper is to identify HIPAA technical requirements, evaluate two open source EHR applications (OpenEMR and OpenClinic) for security vulnerabilities using two open-source scanner tools (RIPS and PHP VulnHunter), and map the identified vulnerabilities to HIPAA technical requirements.

scholarly journals Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data

2019 ◽  
Author(s):  
Daniel M. Bean ◽  
James Teo ◽  
Honghan Wu ◽  
Ricardo Oliveira ◽  
Raj Patel ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Natural Language Processing ◽  
Natural Language ◽  
Electronic Health Record ◽  
Open Source ◽  
Language Processing ◽  
Risk Scores ◽  
Free Text ◽  
Health Record ◽  
Electronic Health

AbstractAtrial fibrillation (AF) is the most common arrhythmia and significantly increases stroke risk. This risk is effectively managed by oral anticoagulation. Recent studies using national registry data indicate increased use of anticoagulation resulting from changes in guidelines and the availability of newer drugs.The aim of this study is to develop and validate an open source risk scoring pipeline for free-text electronic health record data using natural language processing.AF patients discharged from 1st January 2011 to 1st October 2017 were identified from discharge summaries (N=10,030, 64.6% male, average age 75.3 ± 12.3 years). A natural language processing pipeline was developed to identify risk factors in clinical text and calculate risk for ischaemic stroke (CHA2DS2-VASc) and bleeding (HAS-BLED). Scores were validated vs two independent experts for 40 patients.Automatic risk scores were in strong agreement with the two independent experts for CHA2DS2-VASc (average kappa 0.78 vs experts, compared to 0.85 between experts). Agreement was lower for HAS-BLED (average kappa 0.54 vs experts, compared to 0.74 between experts).In high-risk patients (CHA2DS2-VASc ≥2) OAC use has increased significantly over the last 7 years, driven by the availability of DOACs and the transitioning of patients from AP medication alone to OAC. Factors independently associated with OAC use included components of the CHA2DS2-VASc and HAS-BLED scores as well as discharging specialty and frailty. OAC use was highest in patients discharged under cardiology (69%).Electronic health record text can be used for automatic calculation of clinical risk scores at scale. Open source tools are available today for this task but require further validation. Analysis of routinely-collected EHR data can replicate findings from large-scale curated registries.

scholarly journals Using electronic health record audit logs to study clinical activity: a systematic review of aims, measures, and methods

2019 ◽  
Vol 27 (3) ◽  
pp. 480-490 ◽  
Author(s):  
Adam Rule ◽  
Michael F Chiang ◽  
Michelle R Hribar
Keyword(s):  
Systematic Review ◽  
Access Control ◽  
Electronic Health Record ◽  
Chart Review ◽  
Observational Research ◽  
Clinical Activity ◽  
Health Record ◽  
Problem List ◽  
Inclusion Criteria ◽  
Electronic Health

Abstract Objective To systematically review published literature and identify consistency and variation in the aims, measures, and methods of studies using electronic health record (EHR) audit logs to observe clinical activities. Materials and Methods In July 2019, we searched PubMed for articles using EHR audit logs to study clinical activities. We coded and clustered the aims, measures, and methods of each article into recurring categories. We likewise extracted and summarized the methods used to validate measures derived from audit logs and limitations discussed of using audit logs for research. Results Eighty-five articles met inclusion criteria. Study aims included examining EHR use, care team dynamics, and clinical workflows. Studies employed 6 key audit log measures: counts of actions captured by audit logs (eg, problem list viewed), counts of higher-level activities imputed by researchers (eg, chart review), activity durations, activity sequences, activity clusters, and EHR user networks. Methods used to preprocess audit logs varied, including how authors filtered extraneous actions, mapped actions to higher-level activities, and interpreted repeated actions or gaps in activity. Nineteen studies validated results (22%), but only 9 (11%) through direct observation, demonstrating varying levels of measure accuracy. Discussion While originally designed to aid access control, EHR audit logs have been used to observe diverse clinical activities. However, most studies lack sufficient discussion of measure definition, calculation, and validation to support replication, comparison, and cross-study synthesis. Conclusion EHR audit logs have potential to scale observational research but the complexity of audit log measures necessitates greater methodological transparency and validated standards.

scholarly journals Clinical Implementation of Predictive Models Embedded within Electronic Health Record Systems: A Systematic Review

Informatics ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 25
Author(s):  
Terrence C. Lee ◽  
Neil U. Shah ◽  
Alyssa Haack ◽  
Sally L. Baxter
Keyword(s):  
Systematic Review ◽  
Electronic Health Record ◽  
Clinical Outcomes ◽  
Predictive Models ◽  
Performance Metrics ◽  
Predictive Analytics ◽  
Health Record ◽  
Clinical Implementation ◽  
Level Of Evidence ◽  
Electronic Health

Predictive analytics using electronic health record (EHR) data have rapidly advanced over the last decade. While model performance metrics have improved considerably, best practices for implementing predictive models into clinical settings for point-of-care risk stratification are still evolving. Here, we conducted a systematic review of articles describing predictive models integrated into EHR systems and implemented in clinical practice. We conducted an exhaustive database search and extracted data encompassing multiple facets of implementation. We assessed study quality and level of evidence. We obtained an initial 3393 articles for screening, from which a final set of 44 articles was included for data extraction and analysis. The most common clinical domains of implemented predictive models were related to thrombotic disorders/anticoagulation (25%) and sepsis (16%). The majority of studies were conducted in inpatient academic settings. Implementation challenges included alert fatigue, lack of training, and increased work burden on the care team. Of 32 studies that reported effects on clinical outcomes, 22 (69%) demonstrated improvement after model implementation. Overall, EHR-based predictive models offer promising results for improving clinical outcomes, although several gaps in the literature remain, and most study designs were observational. Future studies using randomized controlled trials may help improve the generalizability of findings.

scholarly journals Open source cardiology electronic health record development for DIGICARDIAC implementation

2015 ◽  
Author(s):  
Nelson Dugarte ◽  
Rubén Medina ◽  
Lourdes Huiracocha ◽  
Rubén Rojas
Keyword(s):  
Electronic Health Record ◽  
Open Source ◽  
Health Record ◽  
Electronic Health
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