The Clinical Value of Electronic Health Record: a systematic review and meta-analysis

2014 ◽  
Vol 24 (suppl_2) ◽  
Author(s):  
P Campanella ◽  
ML Specchia ◽  
C Marone ◽  
L Fallacara ◽  
A Mancuso ◽  
...  
BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e037405
Author(s):  
Daniel Dedman ◽  
Melissa Cabecinha ◽  
Rachael Williams ◽  
Stephen J W Evans ◽  
Krishnan Bhaskaran ◽  
...  

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.


2019 ◽  
Vol 27 (3) ◽  
pp. 480-490 ◽  
Author(s):  
Adam Rule ◽  
Michael F Chiang ◽  
Michelle R Hribar

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.


Informatics ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 25
Author(s):  
Terrence C. Lee ◽  
Neil U. Shah ◽  
Alyssa Haack ◽  
Sally L. Baxter

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.


2017 ◽  
Vol 5 (4) ◽  
pp. e44 ◽  
Author(s):  
Assel Syzdykova ◽  
André Malta ◽  
Maria Zolfo ◽  
Ermias Diro ◽  
José Luis Oliveira

PLoS ONE ◽  
2019 ◽  
Vol 14 (10) ◽  
pp. e0224272
Author(s):  
Samantha S. R. Crossfield ◽  
Lana Yin Hui Lai ◽  
Sarah R. Kingsbury ◽  
Paul Baxter ◽  
Owen Johnson ◽  
...  

2020 ◽  
Author(s):  
Nooshin Ghodsian ◽  
Erik Abner ◽  
Émilie Gobeil ◽  
Nele Taba ◽  
Alexis St-Amand ◽  
...  

Abstract Non-alcoholic fatty liver disease (NAFLD) has been associated with several blood biomarkers and chronic diseases. Whether these associations underlie causal effects remains to be determined. We aimed at identifying blood metabolites, blood proteins and human diseases that are causally impacted by the presence of NAFLD using Mendelian randomization. We created a NAFLD genetic instrument from NAFLD loci (MTARC1, GCKR, LPL, TRIB1, LMO3, FTO, TM6SF2, APOE and PNPLA3) identified in a new electronic health record based-GWAS meta-analysis (6715 cases and 682,748 controls). We found a potentially causal effect of NAFLD on tyrosine metabolism as well as on blood levels of eight proteins that could potentially represent new early biomarkers of NAFLD. Using results from the UK Biobank, FinnGen and the COVID-19 Host Genetics Initiative, we found that NAFLD was not causally associated with diseases outside the spectrum of liver diseases, suggesting that the resolution of NAFLD might not prevent other diseases.


2008 ◽  
Vol 17 (01) ◽  
pp. 128-144 ◽  
Author(s):  
G. K. Savova ◽  
K. C. Kipper-Schuler ◽  
J. F. Hurdle ◽  
S. M. Meystre

Summary Objectives We examine recent published research on the extraction of information from textual documents in the Electronic Health Record (EHR). Methods Literature review of the research published after 1995, based on PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers already included. Results 174 publications were selected and are discussed in this review in terms of methods used, pre-processing of textual documents, contextual features detection and analysis, extraction of information in general, extraction of codes and of information for decision-support and enrichment of the EHR, information extraction for surveillance, research, automated terminology management, and data mining, and de-identification of clinical text. Conclusions Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in. Competitive challenges for information extraction from clinical text, along with the availability of annotated clinical text corpora, and further improvements in system performance are important factors to stimulate advances in this field and to increase the acceptance and usage of these systems in concrete clinical and biomedical research contexts.


JAMIA Open ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 294-303 ◽  
Author(s):  
Ruth A Bush ◽  
Alexa Pérez ◽  
Tanja Baum ◽  
Caroline Etland ◽  
Cynthia D Connelly

Abstract Objectives Globally, healthcare systems are using the electronic health record (EHR) and elements of clinical decision support (CDS) to facilitate palliative care (PC). Examination of published results is needed to determine if the EHR is successfully supporting the multidisciplinary nature and complexity of PC by identifying applications, methodology, outcomes, and barriers of active incorporation of the EHR in PC clinical workflow. Methods A systematic review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources PubMed, CINAL, EBSCOhost, and Academic Search Premier were used to identify literature published 1999–2017 of human subject peer-reviewed articles in English containing original research about the EHR and PC. Results The search returned 433 articles, 30 of which met inclusion criteria. Most studies were feasibility studies or retrospective cohort analyses; one study incorporated prospective longitudinal mixed methods. Twenty-three of 30 (77%) were published after 2014. The review identified five major areas in which the EHR is used to support PC. Studies focused on CDS to: identify individuals who could benefit from PC; electronic advanced care planning (ACP) documentation; patient-reported outcome measures (PROMs) such as rapid, real-time pain feedback; to augment EHR PC data capture capabilities; and to enhance interdisciplinary communication and care. Discussion Beginning in 2015, there was a proliferation of articles about PC and EHRs, suggesting increasing incorporation of and research about the EHR with PC. This review indicates the EHR is underutilized for PC CDS, facilitating PROMs, and capturing ACPs.


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