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 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.

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 Electronic health record-based predictive models for acute kidney injury screening in pediatric inpatients

2017 ◽  
Vol 82 (3) ◽  
pp. 465-473 ◽  
Author(s):  
Li Wang ◽  
Tracy L McGregor ◽  
Deborah P Jones ◽  
Brian C Bridges ◽  
Geoffrey M Fleming ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Electronic Health Record ◽  
Predictive Models ◽  
Kidney Injury ◽  
Health Record ◽  
Electronic Health ◽  
Injury Screening

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
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