scholarly journals Cardiovascular Health Trends in Electronic Health Record Data (2012–2015): A Cross-Sectional Analysis of The Guideline Advantage™

2019 ◽  
Vol 7 (1) ◽  
pp. 30
Author(s):  
Joyce E. Rudy ◽  
Yosef Khan ◽  
Julie K. Bower ◽  
Sejal Patel ◽  
Randi E. Foraker
Keyword(s):  
Electronic Health Record ◽  
Cardiovascular Health ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Cross Sectional ◽  
Health Trends ◽  
Cross Sectional Analysis ◽  
Record Data ◽  
Electronic Health ◽  
Sectional Analysis

scholarly journals Enhanced Screening and Research Data Collection via Automated EHR Data Capture and Early Identification of Sepsis

2019 ◽  
Vol 5 ◽  
pp. 237796081985097
Author(s):  
Reba Umberger ◽  
Chayawat “Yo” Indranoi ◽  
Melanie Simpson ◽  
Rose Jensen ◽  
James Shamiyeh ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Data Extraction ◽  
Future Research ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Cross Sectional ◽  
Apply Research ◽  
Record Data ◽  
Electronic Health ◽  
Research Criteria

Clinical research in sepsis patients often requires gathering large amounts of longitudinal information. The electronic health record can be used to identify patients with sepsis, improve participant study recruitment, and extract data. The process of extracting data in a reliable and usable format is challenging, despite standard programming language. The aims of this project were to explore infrastructures for capturing electronic health record data and to apply criteria for identifying patients with sepsis. We conducted a prospective feasibility study to locate and capture/abstract electronic health record data for future sepsis studies. We located parameters as displayed to providers within the system and then captured data transmitted in Health Level Seven® interfaces between electronic health record systems into a prototype database. We evaluated our ability to successfully identify patients admitted with sepsis in the target intensive care unit (ICU) at two cross-sectional time points and then over a 2-month period. A majority of the selected parameters were accessible using an iterative process to locate and abstract them to the prototype database. We successfully identified patients admitted to a 20-bed ICU with sepsis using four data interfaces. Retrospectively applying similar criteria to data captured for 319 patients admitted to ICU over a 2-month period was less sensitive in identifying patients admitted directly to the ICU with sepsis. Classification into three admission categories (sepsis, no-sepsis, and other) was fair (Kappa .39) when compared with manual chart review. This project confirms reported barriers in data extraction. Data can be abstracted for future research, although more work is needed to refine and create customizable reports. We recommend that researchers engage their information technology department to electronically apply research criteria for improved research screening at the point of ICU admission. Using clinical electronic health records data to classify patients with sepsis over time is complex and challenging.

Automated chronic disease surveillance and visualization using electronic health record data

2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Michael Klompas ◽  
Chaim Kirby ◽  
Jason McVetta ◽  
Paul Oppedisano ◽  
John Brownstein ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Electronic Health Record ◽  
Disease Surveillance ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

Leveraging electronic health record data to inform hospital resource management

2021 ◽  
Author(s):  
José Carlos Ferrão ◽  
Mónica Duarte Oliveira ◽  
Daniel Gartner ◽  
Filipe Janela ◽  
Henrique M. G. Martins
Keyword(s):  
Resource Management ◽  
Electronic Health Record ◽  
Health Record ◽  
Hospital Resource ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

scholarly journals HAI-Proactive: Development of an Automated Surveillance System for Healthcare-Associated Infections in Sweden

2020 ◽  
Vol 41 (S1) ◽  
pp. s39-s39
Author(s):  
Pontus Naucler ◽  
Suzanne D. van der Werff ◽  
John Valik ◽  
Logan Ward ◽  
Anders Ternhag ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Electronic Health Record ◽  
Predictive Value ◽  
Surveillance System ◽  
Free Text ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health ◽  
Healthcare Associated

Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections.Funding: Sweden’s Innovation Agency and Stockholm County CouncilDisclosures: None

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