Evidence appraisal of Hu Z, Simon GJ, Arsoniadis EG, Wang Y, Kwaan MR, Melton GB. Automated detection of postoperative surgical site infections using supervised methods with electronic health record data. Stud Health Technol Inform. 2015;216:706-710

AORN Journal ◽  
2018 ◽  
Vol 107 (6) ◽  
pp. 776-781
Keyword(s):  
Electronic Health Record ◽  
Surgical Site Infections ◽  
Automated Detection ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Supervised Methods ◽  
Electronic Health

scholarly journals Automated Detection and Classification of Type 1 Versus Type 2 Diabetes Using Electronic Health Record Data

Diabetes Care ◽  
2012 ◽  
Vol 36 (4) ◽  
pp. 914-921 ◽  
Author(s):  
M. Klompas ◽  
E. Eggleston ◽  
J. McVetta ◽  
R. Lazarus ◽  
L. Li ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Electronic Health Record ◽  
Automated Detection ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

Identification of surgical site infections using electronic health record data

2018 ◽  
Vol 46 (11) ◽  
pp. 1230-1235 ◽  
Author(s):  
Kathryn L. Colborn ◽  
Michael Bronsert ◽  
Elise Amioka ◽  
Karl Hammermeister ◽  
William G. Henderson ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Surgical Site Infections ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

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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