Comparing the predictive value of screening to the use of electronic health record data for detecting future suicidal thoughts and behavior in an urban pediatric emergency department: A preliminary analysis

2021 ◽  
Author(s):  
Emily E. Haroz ◽  
Christopher Kitchen ◽  
Paul S. Nestadt ◽  
Holly C. Wilcox ◽  
Jordan E. DeVylder ◽  
...  
Keyword(s):  
Emergency Department ◽  
Electronic Health Record ◽  
Predictive Value ◽  
Preliminary Analysis ◽  
Pediatric Emergency ◽  
Pediatric Emergency Department ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
And Behavior

scholarly journals Automating a Manual Sepsis Screening Tool in a Pediatric Emergency Department

2018 ◽  
Vol 09 (04) ◽  
pp. 803-808 ◽  
Author(s):  
Julia Lloyd ◽  
Erin Ahrens ◽  
Donnie Clark ◽  
Terri Dachenhaus ◽  
Kathryn Nuss
Keyword(s):  
Emergency Department ◽  
Electronic Health Record ◽  
Screening Tool ◽  
Pediatric Emergency ◽  
Pediatric Emergency Department ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Electronic Tool ◽  
Electronic Health ◽  
Sepsis Screening

Objective This article describes the method of integrating a manual pediatric emergency department sepsis screening process into the electronic health record that leverages existing clinical documentation and keeps providers in their current, routine clinical workflows. Methods Criteria in the manual pediatric emergency department sepsis screening tool were mapped to standard documentation routinely entered in the electronic health record. Data elements were extracted and scored from the medical history, medication record, vital signs, and physical assessments. Scores that met a predefined sepsis risk threshold triggered interruptive system alerts which notified emergency department staff to perform sepsis huddles and consider appropriate interventions. Statistical comparison of the new electronic tool to the manual process was completed by a two-tail paired t-test. Results The performance of the pediatric electronic sepsis screening tool was evaluated by comparing flowsheet row documentation of the manual, sepsis alert process against the interruptive system alert instance of the electronic sepsis screening tool. In an 8-week testing period, the automated pediatric electronic sepsis screening tool identified 100% of patients flagged by the manual process (n = 29), on average, 68 minutes earlier. Conclusion Integrating a manual sepsis screening tool into the electronic health record automated identification of pediatric sepsis screening in a busy emergency department. The electronic sepsis screening tool is as accurate as a manual process and would alert bedside clinicians significantly earlier in the emergency department course. Deployment of this electronic tool has the capability to improve timely sepsis detection and management of patients at risk for sepsis without requiring additional documentation by providers.

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

Modeling patient-related workload in the emergency department using electronic health record data

2021 ◽  
Vol 150 ◽  
pp. 104451
Author(s):  
Xiaomei Wang ◽  
H. Joseph Blumenthal ◽  
Daniel Hoffman ◽  
Natalie Benda ◽  
Tracy Kim ◽  
...  
Keyword(s):  
Emergency Department ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

scholarly journals Patient-related Workload Prediction in the Emergency Department: A Big Data Approach

2019 ◽  
Vol 8 (1) ◽  
pp. 33-36
Author(s):  
Xiaomei Wang ◽  
H. Joseph Blumenthal ◽  
Daniel Hoffman ◽  
Natalie Benda ◽  
Tracy Kim ◽  
...  
Keyword(s):  
Emergency Department ◽  
Emergency Medicine ◽  
Big Data ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Workload Prediction ◽  
Multi Stage ◽  
Record Data ◽  
Electronic Health

This research is a first stage in developing a method for modeling the clinician workload associated with an emergency medicine patient in order to display workload for purposes of managing clinician workload and emergency department (ED) flow. We proposed a multi-stage approach of predicting patient-related drivers of clinician’s workload in the emergency department. We trained the model from one month of electronic health record data (EHR) records of an ED. The model predicts the amount of work that individual patients contribute to the workload of clinicians. It can potentially help to manage clinician workload by supporting the decision of assigning new patients.

scholarly journals Time to Recognition of Sepsis in the Emergency Department Using Electronic Health Record Data

2020 ◽  
Vol 48 (2) ◽  
pp. 200-209 ◽  
Author(s):  
Priya A. Prasad ◽  
Margaret C. Fang ◽  
Yumiko Abe-Jones ◽  
Carolyn S. Calfee ◽  
Michael A. Matthay ◽  
...  
Keyword(s):  
Emergency Department ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health
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