Enhanced Screening and Research Data Collection via Automated EHR Data Capture and Early Identification of Sepsis. Umberger, R., Indranoi, C. “., Simpson, M., Jensen, R., Shamiyeh, J., & Yende, S. SAGE Open Nursing, 5:2377960819850972, January, 2019.
Enhanced Screening and Research Data Collection via Automated EHR Data Capture and Early Identification of Sepsis [link]Paper  doi  abstract   bibtex   
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.
@article{umberger_enhanced_2019,
	title = {Enhanced {Screening} and {Research} {Data} {Collection} via {Automated} {EHR} {Data} {Capture} and {Early} {Identification} of {Sepsis}},
	volume = {5},
	issn = {2377-9608},
	url = {https://doi.org/10.1177/2377960819850972},
	doi = {10/ggckvw},
	abstract = {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.},
	language = {en},
	urldate = {2019-11-08},
	journal = {SAGE Open Nursing},
	author = {Umberger, Reba and Indranoi, Chayawat “Yo” and Simpson, Melanie and Jensen, Rose and Shamiyeh, James and Yende, Sachin},
	month = jan,
	year = {2019},
	pages = {2377960819850972}
}
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