Ellen Harper, DNP, RN-BC, MBA, FAAN 
eharper3@kumc.edu

We will describe how researchers, nursing and information technology teams used large and complex data sets to identify, extract, anonymize patient and nurse information and transfer files for analysis while complying with security and confidentiality requirements. There is a growing need to devise methods to better understand how nursing costs and resources are expended for each patient and how these resources relate to quality and outcomes. Time-stamped data can provide useful information and several means to identify both the clinical trajectory as well as the sequence of nursing care and nurses’ engagement during a patient during hospitalization. Nurses are linked to individual patients in several ways e.g., the electronic capture of the nurse-patient assignment and data from barcoding technologies used for medication administration. Automatic capture of nurse-generated clinical observations and nursing interventions data for secondary use can provide new and unique opportunities to measure nursing care in several different dimensions. 

Part of a panel presentation Nursing Knowledge Big Data Science: A National Collaborative to Achieve Sharable and Comparable Nurse-Sensitive Data. 

August 28, 2019, Lyon France

Type
erepository
MedInfo2019
Nursing Value
Data-driven research and discovery
Data Integration
Data mining and knowledge discovery
Data standards
Electronic health record
Machine learning and predictive modeling
Measuring outcomes
Precision health/ medicine
Secondary use of EHR data