Stifter Janet, Yao Yingwei, Lopez Karen Dunn, Khokhar Ashfaq, Wilkie Diana J, Keenan Gail M
College of Nursing, University of Illinois at Chicago (Drs Stifter and Lopez); College of Nursing, University of Florida, Gainesville (Drs Yao, Wilkie, and Keenan); and Illinois Institute of Technology, Chicago, Illinois (Dr Khokhar).
ANS Adv Nurs Sci. 2015 Jul-Sep;38(3):241-51. doi: 10.1097/ANS.0000000000000081.
The influence of the staffing variable relational nurse continuity on patient outcomes has been rarely studied and with inconclusive results. Multiple definitions and an absence of systematic methods for measuring the influence of continuity have resulted in its exclusion from nurse-staffing studies and conceptual models. We present a new conceptual model and an innovative use of health information technology to measure relational nurse continuity and to demonstrate the potential for bringing the results of big data science back to the bedside. Understanding the power of big data to address critical clinical issues may foster a new direction for nursing administration theory development.