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Authors

  1. Stifter, Janet PhD, RN
  2. Yao, Yingwei PhD
  3. Lopez, Karen Dunn PhD, MPH, RN
  4. Khokhar, Ashfaq PhD
  5. Wilkie, Diana J. PhD, RN, FAAN
  6. Keenan, Gail M. PhD, RN, FAAN

Abstract

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.