mercoledì 27 marzo 2013


Data Mining Nursing Care Plans of End-of-Life Patients: A Study to ImproveHealthcare Decision Making

  1. Fadi Almasalha PhD, 
  2. Dianhui Xu PhD,
  3. Gail M. Keenan RN, PhD*
  4. Ashfaq Khokhar PhD, 
  5. Yingwei Yao PhD, 
  6. Yu-C. Chen PhD,
  7. Andy Johnson PhD, 
  8. R. Ansari PhD, 
  9. Diana J. Wilkie RN, PhD
Article first published online: 17 AUG 2012
DOI: 10.1111/j.2047-3095.2012.01217.x
International Journal of Nursing Knowledge

International Journal of Nursing Knowledge

Volume 24Issue 1pages 15–24February 2013













Abstract

PURPOSE:  To reveal hidden patterns and knowledge present in nursing care information documented with standardized nursing terminologies on end-of-life (EOL) hospitalized patients.
METHOD:  596 episodes of care that included pain as a problem on a patient's care plan were examined using statistical and data mining tools. The data were extracted from the Hands-On Automated Nursing Data System database of nursing care plan episodes (n = 40,747) coded with NANDA-I, Nursing Outcomes Classification, and Nursing Intervention Classification (NNN) terminologies. System episode data (episode = care plans updated at every hand-off on a patient while staying on a hospital unit) had been previously gathered in eight units located in four different healthcare facilities (total episodes = 40,747; EOL episodes = 1,425) over 2 years and anonymized prior to this analyses.
RESULTS:  Results show multiple discoveries, including EOL patients with hospital stays (<72 hr) are less likely (p < .005) to meet the pain relief goals compared with EOL patients with longer hospital stays.
CONCLUSIONS:  The study demonstrates some major benefits of systematically integrating NNN into electronic health records.

LINK TO onlinelibrary.wiley.com/full

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