Data mining nursing care plans of end-of-life patients: a study to improve healthcare decision making

Int J Nurs Knowl. 2013 Feb;24(1):15-24. doi: 10.1111/j.2047-3095.2012.01217.x. Epub 2012 Aug 17.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Automation
  • Decision Making, Organizational*
  • Information Storage and Retrieval*
  • Nursing Care*
  • Patient Care Planning*
  • Terminal Care*