Author Disclosure Statement: Rights to HANDS® are owned by HealthTeam IQ, LLC, a company owned and managed by Gail Keenan. She has a conflict management plan with the University of Illinois to assure scientific integrity of research related to HANDS.
Data Mining Nursing Care Plans of End-of-Life Patients: A Study to Improve Healthcare Decision Making
Article first published online: 17 AUG 2012
© 2012, The Authors International Journal of Nursing Knowledge © 2012, NANDA International
International Journal of Nursing Knowledge
Volume 24, Issue 1, pages 15–24, February 2013
How to Cite
Almasalha, F., Xu, D., Keenan, G. M., Khokhar, A., Yao, Y., Chen, Y.-C., Johnson, A., Ansari, R. and Wilkie, D. J. (2013), Data Mining Nursing Care Plans of End-of-Life Patients: A Study to Improve Healthcare Decision Making. Int Jnl Nurs Knowledge, 24: 15–24. doi: 10.1111/j.2047-3095.2012.01217.x
Authors who are also affiliated to University of Illinois at Chicago in Chicago, Illinois.
- Issue published online: 17 FEB 2013
- Article first published online: 17 AUG 2012
- Data mining;
- electronic health record;
- end-of-life hospital care;
- plan of care
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.