Data Mining Nursing Care Plans of End‐of‐Life Patients: A Study to Improve Healthcare Decision Making |
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| Authors: | Fadi Almasalha PhD Dianhui Xu PhD Gail M. Keenan RN PhD Ashfaq Khokhar PhD Yingwei Yao PhD Yu‐C. Chen PhD Andy Johnson PhD R. Ansari PhD Diana J. Wilkie RN PhD |
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| Affiliation: | Fadi Almasalha, PhD, is an Assistant Professor at The Applied Science Private University, Amman, Jordan, Dianhui Xu, PhD, is a System Software Engineer at Litepoint Company, Sunnyvale, California, Yu‐C. Chen, PhD, is a Software Engineer for Pixar Animations Studios, Emerville, California, Gail M. Keenan, RN, PhD, is an Associate Professor in the College of Nursing*, Ashfaq Khokhar, PhD, is a Professor in the Department of Electrical and Computer Engineering*, Yinwei Yao, PhD, is an Associate Clinical Professor in the College of Nursing*, Andy Johnson, PhD, is an Associate Professor in the Department of Computer Science*, Rashid Ansari, PhD, is a Professor in the Department of Engineering*, and Diana J. Wilkie, RN, PhD, is a Professor in the College of Nursing, University of Illinois at Chicago in Chicago, Illinois. |
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| 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. |
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| Keywords: | Data mining electronic health record end‐of‐life hospital care pain plan of care |
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