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Big data analysis, Recovery after surgery, Time-series analysis



  1. Noguchi, Hiroshi PhD
  2. Miyahara, Maki HMS, RN
  3. Takahashi, Toshiaki PhD, RN
  4. Sanada, Hiromi PhD, PhD, RN, WOCN, FAAN
  5. Mori, Taketoshi PhD


Nurse call data may be used to evaluate the quality of nursing. However, traditional frequency-based statistics may not easily apply to nurse calls due to the large individual variability and daily call changes. We intended to propose a probabilistic modeling of nurse calls based on Bayesian statistics. We constructed the model including nurse call daily changes, individual variability, and adjustment according to characteristics (age and sex). Nurse call differences after surgery were analyzed based on data from the orthopedic ward from April 2014 to October 2017. Results show that there were differences in nurse calls from day 1 to day 10 after surgery between patients who had undergone orthopedic surgery and those who had undergone other surgeries such as tumor surgery. Furthermore, there were differences in nurse calls from day 1 to day 8 after surgery between patients who used extra pain relief medicine and those who did not. Although the analysis required multiple comparisons regarding daily nurse call changes and fixed data samples per day, our approach using Bayesian statistics could detect the periods and significant differences. This indicates that our nurse call modeling based on Bayesian statistics may be used to analyze nurse call changes.