2021 Volume 16 Issue 1 Pages 3-12
“Nursing necessity” was developed by Tsutsui et al. as an evaluation criterion aimed at reflecting the volume and quality of nursing services in patient-to-nurse ratios. In the 2008 revision of the medical payment system, the evaluation of nursing necessity became obligatory owing to the seven to one patient-to-nurse ratio requirement. However, challenges include the high burden of education and input duties in the evaluation of nursing necessity; moreover, although it is possible to calculate past nursing necessity, future nursing necessity cannot be calculated and used as data that justify the deployment of nurses. By the way, the volume of nursing may be regulated with reference to the order information in electronic medical records by each patient’s condition. Therefore, if nursing necessity could be automatically predicted based on this order, it would be possible to reduce the burden of education and input duties in the evaluation of nursing necessity, and the appropriate deployment of nurses would be possible. Thus, in this study, we attempted to verify the prediction of nursing necessity items based on order data using supervised machine learning. Prediction results indicated the accuracy rate of the nursing necessity items was 0.90 or more for almost all items, and the f value varied from 0-0.85. Furthermore, when predicting the deployment of nurses, the accuracy rate was 0.90 or more for all and number of nurses required is almost same between by nursing necessity and prediction of this method. Therefore, a highly accurate prediction was possible.
