Secondary use of clinical text data are gaining much attention in improving the quality and the efficiency of medical treatment. Although there is some case studies of medical-examination text data, there are not many examples fed back to the medical-examination spot. The present paper analyses the operation records of total hip arthroplasty. We extracted feature words that characterize the two peaks which appeared in distribution of postoperative hospital days using SVM (support vector machine) and FS (feature selection). The models gained by optimal FS attained 60% accuracy as prediction performance. We applied logistic regression analysis to estimate postoperative length of stay from the extracted feature words. Most words were not statistically significant except two words.