Pattern discovery from patient controlled analgesia demand behavior

Unlike previous research on patient controlled analgesia, this study explores patient demand behavior over time. We apply clustering methods to disclose demand patterns among patients over the first 24 h of analgesic medication after surgery. We consider demographic, biomedical, and surgery-related data in statistical analyses to determine predictors for patient demand behavior, and use stepwise regression and Bayes risk analysis to evaluate the influence of demand pattern on analgesic requirements. We identify three demand patterns from 1655 patient controlled analgesia request log files. Statistical tests show correlations of gender (p=.0022), diastolic blood pressure (p=.025), surgery type (p=.0028), and surgical duration (p<.0095) with demand patterns. Stepwise regression and Bayes risk analysis show demand pattern plays the most important role in analgesic consumption prediction (p=0.E+0). This study suggests analgesia request patterns over time exist among patients, and clustering can disclose demand behavioral patterns.