Fuzzy logic algorithm for quantifying clinical effectiveness of genitourinary patient-controlled analgesia services

The self-regulated patient-controlled analgesia (PCA) has been widely used in postoperative pain relief. The patient is allowed to self-administer doses of analgesic through a simple electric-mechanical pumping control mechanism. PCA has become an acceptable and safe method for relieving postoperative pain. Since the pain is a personal, subjective experience. It is difficult to quantify and evaluate the clinical effectiveness of PCA therapy. In this work, a novel clinical effectiveness index based on fuzzy logic model is proposed to evaluate pain management quality of PCA services. In order to realize the pain relief profile during the PCA therapy, the analgesic demand and delivery events with corresponding timestamps are extracted from the PCA logs. The time series of D/D ratio w pre-processed through the aggregation technique of sliding window. A four-hour and twelve-hour windows are adopted to calculate the D/D ratio and the window slides every one hour. Patients who adopted genitourinary surgery and patient-controlled analgesia therapy were selected in this study. To obtain a reasonable PCA clinical effectiveness index, we design a fuzzy logic algorithm for quantifying clinical effectiveness. The establishment of fuzzy control rules was cooperated with the anesthesiologists. The results have demonstrated that the generated PCA clinical effectiveness index gives a reliable and suitable estimation of the PCA service. It fulfills the requirement of therapy evaluation.