Evaluation of rule effectiveness and positive predictive value of clinical rules in a Dutch clinical decision support system in daily hospital pharmacy practice

摘要: Introduction: Our advanced clinical decision support (CDS) system, entitled 'adverse drug event alerting system' (ADEAS), is in daily use our hospital pharmacy. It used by pharmacists to select patients at risk of possible adverse events (ADEs). The system retrieves data from several information systems, and uses rules the ADEs. are all medication related formulated using seven categories. Objective: This studies objectives 1) evaluate CDS ADEAS pharmacy practice, 2) assess rule effectiveness positive predictive value (PPV) incorporated system. Setting: Leiden University Medical Center, Netherlands. All admitted on six different internal medicine cardiology wards were included. Measures: Outcome measures total number alerts, with alerts outcome these alerts: whether pharmacist gave advice prevent a ADE or not. Both overall PPV per category scored. Study design: During 5 month study period safety generated means ADEAS. evaluated if necessary, healthcare professionals subsequently contacted was given order Results: 2650 931 patients. In 270 (10%) physician nurse 204 (76%) cases this led an ADE. remaining 2380 (90%) scored as non-relevant. Most linking laboratory (1685 alerts). 0.10 0.08. Combination highest for based upon ''basic computerized entry (CPOE) fine-tuned high patients'' (rule efficiency=0.17; PPV=0.14). Conclusion: can effectively be practice potential ADEs, but increase benefits routine patient care efficiency, both should improved. Furthermore, would have refined restricted those categories that potentially most promising relevance, i.e. ''clinical combination data'' basic CPOE patients''.