The effect of medication related clinical decision support at the time of physician order entry.

Background In advanced clinical decision support systems, patient characteristics and laboratory values are included in the algorithms that generate alerts. These alerts have a higher specificity than basic medication surveillance alerts. The alerts of advanced clinical decision support systems can be shown directly to the prescriber during order entry, without the risk of generating an overload of irrelevant alerts. We implemented five advanced algorithms that are shown directly to the prescriber. These algorithms are for gastrointestinal prophylaxis, folic or folinic acid prescribed with orally or subcutaneously administered methotrexate, vitamin D prescribed with bisphosphonates, hyponatremia and measuring plasma levels for vancomycin and gentamicin. Objective We evaluated the effect of the implementation of the algorithms. Setting We performed prospective intervention studies with a historical group for comparison in both inpatients and outpatients at a teaching hospital in the Netherlands. Methods We compared the time period after implementation of the algorithm with the time period before implementation, using data from the hospital information system Epic. Difference in guideline adherence were analyzed using Chi square tests. Main outcome measure The outcome measures were the number of alerts, the acceptance rate of the advice in the alert, and for the algorithm measuring plasma levels for vancomycin and gentamicin the time to the correct dose. Results For all algorithms, the implementation resulted in a significant increase in guideline adherence, varying from 11 to 36%. The acceptance rate varied from 14% for hyponatremia to 90% for methotrexate. For gastrointestinal prophylaxis the acceptance rate was 4.4% for basic drug-drug interaction alerts when no gastrointestinal prophylaxis was prescribed and increased to 44.7% after implementation of the advanced algorithm. This algorithm substantially decreased the number of alerts from 812 before implementation to 217 after implementation. After implementation of the algorithm for measuring plasma levels for vancomycin and gentamicin, the proportion of patients receiving the correct dose after 48 h increased from 73 to 84% (p = 0.03). Conclusion Implementation of advanced algorithms that take patient characteristics into account and are shown directly to the physician during order entry, result in an increased guideline adherence.

Physicians' Compliance with a Clinical Decision Support System Alerting during the Prescribing Process.

Clinical decision support systems have been shown to improve practitioner performance. Most systems designed to prevent medication errors generate lists with patients who fulfill the criteria of the algorithm. These lists are reviewed by a pharmacist and physicians are contacted by telephone. Presenting pop-up alerts as part of the workflow with a clear recommendation is a feature critical to success. Therefore we implemented three algorithms in a clinical decision support system alerting during the medication ordering process. We analyzed whether the recommendations in these alerts were followed. We evaluated 1. whether folic or folinic acid was co-prescribed more frequently within 48 h after ordering methotrexate, 2. whether vitamin D or analogues were co-prescribed more frequently within 48 h after ordering bisphophonates and 3. whether sodium lowering drugs were stopped more frequently within one hour in patients with hyponatremia. We analyzed the difference in the 48 days before implementation and the 43 days after implementation, using Pearson's Chi2 test. Co-prescription of folic or folinic acid increased from 54 to 91% (p = 0.014), co-prescription of vitamin D or analogues increased from 11 to 40% (p = 0.001) and the number of stopped orders for sodium lowering drugs increased from 3 to 14% (p = 0.002). This clinical decision support system that alerts physicians for preventable medication errors during the medication ordering process is an effective approach to improve prescribing behavior.