Implementing a commercial rule base as a medication order safety net.

A commercial rule base (Cerner Multum) was used to identify medication orders exceeding recommended dosage limits at five hospitals within BJC HealthCare, an integrated health care system. During initial testing, clinical pharmacists determined that there was an excessive number of nuisance and clinically insignificant alerts, with an overall alert rate of 9.2%. A method for customizing the commercial rule base was implemented to increase rule specificity for problematic rules. The system was subsequently deployed at two facilities and achieved alert rates of less than 1%. Pharmacists screened these alerts and contacted ordering physicians in 21% of cases. Physicians made therapeutic changes in response to 38% of alerts presented to them. By applying simple techniques to customize rules, commercial rule bases can be used to rapidly deploy a safety net to screen drug orders for excessive dosages, while preserving the rule architecture for later implementations of more finely tuned clinical decision support.

Development, Implementation and Use of Electronic Surveillance for Ventilator-Associated Events (VAE) in Adults.

Mechanical ventilation provides an important, life-saving therapy for severely ill patients, but ventilated patients are at an increased risk for complications, poor outcomes, and death during hospitalization.1 The timely measurement of negative outcomes is important in order to identify potential issues and to minimize the risk to patients. The Centers for Disease Control and Prevention (CDC) created an algorithm for identifying Ventilator-Associated Events (VAE) in adult patients for reporting to the National Healthcare Safety Network (NHSN). Currently, the primarily manual surveillance tools require a significant amount of time from hospital infection prevention (IP) staff to apply and interpret. This paper describes the implementation of an electronic VAE tool using an internal clinical data repository and an internally developed electronic surveillance system that resulted in a reduction of labor efforts involved in identifying VAE at Barnes Jewish Hospital (BJH).

Using business intelligence to monitor clinical quality metrics.

BJC HealthCare (BJC) uses a number of industry standard indicators to monitor the quality of services provided by each of its hospitals. By establishing an enterprise data warehouse as a central repository of clinical quality information, BJC is able to monitor clinical quality performance in a timely manner and improve clinical outcomes.

Implementing daily dosing rules using a commercial rule base.

We had previously developed and implemented a pharmacy expert system (DoseRanger) that examines drug orders for appropriate single dosage using a commercial rule base and patient specific information. A set of rule adjustments were described and evaluated in order to reduce clinically insignificant alerts. A similar analysis has been performed for daily dose rules provided by the commercial rule base which demonstrated that analogous techniques will be needed.

Customizing a commercial rule base for detecting drug-drug interactions.

We developed and implemented an adverse drug event system (PharmADE) that detects potentially dangerous drug combinations using a commercial rule base. While commercial rule bases can be useful for rapid deployment of a safety net to screen for drug-drug interactions, they sometimes do not provide the desired rule sensitivity. We implemented methods for enhancing commercial drug-drug interaction rules while preserving the original rule base architecture for easy and low cost maintenance.

Strategies for reducing nuisance alerts in a dose checking application.

Commercial rule bases can be implemented to identify medication orders that fall outside recommended dosage ranges, but they are likely to produce an excessive number of nuisance and clinically insignificant alerts. Strategies for customizing commercial dosing rules can be implemented to minimize this problem. This paper describes specific strategies implemented in a dose checking application necessary for achieving a clinically acceptable alert rate.

Monitoring pharmacy expert system performance using statistical process control methodology.

Automated expert systems provide a reliable and effective way to improve patient safety in a hospital environment. Their ability to analyze large amounts of data without fatigue is a decided advantage over clinicians who perform the same tasks. As dependence on expert systems increase and the systems become more complex, it is important to closely monitor their performance. Failure to generate alerts can jeopardize the health and safety of patients, while generating excessive false positive alerts can lead to valid alerts being dismissed as noise. In this study, statistical process control charts were used to monitor an expert system, and the strengths and weaknesses of this technology are presented.

Implementing a commercial rule base as a medication order safety net.

A commercial rule base was used to identify drug orders exceeding standard dosage limits at a university hospital. Initially, there were substantial numbers of clinically insignificant alerts. A method for altering the commercial rule base will be implemented to increase rule specificity for problematic drugs. With minor modifications, commercial rule bases can be used to rapidly create a safety net that screens drug orders for excessive dosages, while preserving the rule architecture for more finely tuned clinical decision support.