Computerized surveillance for adverse drug events in a pediatric hospital.

There are limited data on adverse drug event rates in pediatrics. The authors describe the implementation and evaluation of an automated surveillance system modified to detect adverse drug events (ADEs) in pediatric patients. The authors constructed an automated surveillance system to screen admissions to a large pediatric hospital. Potential ADEs identified by the system were reviewed by medication safety pharmacists and a physician and scored for causality and severity. Over the 6 month study period, 6,889 study children were admitted to the hospital for a total of 40,250 patient-days. The ADE surveillance system generated 1226 alerts, which yielded 160 true ADEs. This represents a rate of 2.3 ADEs per 100 admissions or 4 per 1,000 patient-days. Medications most frequently implicated were diuretics, antibiotics, immunosuppressants, narcotics, and anticonvulsants. The composite positive predictive value of the ADE surveillance system was 13%. Automated surveillance can be an effective method for detecting ADEs in hospitalized children.

Computerized surveillance for adverse drug events in a pediatric hospital.

Adverse drug event (ADE) surveillance is needed to inform processes and methods for prevention. Voluntary reporting and manual chart review have limitations. Automated surveillance systems are gaining recognition and provide useful information to supplement the other methods. Preliminary evaluation of a pediatric automated adverse drug event application shows a positive predictive value of 13%, discovering events with harm in 1.3% of inpatient admissions.

Improving adherence to dyslipidemia medication guidelines in hospitalized diabetic patients using a technology-assisted pharmacist intervention.

We tested whether a technology-assisted pharmacist intervention improved physician adherence to guidelines for lipid-lowering therapy in diabetic patients. Computerized alerts identified diabetic patients above LDL-Cholesterol (LDL-C) goal. During Period 1 prescribing behavior was observed in both control and intervention physician groups without intervening. In Period 2, pharmacists conducted academic detailing with intervention group physicians. Control group physicians were observed. The intervention significantly improved the proportion of diabetic patients discharged on statin therapy.

An intervention to improve secondary prevention of coronary heart disease.

BACKGROUND: Translating guidelines into clinical practice has proved to be quite difficult, even when the guidelines are well accepted and noncontroversial. Both computerized reminders and academic detailing have been effective in changing physician prescribing behavior. In this study, we sought to use these methods, mediated by clinical pharmacists, to improve adherence to the secondary prevention guidelines in hospitalized patients with myocardial infarction. METHODS: A randomized, prospective study was performed in which computerized alerts identifying hospitalized patients with elevated troponin I levels were routed to clinical pharmacists. The pharmacists then conducted academic detailing for physicians caring for patients with acute myocardial infarction who were randomized to the intervention group. Patients in the control group received standard care. The main outcome measure was the proportion of patients discharged on a regimen of aspirin, beta-blockers, angiotensin-converting enzyme inhibitors, and statins. RESULTS: The intervention had a significant impact on the proportion of patients discharged on a regimen of angiotensin-converting enzyme inhibitors (328/365 [89.9%] vs 409/488 [83.8%], intervention vs control, respectively, P = .02), and statins (344/365 [94.2%] vs 436/488 [89.3%], P = .02). There was no statistical impact on beta-blocker (350/365 [95.9%] vs 448/488 [91.8%], P = .10) or aspirin use (352/365 [96.4%] vs 471/488 [96.5%], P = .87). When all 4 classes were considered together, 305 (83.6%) of 365 patients vs 343 (70.3%) of 488 patients were discharged on a regimen of all secondary prevention medications to which they did not have a contraindication (P<.001). CONCLUSION: A computerized alert with pharmacist-mediated academic detailing is an effective means to increase adherence to secondary prevention guidelines for coronary heart disease.

Migrating toward a next-generation clinical decision support application: the BJC HealthCare experience.

The next-generation model outlined in the AMIA Roadmap for National Action on Clinical Decision Support (CDS) is aimed to optimize the effectiveness of CDS interventions, and to achieve widespread adoption. BJC HealthCare re-engineered its existing CDS system in alignment with the AMIA roadmap and plans to use it for guidance on further enhancements. We present our experience and discuss an incremental approach to migrate towards the next generation of CDS applications from the viewpoint of a healthcare institution. Specifically, a CDS rule engine service with a standards-based rule representation format was built to simplify maintenance and deployment. Rules were separated from execution code and made customizable for multi-facility deployment. Those changes resulted in system improvement in the short term while aligning with long-term strategic objectives.

Alerts to improve chart documentation for National Quality Measures.

Clinical decision support (CDS) rules monitoring adherence to guidelines for secondary prevention of acute myocardial infarction (AMI) have been in use at BJC HealthCare's academic facility for five years. The alert web response form for these rules was enhanced to facilitate documentation of contraindications for ACE/ARB, beta blocker, aspirin, and lipid-lowering medications. An analysis of the impact of these enhancements and the changes to pharmacy workflow are presented here.

Automated dose checking and intervention for bariatric patients.

According to a recent Center for Disease Control survey, 33% of the US population is obese. Because labeled dosing guidelines are based upon non-obese individuals, under dosing of antibiotics may be problematic in this population. We developed an automated dose checking tool to efficiently detect potentially inappropriate dosing of antibiotics in bariatric (morbidly obese patients).

Long-term effectiveness of an automated guideline adherence monitor for secondary prevention of acute myocardial infarction.

An automated guideline monitor for secondary prevention of acute myocardial infarction (AMI) has been favorably tested at an academic medical center using a randomized controlled trial. Subsequently, the monitor was implemented in a production mode and has been in production use for five years. Statistical process control analysis shows a generally sustainable effect of the intervention.

Physicians' knowledge and attitudes about coronary heart disease secondary prevention.

We administered a knowledge and attitudes questionnaire regarding a technology assisted pharmacist mediated academic detailing intervention designed to improve physician adherence to coronary heart disease (CHD) secondary-prevention guidelines. Questionnaires were administered in two settings: an academic hospital and a community hospital. More knowledgeable physicians reported being more likely to prescribe a pharmacists' recommended medication and to agree that lipid profiles should be automatically performed for inpatients with elevated troponin.

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.

Automated clinical data collection for national quality measurement reporting.

Collecting data for the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) ORYX Core Measurement Reporting can be automated using an object-oriented, client-developed program that extracts data from a clinical data repository and utilizes an MHA vendor upload process. The process eliminated 39% of the manual data collection efforts.

Implementing GermWatcher, an enterprise infection control application.

Automated surveillance tools can provide significant advantages to infection control practitioners. When stored in a relational database, the data collected can also be used to support numerous research and quality improvement opportunities. A previously described electronic infection control surveillance system was remodeled to provide multi-hospital support, an XML based rule set, and interoperability with an enterprise terminology server. This paper describes the new architecture being used at hospitals across BJC HealthCare.

Improving adherence to coronary heart disease secondary prevention medication guidelines at a community hospital.

Using a two-period group randomized study, we tested whether a technology assisted pharmacist intervention improved physician adherence to coronary heart disease (CHD) secondary prevention medication guidelines. After an observation period, physician practices were randomized to intervention or control arms. In the intervention arm, alerts prompted a pharmacist to communicate with the responsible physician about secondary prevention medications. The intervention significantly improved the proportion of patients discharged on appropriate secondary prevention medications.

Using BPEL to define an executable CDS rule process.

Business Process Execution Language for Web Services (BPEL) is an XML-based language used to define business process and workflow logic. While its original purpose was to provide a method of linking several disparate business applications, we have found that BPEL provides several features and advantages that lend it to incorporation in a clinical decision support (CDS) rule engine.

Implementation of virtual medical record object model for a standards-based clinical decision support rule engine.

The Virtual Medical Record (vMR) is a structured data model for representing individual patient informations. Our implementation of vMR is based on HL7 Reference Information Model (RIM) v2.13 from which a minimum set of objects and attributes are selected to meet the requirement of a clinical decision support (CDS) rule engine. Our success of mapping local patient data to the vMR model and building a vMR adaptor middle layer demonstrate the feasibility and advantages of implementing a vMR in a portable CDS solution.

Prospective validation of an algorithm to identify hospitalized diabetic patients using administrative data.

To facilitate healthcare quality improvement initiatives, we previously developed an algorithm to identify diabetes mellitus (DM) patients using only electronically available administrative data. In this study, we have validated our prediction model, screening over 28,000 admissions and determining factors associated with false positive assignment. These factors will be incorporated into a revised algorithm.

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.

Identification of patients with coronary heart disease: meeting regulatory standards vs. improving overall care.

Acute myocardial infarction (AMI) patients can be identified prospectively by troponin-I (TnI) result monitoring and retrospectively by ICD-9 diagnosis coding. Prospective identification is needed for interventions, while retrospective identification is required for regulatory reporting. Prospective approaches can identify patients with a reasonable degree of accuracy, but they cannot always predict ICD-9 coding for that condition. Our approach focuses on prospective identification of patients with CHD to improve their care. Meeting regulatory standards is a secondary goal.

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.

Automatic detection of spironolactone - related adverse drug events.

Due to increasing reports of spironolactone associated life-threatening hyperkalemia, we implemented a rule in our automated event detection system to monitor serum potassium results in patients receiving spironolactone. In 2004, 419 (10.49%) of 3995 admissions at 3 BJC HealthCare hospitals were identified as having hyperkalemia while on spironolactone. For a 9-month period in one facility, 33 of 52 automatically detected potential ADEs had been validated by pharmacists through manual chart review to have spironolactone as a contributing factor (PPV=63.5%).