Inpatient adverse drug events (ADEs) continue to be a major source of morbidity and mortality despite advances in computerized drug safety measures. Reports on the ability of computerized ADE alerts to prevent and mitigate ADEs are lacking.
The aims of this project are to:
1. Assess organizational, social, and cognitive factors that affect adoption of a real-time ADE alerting technology.
2. Analyze the effect of the ADE alerting technology on management and rate of ADEs.
3. Estimate the cost-benefit of the ADE alerting technology.
This is a study, in two parts, of computerized real-time ADE alerts intended for primary and secondary prevention of ADEs. At Salt Lake City, we are conducting a pseudo-randomized controlled trial in which 50% of the patients' records will be randomized to be eligible to produce ADE alerts.
Theoretical perspectives at the organizational, social, and cognitive levels frame the design and analysis; they include the IT Implementation Framework, the Theory of Planned Behavior, and signal detection theory. Difference in rates of usage, ADEs, and recognition of ADEs will be modeled using multi-level Poisson regression. Differences in time to intervention in events will be analyzed using multi-level accelerated failure parametric survival model with shared frailty. The cost-benefit of the ADE alerts will be modeled with multivariate linear regression. Data from the electronic medical record, chart review, surveys, work logs, and case studies inform the analysis.
From September 2009 to September 2010, there were 573 potential adverse drug events with a trigger condition identified, 291 in the control and 282 in the intervention group. These triggers alerted potential adverse drug events for 394 unique patients.
The ADE alerts promise to reduce mortality, morbidity, and costs due to ADEs.
Remaining analyses will quantify the effect of the alerts in the hands of VA medical residents (including, but not limited to, first-year residents); explore the associations of organizational and socio-cognitive barriers and facilitators with the adoption of the ADE alert technology; and explore whether ADE alerts change user bias in diagnosing ADEs or whether the alerts heighten sensitivity to drug problems.
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Adverse Event Monitoring, Adverse events, Decision Support, Informatics, Quality assurance, improvement, Safety