Application of Information Technology: Computerized Surveillance for Adverse Drug Events in a Pediatric Hospital.

Department of Pediatrics, Washington University School of Medicine, St Louis, MO; Department of Internal Medicine, Washington University School of Medicine, St Louis, MO; BJC Healthcare, Center for Healthcare Quality and Effectiveness, St Louis, MO; St Louis Children's Hospital, St Louis, MO
JAMIA 01/2009; 16:607-612. DOI: 10.1197/jamia.M3167
Source: DBLP

ABSTRACT 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.

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    ABSTRACT: PURPOSE: To test if two of the adverse event triggers proposed by the Institute of Healthcare Improvement can detect adverse drug events (ADEs) in a UK secondary care setting, using an electronic prescribing and health record system. METHODS: In order to identify triggers for over-anticoagulation and potential opioid overdose and we undertook a retrospective review of electronic medical and prescription records from 54,244 hospital admissions over a 1-year period, alongside a review of medical incident reports. Once prescription data were linked to triggers and duplicates were removed, case note review eliminated the false positive ADEs. Additionally, we tested the use of an electronic algorithm for the International Normalized Ratio (INR) ≥6 trigger. RESULTS: The INR ≥6 electronic trigger identified 46 potential ADEs and the naloxone electronic trigger identified 82 ADEs. Based on the available case note review, the INR ≥6 trigger had a positive predictive value (PPV) of 38 % (14/37) and the naloxone trigger had a PPV of 91 % (61/67). The electronic algorithm for the INR ≥6 trigger identified 12 ADEs, thus reducing the need of case note review. This was in comparison with one and two critical incidents reported in the trust medical incident reports system, which respectively related to over-coagulation with warfarin and over-sedation with opioid medication. CONCLUSIONS: We have integrated automated and manual methods of detecting ADEs using previously defined triggers. Incorporating electronic triggers in already established electronic health records with prescription and laboratory test data can improve the detection of ADEs, and potentially lead to methods to avert them.
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