Article

A systematic review to evaluate the accuracy of electronic adverse drug event detection

Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
Journal of the American Medical Informatics Association (Impact Factor: 3.5). 01/2012; 19(1):31-8. DOI: 10.1136/amiajnl-2011-000454
Source: PubMed

ABSTRACT

Adverse drug events (ADEs), defined as adverse patient outcomes caused by medications, are common and difficult to detect. Electronic detection of ADEs is a promising method to identify ADEs. We performed this systematic review to characterize established electronic detection systems and their accuracy.
We identified studies evaluating electronic ADE detection from the MEDLINE and EMBASE databases. We included studies if they contained original data and involved detection of electronic triggers using information systems. We abstracted data regarding rule characteristics including type, accuracy, and rationale.
Forty-eight studies met our inclusion criteria. Twenty-four (50%) studies reported rule accuracy but only 9 (18.8%) utilized a proper gold standard (chart review in all patients). Rule accuracy was variable and often poor (range of sensitivity: 40%-94%; specificity: 1.4%-89.8%; positive predictive value: 0.9%-64%). 5 (10.4%) studies derived or used detection rules that were defined by clinical need or the underlying ADE prevalence. Detection rules in 8 (16.7%) studies detected specific types of ADEs.
Several factors led to inaccurate ADE detection algorithms, including immature underlying information systems, non-standard event definitions, and variable methods for detection rule validation. Few ADE detection algorithms considered clinical priorities. To enhance the utility of electronic detection systems, there is a need to systematically address these factors.

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    • "A challenge for pharmacovigilance is that ADEs are heav­ ily underreported [4], both in spontaneous reporting systems -wherein ADE case reports submitted voluntarily by patients and clinicians are collected -and in EHRs, wherein ADEs can be encoded by a limited set of diagnosis codes. To address the underreporting problem, alerting systems that can automati­ cally detect ADEs in EHRs are potentially very valuable, and much research has been conducted to that end. "
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    ABSTRACT: Adverse drug events (ADEs) are heavily underreported in electronic health records (EHRs). Alerting systems that are able to detect potential ADEs on the basis of patient-specific EHR data would help to mitigate this problem. To that end, the use of machine learning has proven to be both efficient and effective; however, challenges remain in representing the heterogeneous EHR data, which moreover tends to be high- dimensional and exceedingly sparse, in a manner conducive to learning high-performing predictive models. Prior work has shown that distributional semantics – that is, natural language processing methods that, traditionally, model the meaning of words in semantic (vector) space on the basis of co-occurrence information – can be exploited to create effective representations of sequential EHR data of various kinds. When modeling data in semantic space, an important design decision concerns the size of the context window around an object of interest, which governs the scope of co-occurrence information that is taken into account and affects the composition of the resulting semantic space. Here, we report on experiments conducted on 27 clinical datasets, demonstrating that performance can be significantly improved by modeling EHR data in ensembles of semantic spaces, consisting of multiple semantic spaces built with different context window sizes. A follow-up investigation is conducted to study the impact on predictive performance as increasingly more semantic spaces are included in the ensemble, demonstrating that accuracy tends to improve with the number of semantic spaces, albeit not monotonically so. Finally, a number of different strategies for combining the semantic spaces are explored, demonstrating the advantage of early (feature) fusion over late (classifier) fusion. Semantic space ensembles allow multiple views of (sparse) data to be captured (densely) and thereby enable improved performance to be obtained on the task of detecting ADEs in EHRs.
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    • "They included "studies if they contained original data and involved detection of electronic triggers using information systems"[15]. "They abstracted data regarding rule characteristics including type, accuracy, and rational "[15]. Honigman et al. also developed a program that combines four computer search methods, including text searching of the electronic medical record, to detect ADEs in outpatient settings[16]. "
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