This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs.
The study suggests that pregnancy and lactation labeling in PI for drugs marketed by the same pharmaceutical companies depend on the country of marketing; this raises concern about the reliability of PI documents as a useful source of information for appropriate prescribing during pregnancy and lactation. Discrepancies in this information can potentially lead to inappropriate prescribing in pregnant and breastfeeding women, who may expose their fetuses and breastfed children to unnecessary risks. At the same time, unjustified warnings against breastfeeding may result in children being unnecessarily weaned from being breastfed.
Objective: The purpose of this study was to investigate therapy switching from methylphenidate (MPH) to atomoxetine (ATX) in a clinical sample of Danish children and adolescents with attention-deficit/hyperactivity disorder (ADHD); specifically, to determine the duration of MPH treatment before switching to ATX, and the reasons leading to a switch in therapy.
Objective:Through manual review of clinical notes for patients with type 2 diabetes mellitus attending a Danish diabetes center, the aim of the study was to identify adverse drug reactions (ADRs) associated with three classes of glucose-lowering medicines: “Combinations of oral blood-glucose lowering medicines” (A10BD), “dipeptidyl peptidase-4 (DDP-4) inhibitors” (A10BH), and “other blood glucose lowering medicines” (A10BX). Specifically, we aimed to describe the potential of clinical notes to identify new ADRs and to evaluate if sufficient information can be obtained for causality assessment.Methods:For observed adverse events (AEs) we extracted time to onset, outcome, and suspected medicine(s). AEs were assessed according to World Health Organization-Uppsala Monitoring Centre causality criteria and analyzed with respect to suspected medicines, type of ADR (system organ class), seriousness and labeling status.Findings:A total of 207 patients were included in the study leading to the identification of 163 AEs. 14% were categorized as certain, 60% as probable/likely, and 26% as possible. 15 (9%) ADRs were unlabeled of which two were serious: peripheral edema associated with sitagliptin and stomach ulcer associated with liraglutide. Of the unlabeled ADRs, 13 (87%) were associated with “other blood glucose lowering medications,” the remaining 2 (13%) with “DDP-4 inhibitors.”Conclusion:Clinical notes could potentially reveal unlabeled ADRs associated with prescribed medicines and sufficient information is generally available for causality assessment. However, manual review of clinical notes is too time-consuming for routine use and hence there is a need for developing information technology (IT) tools for automatic screening of patient records with the purpose to detect information about potentially serious and unlabeled ADRs.
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