The safety of medication use has been a priority in the United States since the late 1930s. Recently, it has gained prominence due to the increasing amount of data suggesting that a large amount of patient harm is preventable and can be mitigated with effective risk strategies that have not been sufficiently adopted. Adverse events from medications are part of clinical practice, but the ability to identify a patient's risk and to minimize that risk must be a priority. The ability to identify adverse events has been a challenge due to limitations of available data sources, which are often free text. The use of natural language processing (NLP) may help to address these limitations. NLP is the artificial intelligence domain of computer science that uses computers to manipulate unstructured data (i.e., narrative text or speech data) in the context of a specific task. In this narrative review, we illustrate the fundamentals of NLP and discuss NLP's application to medication safety in four data sources: electronic health records, Internet-based data, published literature, and reporting systems. Given the magnitude of available data from these sources, a growing area is the use of computer algorithms to help automatically detect associations between medications and adverse effects. The main benefit of NLP is in the time savings associated with automation of various medication safety tasks such as the medication reconciliation process facilitated by computers, as well as the potential for near-real-time identification of adverse events for postmarketing surveillance such as those posted on social media that would otherwise go unanalyzed. NLP is limited by a lack of data sharing between health care organizations due to insufficient interoperability capabilities, inhibiting large-scale adverse event monitoring across populations. We anticipate that future work in this area will focus on the integration of data sources from different domains to improve the ability to identify potential adverse events more quickly and to improve clinical decision support with regard to a patient's estimated risk for specific adverse events at the time of medication prescription or review.
Background: Food allergy prevalence is reported to be increasing, but epidemiological data using patients' electronic health records (EHRs) remain sparse. Objective: We sought to determine the prevalence of food allergy and intolerance documented in the EHR allergy module. Methods: Using allergy data from a large health care organization's EHR between 2000 and 2013, we determined the prevalence of food allergy and intolerance by sex, racial/ethnic group, and allergen group. We examined the prevalence of reactions that were potentially IgE-mediated and anaphylactic. Data were validated using radioallergosorbent test and ImmunoCAP results, when available, for patients with reported peanut allergy. Results: Among 2.7 million patients, we identified 97,482 patients (3.6%) with 1 or more food allergies or intolerances (mean, 1.4 6 0.1). The prevalence of food allergy and intolerance was higher in females (4.2% vs 2.9%; P < .001) and Asians (4.3% vs 3.6%; P < .001). The most common food allergen groups were shellfish (0.9%), fruit or vegetable (0.7%), dairy (0.5%), and peanut (0.5%). Of the 103,659 identified reactions to foods, 48.1% were potentially IgE-mediated (affecting 50.8% of food allergy or intolerance patients) and 15.9% were anaphylactic. About 20% of patients with reported peanut allergy had a radioallergosorbent test/ImmunoCAP performed, of which 57.3% had an IgE level of grade 3 or higher. Conclusions: Our findings are consistent with previously validated methods for studying food allergy, suggesting that the EHR's allergy module has the potential to be used for clinical and epidemiological research. The spectrum of severity observed with
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