Background The prevalence of drug allergies documented in electronic health records (EHRs) of large patient populations is understudied. Objective We aimed to describe the prevalence of common drug allergies and patient characteristics documented in EHRs of a large healthcare network over the last two decades. Methods Drug allergy data were obtained from EHRs of patients who visited two large tertiary care hospitals in Boston from 1990 to 2013. The prevalence of each drug and drug class was calculated and compared by sex and race/ethnicity. The number of allergies per patient was calculated and the frequency of patients having 1, 2, 3…, or 10+ drug allergies was reported. We also conducted a trend analysis by comparing the proportion of each allergy to the total number of drug allergies over time. Results Among 1 766 328 patients, 35.5% of patients had at least one reported drug allergy with an average of 1.95 drug allergies per patient. The most commonly reported drug allergies in this population were to penicillins (12.8%), sulfonamide antibiotics (7.4%), opiates (6.8%), and nonsteroidal anti‐inflammatory drugs (NSAIDs) (3.5%). The relative proportion of allergies to angiotensin‐converting enzyme (ACE) inhibitors and HMG CoA reductase inhibitors (statins) have more than doubled since early 2000s. Drug allergies were most prevalent among females and white patients except for NSAIDs, ACE inhibitors, and thiazide diuretics, which were more prevalent in black patients. Conclusion Females and white patients may be more likely to experience a reaction from common medications. An increase in reported allergies to ACE inhibitors and statins is noteworthy.

These findings underline the urgent need for more efforts to provide more accurate and relevant drug allergy alerts to help reduce alert override rates and improve alert fatigue.

Accurate electronic health records are important for clinical care and research as well as ensuring patient safety. It is crucial for misspelled words to be corrected in order to ensure that medical records are interpreted correctly. This paper describes the development of a spelling correction system for medical text. Our spell checker is based on Shannon's noisy channel model, and uses an extensive dictionary compiled from many sources. We also use named entity recognition, so that names are not wrongly corrected as misspellings. We apply our spell checker to three different types of free-text data: clinical notes, allergy entries, and medication orders; and evaluate its performance on both misspelling detection and correction. Our spell checker achieves detection performance of up to 94.4% and correction accuracy of up to 88.2%. We show that high-performance spelling correction is possible on a variety of clinical documents.

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

Background: Hypersensitivity reactions (HSRs) are immunologic responses to drugs. Identification of HSRs documented in the electronic health record (EHR) is important for patient safety. Objective: To examine HSR epidemiology using longitudinal EHR data from a large United States healthcare system. Methods: Patient demographic information and drug allergy data were obtained from the Partners Enterprise-wide Allergy Repository (PEAR) for two large tertiary care hospitals from 2000 to 2013. Drug-induced HSRs were categorized into immediate and delayed HSRs based on typical phenotypes. Causative drugs and drug groups were assessed. The prevalence of HSRs were determined, and sex and racial differences were analyzed.

Study objective We examine the characteristics of clinical decision support alerts triggered when opioids are prescribed, including alert type, override rates, adverse drug events associated with opioids, and preventable adverse drug events. Methods This was a retrospective chart review study assessing adverse drug event occurrences for emergency department (ED) visits in a large urban academic medical center using a commercial electronic health record system with clinical decision support. Participants include those aged 18 to 89 years who arrived to the ED every fifth day between September 2012 and January 2013. The main outcome was characteristics of opioid drug alerts, including alert type, override rates, opioid-related adverse drug events, and adverse drug event preventability by clinical decision support. Results Opioid drug alerts were more likely to be overridden than nonopioid alerts (relative risk 1.35; 95% confidence interval [CI] 1.21 to 1.50). Opioid drug-allergy alerts were twice as likely to be overridden (relative risk 2.24; 95% CI 1.74 to 2.89). Opioid duplicate therapy alerts were 1.57 times as likely to be overridden (95% CI 1.30 to 1.89). Fourteen of 4,581 patients experienced an adverse drug event (0.31%; 95% CI 0.15% to 0.47%), and 8 were due to opioids (57.1%). None of the adverse drug events were preventable by clinical decision support. However, 46 alerts were accepted for 38 patients that averted a potential adverse drug event. Overall, 98.9% of opioid alerts did not result in an actual or averted adverse drug event, and 96.3% of opioid alerts were overridden. Conclusion Overridden opioid alerts did not result in adverse drug events. Clinical decision support successfully prevented adverse drug events at the expense of generating a large volume of inconsequential alerts. To prevent 1 adverse drug event, providers dealt with more than 123 unnecessary alerts. It is essential to refine clinical decision support alerting systems to eliminate inconsequential alerts to prevent alert fatigue and maintain patient safety.

Key Points Question How accurate are dictated clinical documents created by speech recognition software, edited by professional medical transcriptionists, and reviewed and signed by physicians? Findings Among 217 clinical notes randomly selected from 2 health care organizations, the error rate was 7.4% in the version generated by speech recognition software, 0.4% after transcriptionist review, and 0.3% in the final version signed by physicians. Among the errors at each stage, 15.8%, 26.9%, and 25.9% involved clinical information, and 5.7%, 8.9%, and 6.4% were clinically significant, respectively. Meaning An observed error rate of more than 7% in speech recognition–generated clinical documents demonstrates the importance of manual editing and review.

Future studies should develop further natural language methods for a more detailed data analysis (i.e., identifying causality and temporal aspects in the social media data).