Drug allergies documented in electronic health records of a large healthcare system.

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.

Representation of information about family relatives as structured data in electronic health records.

BACKGROUND: The ability to manage and leverage family history information in the electronic health record (EHR) is crucial to delivering high-quality clinical care. OBJECTIVES: We aimed to evaluate existing standards in representing relative information, examine this information documented in EHRs, and develop a natural language processing (NLP) application to extract relative information from free-text clinical documents. METHODS: We reviewed a random sample of 100 admission notes and 100 discharge summaries of 198 patients, and also reviewed the structured entries for these patients in an EHR system's family history module. We investigated the two standards used by Stage 2 of Meaningful Use (SNOMED CT and HL7 Family History Standard) and identified coverage gaps of each standard in coding relative information. Finally, we evaluated the performance of the MTERMS NLP system in identifying relative information from free-text documents. RESULTS: The structure and content of SNOMED CT and HL7 for representing relative information are different in several ways. Both terminologies have high coverage to represent local relative concepts built in an ambulatory EHR system, but gaps in key concept coverage were detected; coverage rates for relative information in free-text clinical documents were 95.2% and 98.6%, respectively. Compared to structured entries, richer family history information was only available in free-text documents. Using a comprehensive lexicon that included concepts and terms of relative information from different sources, we expanded the MTERMS NLP system to extract and encode relative information in clinical documents and achieved a corresponding precision of 100% and recall of 97.4%. CONCLUSIONS: Comprehensive assessment and user guidance are critical to adopting standards into EHR systems in a meaningful way. A significant portion of patients' family history information is only documented in free-text clinical documents and NLP can be used to extract this information.