Foster R. Goss

Rising drug allergy alert overrides in electronic health records: an observational retrospective study of a decade of experience

OBJECTIVE There have been growing concerns about the impact of drug allergy alerts on patient safety and provider alert fatigue. The authors aimed to explore the common drug allergy alerts over the last 10 years and the reasons why providers tend to override these alerts. DESIGN Retrospective observational cross-sectional study (2004-2013). MATERIALS AND METHODS Drug allergy alert data (n = 611,192) were collected from two large academic hospitals in Boston, MA (USA). RESULTS Overall, the authors found an increase in the rate of drug allergy alert overrides, from 83.3% in 2004 to 87.6% in 2013 (P < .001). Alarmingly, alerts for immune mediated and life threatening reactions with definite allergen and prescribed medication matches were overridden 72.8% and 74.1% of the time, respectively. However, providers were less likely to override these alerts compared to possible (cross-sensitivity) or probable (allergen group) matches (P < .001). The most common drug allergy alerts were triggered by allergies to narcotics (48%) and other analgesics (6%), antibiotics (10%), and statins (2%). Only slightly more than one-third of the reactions (34.2%) were potentially immune mediated. Finally, more than half of the overrides reasons pointed to irrelevant alerts (i.e., patient has tolerated the medication before, 50.9%) and providers were significantly more likely to override repeated alerts (89.7%) rather than first time alerts (77.4%, P < .001). DISCUSSION AND CONCLUSIONS 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.

Prospective pilot study of a tablet computer in an Emergency Department.

BACKGROUND The recent availability of low-cost tablet computers can facilitate bedside information retrieval by clinicians. OBJECTIVE To evaluate the effect of physician tablet use in the Emergency Department. DESIGN Prospective cohort study comparing physician workstation usage with and without a tablet. SETTING 55,000 visits/year Level 1 Emergency Department at a tertiary academic teaching hospital. PARTICIPANTS 13 emergency physicians (7 Attendings, 4 EM3s, and 2 EM1s) worked a total of 168 scheduled shifts (130 without and 38 with tablets) during the study period. INTERVENTION Physician use of a tablet computer while delivering direct patient care in the Emergency Department. MAIN OUTCOME MEASURES The primary outcome measure was the time spent using the Emergency Department Information System (EDIS) at a computer workstation per shift. The secondary outcome measure was the number of EDIS logins at a computer workstation per shift. RESULTS Clinician use of a tablet was associated with a 38min (17-59) decrease in time spent per shift using the EDIS at a computer workstation (p<0.001) after adjusting for clinical role, location, and shift length. The number of logins was also associated with a 5-login (2.2-7.9) decrease per shift (p<0.001) after adjusting for other covariates. CONCLUSION Clinical use of a tablet computer was associated with a reduction in the number of times physicians logged into a computer workstation and a reduction in the amount of time they spent there using the EDIS. The presumed benefit is that decreasing time at a computer workstation increases physician availability at the bedside. However, this association will require further investigation.

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

The prevalence of drug allergies documented in electronic health records (EHRs) of large patient populations is understudied.

EHR adoption across China's tertiary hospitals: A cross-sectional observational study

HEADING EHR adoption across Chinas tertiary hospitals: a cross-sectional observation study OBJECTIVES To assess electronic health record (EHR) adoption in Chinese tertiary hospitals using a nation-wide standard EHR grading model. METHODS The Model of EHR Grading (MEG) was used to assess the level of EHR adoption across 848 tertiary hospitals. MEG defines 37 EHR functions (e.g., order entry) which are grouped by 9 roles (e.g., inpatient physicians) and grades each function and the overall EHR adoption into eight levels (0-7). We assessed the MEG level of the involved hospitals and calculated the average score of the 37 EHR functions. A multivariate analysis was performed to explore the influencing factors (including hospital characteristics and information technology (IT) investment) of total score and scores of 9 roles. RESULTS Of the 848 hospitals, 260 (30.7%) were Level Zero, 102 (12.0%) were Level One, 269 (31.7%) were Level Two, 188 (22.2%) were Level Three, 23 (2.7%) were Level Four, 5 (0.6%) was Level Five, 1 (0.1%) were Level Six, and none achieved Level Seven. The scores of hospitals in eastern and western China were higher than those of hospitals in central areas. Bed size, outpatient admission, total income in 2011, percent of IT investment per income in 2011, IT investment in last 3 years, number of IT staff, and duration of EHR use were significant factors for total score. CONCLUSIONS We examined levels of EHR adoption in 848 Chinese hospitals and found that most of them have only basic systems, around level 2 and 0. Very few have a higher score and level for clinical information using and sharing.

Automated misspelling detection and correction in clinical free-text records

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 Shannons 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.

Evaluating standard terminologies for encoding allergy information

OBJECTIVE Allergy documentation and exchange are vital to ensuring patient safety. This study aims to analyze and compare various existing standard terminologies for representing allergy information. METHODS Five terminologies were identified, including the Systemized Nomenclature of Medical Clinical Terms (SNOMED CT), National Drug File-Reference Terminology (NDF-RT), Medication Dictionary for Regulatory Activities (MedDRA), Unique Ingredient Identifier (UNII), and RxNorm. A qualitative analysis was conducted to compare desirable characteristics of each terminology, including content coverage, concept orientation, formal definitions, multiple granularities, vocabulary structure, subset capability, and maintainability. A quantitative analysis was also performed to compare the content coverage of each terminology for (1) common food, drug, and environmental allergens and (2) descriptive concepts for common drug allergies, adverse reactions (AR), and no known allergies. RESULTS Our qualitative results show that SNOMED CT fulfilled the greatest number of desirable characteristics, followed by NDF-RT, RxNorm, UNII, and MedDRA. Our quantitative results demonstrate that RxNorm had the highest concept coverage for representing drug allergens, followed by UNII, SNOMED CT, NDF-RT, and MedDRA. For food and environmental allergens, UNII demonstrated the highest concept coverage, followed by SNOMED CT. For representing descriptive allergy concepts and adverse reactions, SNOMED CT and NDF-RT showed the highest coverage. Only SNOMED CT was capable of representing unique concepts for encoding no known allergies. CONCLUSIONS The proper terminology for encoding a patients allergy is complex, as multiple elements need to be captured to form a fully structured clinical finding. Our results suggest that while gaps still exist, a combination of SNOMED CT and RxNorm can satisfy most criteria for encoding common allergies and provide sufficient content coverage.

Clinically Inconsequential Alerts: The Characteristics of Opioid Drug Alerts and Their Utility in Preventing Adverse Drug Events in the Emergency Department

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.

Prevalence of food allergies and intolerances documented in electronic health records

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 organizations 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 ± 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 EHRs allergy module has the potential to be used for clinical and epidemiological research. The spectrum of severity observed with food allergy highlights the critical need for more allergy evaluations.

A value set for documenting adverse reactions in electronic health records

Objective To develop a comprehensive value set for documenting and encoding adverse reactions in the allergy module of an electronic health record. Materials and Methods We analyzed 2 471 004 adverse reactions stored in Partners Healthcares Enterprise-wide Allergy Repository (PEAR) of 2.7 million patients. Using the Medical Text Extraction, Reasoning, and Mapping System, we processed both structured and free-text reaction entries and mapped them to Systematized Nomenclature of Medicine - Clinical Terms. We calculated the frequencies of reaction concepts, including rare, severe, and hypersensitivity reactions. We compared PEAR concepts to a Federal Health Information Modeling and Standards value set and University of Nebraska Medical Center data, and then created an integrated value set. Results We identified 787 reaction concepts in PEAR. Frequently reported reactions included: rash (14.0%), hives (8.2%), gastrointestinal irritation (5.5%), itching (3.2%), and anaphylaxis (2.5%). We identified an additional 320 concepts from Federal Health Information Modeling and Standards and the University of Nebraska Medical Center to resolve gaps due to missing and partial matches when comparing these external resources to PEAR. This yielded 1106 concepts in our final integrated value set. The presence of rare, severe, and hypersensitivity reactions was limited in both external datasets. Hypersensitivity reactions represented roughly 20% of the reactions within our data. Discussion We developed a value set for encoding adverse reactions using a large dataset from one health system, enriched by reactions from 2 large external resources. This integrated value set includes clinically important severe and hypersensitivity reactions. Conclusion This work contributes a value set, harmonized with existing data, to improve the consistency and accuracy of reaction documentation in electronic health records, providing the necessary building blocks for more intelligent clinical decision support for allergies and adverse reactions.

Incidence of speech recognition errors in the emergency department.

BACKGROUND Physician use of computerized speech recognition (SR) technology has risen in recent years due to its ease of use and efficiency at the point of care. However, error rates between 10 and 23% have been observed, raising concern about the number of errors being entered into the permanent medical record, their impact on quality of care and medical liability that may arise. Our aim was to determine the incidence and types of SR errors introduced by this technology in the emergency department (ED). SETTING Level 1 emergency department with 42,000 visits/year in a tertiary academic teaching hospital. METHODS A random sample of 100 notes dictated by attending emergency physicians (EPs) using SR software was collected from the ED electronic health record between January and June 2012. Two board-certified EPs annotated the notes and conducted error analysis independently. An existing classification schema was adopted to classify errors into eight errors types. Critical errors deemed to potentially impact patient care were identified. RESULTS There were 128 errors in total or 1.3 errors per note, and 14.8% (n=19) errors were judged to be critical. 71% of notes contained errors, and 15% contained one or more critical errors. Annunciation errors were the highest at 53.9% (n=69), followed by deletions at 18.0% (n=23) and added words at 11.7% (n=15). Nonsense errors, homonyms and spelling errors were present in 10.9% (n=14), 4.7% (n=6), and 0.8% (n=1) of notes, respectively. There were no suffix or dictionary errors. Inter-annotator agreement was 97.8%. CONCLUSIONS This is the first estimate at classifying speech recognition errors in dictated emergency department notes. Speech recognition errors occur commonly with annunciation errors being the most frequent. Error rates were comparable if not lower than previous studies. 15% of errors were deemed critical, potentially leading to miscommunication that could affect patient care.