A value set for documenting adverse reactions in electronic health records

Goss, Foster R.; Lai, Kenneth H.; Topaz, Maxim; Acker, Warren W.; Kowalski, Leigh T.; Plasek, Joseph M.; Blumenthal, Kimberly G.; Seger, Diane L.; Slight, Sarah P; Fung, Kin Wah; Chang, Frank Y.; Bates, David W.; Zhou, Li

doi:10.1093/jamia/ocx139

Cited by 32 publications

(25 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The frequency of drug‐induced anaphylaxis was estimated to range between 0.2% and 3.1% in Europe . The registration of these cases in electronic health records (EHRs) is an important contribution for patient safety . This practice allows the characterization of the safety profiles of marketed drugs and may help to prevent further ADRs in certain patients.…”

Section: What Is Known and Objectivementioning

confidence: 70%

“…Similar findings were found in other studies . The pattern of hypersensitivity ADRs was relatively different in another study reviewing the allergy module of EHRs on 2.7 million US patients, with rash (14%) the most prevalent reaction, followed by hives (8.2%), gastrointestinal irritation (5.5%), pruritus (3.2%) and anaphylaxis (2.5%) . Of note, acute onset of illness simultaneously involving skin and/or mucosal involvement (eg angioedema, pruritus) and either respiratory compromise (eg dyspnoea, bronchospasm) or blood pressure drop/end‐organ dysfunction may assemble enough clinical criteria for the diagnosis of anaphylaxis …”

Section: Discussionmentioning

confidence: 99%

“…[7][8][9] The registration of these cases in electronic health records (EHRs) is an important contribution for patient safety. 10 This practice allows the characterization of the safety profiles of marketed drugs and may help to prevent further ADRs in certain patients. The aim of this study was to identify and characterize cases of drug-induced hypersensitivity reported in a Portuguese hospital over 5 years.…”

Section: What Is K Nown and Objec Tivementioning

confidence: 99%

See 2 more Smart Citations

Drug-induced hypersensitivity: A 5-year retrospective study in a hospital electronic health records database

et al. 2018

View full text Add to dashboard Cite

Summary What is known and Objective Hypersensitivity adverse drug reactions (HADRs) are associated with considerable morbidity and mortality. The aim of this study was to identify cases of HADRs within a hospital electronic health records (EHR) database. Methods Data were extracted from EHR through the Portuguese catalogue of allergies and other adverse reactions (CPARA). This registry allows the collection and sharing of information on HADRs in a structured and harmonized way across the healthcare system. This module is used by healthcare professionals to record HADRs within the EHR of each patient. It applies to patients admitted to hospital because of HADRs and also to inpatients developing such reactions during hospitalization. Data recorded from 2013 to 2017 within the Centro Hospitalar de Entre o Douro e Vouga (CHEDV) hospital (397 beds; ≈20 300 inpatients/year) were considered. The MedDRA® classification was used to codify HADRs. The ATC classification system was used to codify drugs. The concept of individual case safety report (ICSR) was considered for performing analyses. Results and discussion The database contained 464 valid cases (severe, n = 330; 71.1%), corresponding to 380 patients and 559 HADRs. Most patients were female (n = 254; 66.8%); the median age was 55 years. Approximately 0.1% local inhabitants and ≈0.4% inpatients have experienced HADRs over the study period. Most cases (n = 245; 52.8%) were associated with systemic antibacterials. Most HADRs were skin and subcutaneous tissue disorders (n = 266; 47.6%) and immune system disorders (n = 231; 41.3%). The pattern of suspected drugs implicated in anaphylactic reactions was the same as those involved in other HADRs. What is new and conclusion This registry contains HADRs that are relevant for the pharmacovigilance system, but none was spontaneously reported. The responsible authorities should address this problem. The results also reinforce the association between systemic antibacterials and HADRs.

show abstract

Section: What Is Known and Objectivementioning

confidence: 70%

Section: Discussionmentioning

confidence: 99%

Section: What Is K Nown and Objec Tivementioning

confidence: 99%

See 1 more Smart Citation

Drug-induced hypersensitivity: A 5-year retrospective study in a hospital electronic health records database

et al. 2018

View full text Add to dashboard Cite

show abstract

“…One study reconciled allergy information from emergency department notes, allowing for more accurate identification and coding of allergies within the EHR . The ability to do this would help to potentially separate medication allergies (i.e., immunoglobulin E hypersensitivity) from medication intolerances, as well as to determine temporality and the actual experiencer of the reaction to improve allergy documentation …”

Section: Discussionmentioning

confidence: 99%

“…67 The ability to do this would help to potentially separate medication allergies (i.e., immunoglobulin E hypersensitivity) from medication intolerances, as well as to determine temporality and the actual experiencer of the reaction to improve allergy documentation. 68 Existing Limitations for the Future A key barrier to the utility of NLP is that of interoperability between data sources. Interoperability between data sources allows greater ease in communicating necessary patient data and, in turn, would improve patient safety through improved accuracy of clinical documentation relied on for providing a patient's medical care.…”

Section: Commentary On Future Trendsmentioning

confidence: 99%

Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges

Wong

Plasek

Montecalvo³

et al. 2018

Pharmacotherapy

Self Cite

View full text Add to dashboard Cite

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.

show abstract

A Novel Informatics Tool to Detect Periprocedural Antibiotic Allergy Adverse Events for Near Real-time Surveillance to Support Audit and Feedback

et al. 2023

View full text Add to dashboard Cite

ImportanceStandardized processes for identifying when allergic-type reactions occur and linking reactions to drug exposures are limited.ObjectiveTo develop an informatics tool to improve detection of antibiotic allergic-type events.Design, Setting, and ParticipantsThis retrospective cohort study was conducted from October 1, 2015, to September 30, 2019, with data analyzed between July 1, 2021, and January 31, 2022. The study was conducted across Veteran Affairs hospitals among patients who underwent cardiovascular implantable electronic device (CIED) procedures and received periprocedural antibiotic prophylaxis. The cohort was split into training and test cohorts, and cases were manually reviewed to determine presence of allergic-type reaction and its severity. Variables potentially indicative of allergic-type reactions were selected a priori and included allergies entered in the Veteran Affair’s Allergy Reaction Tracking (ART) system (either historical [reported] or observed), allergy diagnosis codes, medications administered to treat allergic reactions, and text searches of clinical notes for keywords and phrases indicative of a potential allergic-type reaction. A model to detect allergic-type reaction events was iteratively developed on the training cohort and then applied to the test cohort. Algorithm test characteristics were assessed.ExposurePreprocedural and postprocedural prophylactic antibiotic administration.Main Outcomes and MeasuresAntibiotic allergic-type reactions.ResultsThe cohort of 36 344 patients included 34 703 CIED procedures with antibiotic exposures (mean [SD] age, 72 [10] years; 34 008 [98%] male patients); median duration of postprocedural prophylaxis was 4 days (IQR, 2-7 days; maximum, 45 days). The final algorithm included 7 variables: entries in the Veteran Affair’s hospitals ART, either historic (odds ratio [OR], 42.37; 95% CI, 11.33-158.43) or observed (OR, 175.10; 95% CI, 44.84-683.76); PheCodes for “symptoms affecting skin” (OR, 8.49; 95% CI, 1.90-37.82), “urticaria” (OR, 7.01; 95% CI, 1.76-27.89), and “allergy or adverse event to an antibiotic” (OR, 11.84, 95% CI, 2.88-48.69); keyword detection in clinical notes (OR, 3.21; 95% CI, 1.27-8.08); and antihistamine administration alone or in combination (OR, 6.51; 95% CI, 1.90-22.30). In the final model, antibiotic allergic-type reactions were identified with an estimated probability of 30% or more; positive predictive value was 61% (95% CI, 45%-76%); and sensitivity was 87% (95% CI, 70%-96%).Conclusions and RelevanceIn this retrospective cohort study of patients receiving periprocedural antibiotic prophylaxis, an algorithm with a high sensitivity to detect incident antibiotic allergic-type reactions that can be used to provide clinician feedback about antibiotic harms from unnecessarily prolonged antibiotic exposures was developed.

show abstract

A value set for documenting adverse reactions in electronic health records

Abstract: 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.

Cited by 32 publications

References 22 publications

Drug-induced hypersensitivity: A 5-year retrospective study in a hospital electronic health records database

Drug-induced hypersensitivity: A 5-year retrospective study in a hospital electronic health records database

Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges

A Novel Informatics Tool to Detect Periprocedural Antibiotic Allergy Adverse Events for Near Real-time Surveillance to Support Audit and Feedback

Contact Info

Product

Resources

About