Why is it important to ask a patient about allergies before giving a medication?

Abstract

Background: Worldwide, the burden of allergies—in particular, drug allergies—is growing. In the process of prescribing, dispensing, or administering a drug, a medication error may occur and can have adverse consequences; for example, a drug may be given to a patient with a documented allergy to that particular drug. Computerized physician order entry [CPOE] systems with built-in clinical decision support systems [CDSS] have the potential to prevent such medication errors and adverse events.

Objective: The aim of this review is to provide a comprehensive overview regarding all aspects of CDSS for drug allergy, including documenting, coding, rule bases, alerts and alert fatigue, and outcome evaluation.

Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA] guidelines were followed as much as possible and searches were conducted in 5 databases using CPOE, CDSS, alerts, and allergic or allergy as keywords. Bias could not be evaluated according to PRISMA guidelines due to the heterogeneity of study types included in the review.

Results: Of the 3160 articles considered, 60 met the inclusion criteria. A further 9 articles were added based on expert opinion, resulting in a total of 69 articles. An interrater agreement of 90.9% with a reliability Κ=.787 [95% CI 0.686-0.888] was reached. Large heterogeneity across study objectives, study designs, study populations, and reported results was found. Several key findings were identified. Evidence of the usefulness of clinical decision support for drug allergies has been documented. Nevertheless, there are some important problems associated with their use. Accurate and structured documenting of information on drug allergies in electronic health records [EHRs] is difficult, as it is often not clear to healthcare providers how and where to document drug allergies. Besides the underreporting of drug allergies, outdated or inaccurate drug allergy information in EHRs poses an important problem. Research on the use of coding terminologies for documenting drug allergies is sparse. There is no generally accepted standard terminology for structured documentation of allergy information. The final key finding is the consistently reported low specificity of drug allergy alerts. Current systems have high alert override rates of up to 90%, leading to alert fatigue. Important challenges remain for increasing the specificity of drug allergy alerts. We found only one study specifically reporting outcomes related to CDSS for drug allergies. It showed that adverse drug events resulting from overridden drug allergy alerts do not occur frequently.

Conclusions: Accurate and comprehensive recording of drug allergies is required for good use of CDSS for drug allergy screening. We found considerable variation in the way drug allergy are recorded in EHRs. It remains difficult to reduce drug allergy alert overload while maintaining patient safety as the highest priority. Future research should focus on improving alert specificity, thereby reducing override rates and alert fatigue. Also, the effect on patient outcomes and cost-effectiveness should be evaluated.

doi:10.2196/jmir.8206

Keywords

Introduction

Worldwide, the burden of allergies is growing—in particular, drug allergies [DAs] are becoming increasingly common []. DAs can be categorized as abnormal immunoglobulin E-mediated reactions [eg, anaphylaxis] or delayed, nonimmunoglobulin E-mediated reactions, which are generally less severe [eg, intolerances] [].

DAs are perceived as an important problem. In a study conducted by the European Network on Drug Allergy and the EAACI Drug Allergy Interest group, 10% of parents reported that their child was allergic to a drug []. A study in a tertiary care academic medical center in Chicago reported a DA prevalence of 25% in the general adult population []. Looking at the clinical investigations of suspected reactions, the results demonstrate that these numbers are overvalued []. In a general hospital in Singapore, the estimated incidence of DAs was 4.20 per 1000 hospitalizations [95% CI 2.93-5.46] and the estimated mortality attributable to DA was 0.09 per 1000 hospitalizations [95% CI 0.06-0.12] []. A study in a university hospital in Korea reported an estimated incidence of drug hypersensitivity reactions of 1.8 per 1000 hospital admissions [].

In the process of prescribing, dispensing, or administering a drug, a medication error can occur and may have adverse consequences, for example, when a drug is given to a patient with a documented DA to this particular drug []. Only a minority [0.25%] of these medication errors result in an adverse drug event [ADE], but allergic reactions represent an important cause of preventable ADEs caused by medication errors [,]. It was estimated that 12.1% of all medication errors with the potential for an ADE arise from incomplete or incorrect allergy documentation [].

Bates et al [] and Classen et al [] estimated that each ADE resulted in a prolonged length of hospital stay of 2.2 and 1.7 days, respectively. Looking more specifically at penicillin allergy, Macy et al [] demonstrated that in the Kaiser Foundation Hospitals in Southern California, 0.59 additional hospital days [95% CI 0.47-0.71] per hospitalization resulted in an extra cost of US $1252.90 in 2012.

CPOE systems with built-in CDS have the potential to prevent such medication errors and consequent ADEs [-]. When a prescription poses a threat to the patient, the clinical decision support system [CDSS] warns the user by providing an alert message. However, it is well known that current CDSS for DA checking are impaired by alert fatigue caused by low alert specificity [-].

Several systematic reviews have been conducted to evaluate CPOE and CDSS in general or in specific domains of clinical care such as pediatrics [-,-]. To the best of our knowledge, no systematic review has been conducted focusing specifically on CDSS for DA. In this systematic review, we aimed to provide a comprehensive overview of all aspects of CDSS for DA including documenting, coding, rule bases, alerts and alert fatigue, and outcome evaluation.

Methods

Search Strategy

A systematic literature review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA] guidelines for systematic reviews and meta-analyses [] as much as possible. Bias could not be evaluated according to the PRISMA guidelines due to the heterogeneity of study types included in the review. Here, we focused on searching for articles related to CDSS and associated alerts in the domain of DAs. We performed searches in the bibliographic libraries of Cumulative Index to Nursing and Allied Health Literature [CINAHL], Cochrane Library, Embase, Ovid, and PubMed from database inception up to February 2016. The search strategy for the 5 databases is provided in . Because the aim of the review was to provide a broad overview of all aspects of DA-related CDSS, reviews and conference proceedings were also included. Only English language papers were included. Additional publications of interest that included information relevant to this review were included based on expert opinion. Our search strategy is presented in . The terms “Computerized Physician Order Entry” [CPOE] and “Clinical Decision Support System” [CDSS] were combined with the term “alert.” These terms were combined with the term “allergic” or “allergy” to limit the scope to the allergy field.

Study Selection

The titles and abstracts of identified articles were independently screened by two researchers [LL and SVL] to assess inclusion in the full review []. If one or both reviewers selected the paper for further evaluation, we included the article for full assessment. Articles were included for analysis if the study involved at least one of the following: [1] prevalence of allergy alerts; [2] coding or documenting of DA information; [3] implementation of a CDSS for DA; [4] perceptions of care providers on CDS for DAs; or [5] alert acceptance and interface design in the domain of allergies. Disagreements were discussed with a third reviewer [PC] until consensus was reached.

Data Extraction

From each article included, the two researchers [LL and SVL] extracted predefined information including the author names, year of publication, main topic of the paper, aim of the study, study design, number of subjects [care providers, alerts, etc], and key findings. The third reviewer [PC] evaluated the extracted data, and disagreements were resolved by consensus.

Assessment of risk of bias was not conducted because the heterogeneity in the quantitative and qualitative study designs, reviews, and reports did not allow for a comprehensive and consistent evaluation of bias.

Results

Study Selection and Reviewer Agreement

We started the study with 3160 articles from 5 different literary sources []. After the removal of duplicates [725 duplicates], 2435 articles remained for title and abstract review. Eventually, 186 articles were included for full text review, of which 60 were included in this review. The interrater reliability between LL and SVL was calculated using Cohen’s kappa. An interrater agreement of 90.9% with a reliability of Κ=.787 [95% CI 0.686-0.888] was reached between the two reviewers. Additionally, 9 articles were added based on expert opinion, resulting in a total selection of 69 articles. Although these 9 articles were not retrieved by the search query, they reported on aspects relevant for this review, including information related to the coding of allergy information, reporting of DAs, and strategies for improving CPOE alerts. These articles were identified by PC from automatic weekly updates on PubMed [My NCBI]. These weekly updates were based on separate queries with individual key words including “Computerized Physician Order Entry,” “Clinical Decision Support,” “Medication error,” and “Drug allergy.” PC collected and indexed relevant articles from this weekly list using reference manager software over the years. Articles included in the library of the reference software that dealt with relevant topics but were not discovered via the search queries were included.

Study Characteristics

The names of the authors, year of publication, study design, aim of the study, and key findings are shown in . Papers in are organized by topic [,,-,,-]. Papers belonging to different topics are categorized in all corresponding topics. In , a table with the number of subjects included in the study can be found. Most studies [56/69, 81%] were published after 2005. The 69 studies consisted of 28 observational studies [23 retrospective studies, 2 cross-sectional studies, 1 prospective time series analysis, 1 prospective study with interviews, and 1 cohort study], 13 review articles, 8 practice experiences, 6 before-after studies, 3 surveys, 3 controlled trials [1 randomized controlled trial, 1 randomized crossover study, and 1 nonrandomized controlled trial], 2 economic evaluations, 2 focus group studies, 1 scenario-based simulation study, 1 study describing a draft for an algorithm to classify information automatically, 1 study discussing a workshop, and 1 study describing a comparative study on standards in the DA field.

Documenting the Presence or Absence of an Allergy

For a functional CDSS, accurate and consistent documentation of patient allergy information is necessary. Currently, there is no agreement about what needs to be recorded and how to do so []. In 1964, Mills addressed the importance of capturing information on allergies and drugs, proposing a new checklist that served as a guideline in hospitals []. A similar initiative was taken more recently by Burrell et al [], who introduced a pharmacist-driven protocol in a hospital to improve the completeness of DA or intolerance documentation. A review of medical notes in a general district hospital demonstrated poor documentation practices where 97.4% [114/117] of drug allergy boxes were only partially completed and 2.6% [3/117] had nothing documented []. In another analysis comparing two oncology wards, one ward showed 100% consistency, while the other ward demonstrated drug charts with allergy entries in 82.4% of cases, of which only 68.8% corresponded to information in the medical notes []. Failure to accurately document DAs may lead to prescribing and administering medications that could be harmful to the patient. Besides accurate documentation, correct patient identification with linking of medication information with patient DA information, for example wristband barcoding, is required for real-time CDS [].

Lopez-Gonzalez et al [] reviewed factors for not reporting ADRs and found that the most prominent factor associated with underreporting was ignorance based on the fact that physicians often think that only severe ADRs need to be reported. Secondary factors, such as the hierarchical nature of hospital culture combined with stressful working conditions, also contributed to prescribing errors []. Moreover, at the time of documenting, there is often no clear distinction between a real allergy-related ADR and other minor reactions [].

Inaccurate or outdated DA information can also be a factor that influences the functioning of a CPOE system with CDS. Rimawi et al [] observed a small use case [150 patients] that was documented as intolerant to penicillin even though a negative penicillin skin test was observed. Of these, 36% [20/55] patients who revisited the medical center within the year were redocumented as having a penicillin allergy without proper indication.

Porter et al [] demonstrated that without healthcare personnel re-asking and validating information, there is a significant risk of error at the decision step of ordering or prescribing medications. Additionally, side effects related to the drug’s primary pharmacological effect are sometimes misinterpreted and documented as DAs, resulting in inaccurate DA information.

The more complete and accurate DAs recorded in patients’ EHRs, the greater the potential of CPOE systems with CDS to improve patient safety and reduce medication-related costs [].

Coding

Information on how allergy data are structured or coded in EHRs is scarce. Slight et al [] recently stated that the US government has not yet specified what standard terminologies should be used to structure allergy information.

A first approach is to enter information concerning DAs in free text. In order to use free text information for CDSS, natural language processing is applied taking the context into consideration []. Currently, this technique is not widely used because of the difficult nature of natural language processing.

A review of the available literature indicates that different coding systems are used for documenting DA information in EHRs, including International Classification of Diseases [ICD] [,], Systematized Nomenclature of Medicine, Clinical Terms [SNOMED CT] [], RxNorm, and National Drug File-Reference Terminology [NDF-RT] []. Sometimes mapped coding schemes are used to add functionality. Benkhaial et al [] used the Anatomical Therapeutic Chemical classification for mapping all drugs belonging to a specific ICD group. However, mapping might not always be this simple. Bernstein [] recently indicated in a Danish use case that there is not yet a clear consensus regarding the alert information concept [eg, drug alerts] and how drug allergy or other allergy subtypes are linked to that concept.

Goss et al [] performed a comparative study of the SNOMED CT, NDF-RT, Medication Dictionary for Regulatory Activities, unique ingredient identifier, and RxNorm standards for encoding allergy information. The qualitative part of their study demonstrated that SNOMED CT had the most desirable characteristics, including concept coverage, subset capabilities, and vocabulary structure. The quantitative part showed that RxNorm had the highest concept coverage to represent drug allergens, followed by unique ingredient identifier, SNOMED CT, NDF-RT, and Medication Dictionary for Regulatory Activities. SNOMED CT was the only coding system capable of representing unique concepts to encode ignorance of allergies.

The option to have an entry to indicate the ignorance of allergies is important for patient care because not documenting any DA information does not necessarily mean that there are no known DAs. Abookire et al [] stated that “every hospitalized patient should have DAs entered by the admitting physician [this is a forced entry; 'no allergies' may be entered].”

Rule Bases

In the literature, we observed underreporting of the rule bases used to support CDSS. Most CDSS for DA screening are knowledge-based systems supported by evidence-based rule databases. Some organizations use internally developed rule bases [], while others use vendor-supplied rule bases [].

We observed two types of CDSS in the literature: basic and complex. Basic CDSS provide alerts when a prescribed drug is listed in the patient’s DA list. In these systems, rules are implemented to screen for cross-reactivity within and between drug classes []. More complex CDSS use an inference mechanism to generate recommendations specific to a patient by integrating contextual information from the patient’s EHR [eg, previously tolerated administrations of the drug and results from DA tests] [].

As reported by Kuperman et al [], healthcare provider organizations ideally use a combination of vendor-supplied rule sets, which are developed by other organizations, and internally developed rules, which are derived from the literature and national and local consensus on what constitutes best practice. In any system, as medicine evolves and clinical knowledge grows, a timely review of the rule bases is warranted, for example using a Delphi approach to analyze what rules are useful [].

Alerts and Alert Fatigue

Alert fatigue has been defined as “declining physician responsiveness to a particular type of alert as the physician is repeatedly exposed to that alert over a period of time, gradually becoming ‘fatigued’ or desensitized to it” []. Alert fatigue caused by one type of alert may also lead to declined responsiveness to other types of CDS alerts [eg, drug-drug interaction, DDI, alerts]. The state of the art in CDS for DA is such that alerts are not specific enough, resulting in high override rates [,]. Current systems, which generate an alert at the moment of prescribing, have very high override rates of over 90% [,]. The first concern regarding increasing DA alerting rates and overrides was raised by Abookire et al []. This problem has since been investigated in several other studies. Bryant et al [] retrospectively analyzed physician responses to DDI and DA interaction alerts in two university hospitals and reported high override rates in all categories, ie, 92.87% [2280/2455 alerts] in general and 90.86% [1183/1302 alerts] for DAs. No significant difference in override rates was observed between hospitals or between physicians-in-training and residents. Topaz et al [] demonstrated a significant increase in DA alert overrides from 83.3% in 2004 to 87.6% [P