OBJECTIVE: Accurate, complete allergy histories are critical for decision-making and medication prescription. However, allergy information is often spread across the electronic health record (EHR); thus, allergy lists are often inaccurate or incomplete. Discrepant allergy information can lead to suboptimal or unsafe clinical care and contribute to alert fatigue. We developed an allergy reconciliation module within Mass General Brigham (MGB)'s EHR to support accurate and intuitive reconciliation of discrepancies in the allergy list, thereby enhancing patient safety. MATERIALS AND METHODS: We combined data-driven methods and knowledge from domain experts to develop 5 mechanisms to compare allergy information across the EHR and designed a user interface to display discrepancies and suggested reconciliation actions, with links to relevant data sources. Qualitative and quantitative analyses were conducted to assess the module's performance and measure user acceptance. RESULTS: We implemented and tested the proposed allergy reconciliation mechanisms and module. A comprehensive integration workflow was developed for the module, which was piloted among 111 primary care physicians at MGB. F1 scores of the reconciliation mechanisms range from 0.86 to 1.0. Qualitative analysis showed majority positive feedback from pilot users. DISCUSSION: Our allergy reconciliation module achieved high performance, and physicians who used it largely accepted its recommendations. However, 56% of the pilot group ultimately did not use the module. User engagement and education are likely needed to increase adoption. CONCLUSION: We built a module to automatically identify discrepancies within patients' allergy records and remind providers to reconcile and update the allergy list. Its high accuracy shows promise for enhancing patient safety and utility of drug allergy alerts.

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 Healthcare's 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.

We implemented a web-based, patient-centered toolkit that engages patients/caregivers in the hospital plan of care by facilitating education and patient-provider communication. Of the 585 eligible patients approached on medical intensive care and oncology units, 239 were enrolled (119 patients, 120 caregivers). The most common reason for not approaching the patient was our inability to identify a health care proxy when a patient was incapacitated. Significantly more caregivers were enrolled in medical intensive care units compared with oncology units (75% vs 32%; P < .01). Of the 239 patient/caregivers, 158 (66%) and 97 (41%) inputted a daily and overall goal, respectively. Use of educational content was highest for medications and test results and infrequent for problems. The most common clinical theme identified in 291 messages sent by 158 patients/caregivers was health concerns, needs, preferences, or questions (19%, 55 of 291). The average system usability scores and satisfaction ratings of a sample of surveyed enrollees were favorable. From analysis of feedback, we identified barriers to adoption and outlined strategies to promote use.

OBJECTIVE: Accurate food adverse sensitivity documentation in electronic health records (EHRs) is crucial to patient safety. This study examined, encoded, and grouped foods that caused any adverse sensitivity in a large allergy repository using natural language processing and standard terminologies. METHODS: Using the Medical Text Extraction, Reasoning, and Mapping System (MTERMS), we processed both structured and free-text entries stored in an enterprise-wide allergy repository (Partners' Enterprise-wide Allergy Repository), normalized diverse food allergen terms into concepts, and encoded these concepts using the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) and Unique Ingredient Identifiers (UNII) terminologies. Concept coverage also was assessed for these two terminologies. We further categorized allergen concepts into groups and calculated the frequencies of these concepts by group. Finally, we conducted an external validation of MTERMS's performance when identifying food allergen terms, using a randomized sample from a different institution. RESULTS: We identified 158 552 food allergen records (2140 unique terms) in the Partners repository, corresponding to 672 food allergen concepts. High-frequency groups included shellfish (19.3%), fruits or vegetables (18.4%), dairy (9.0%), peanuts (8.5%), tree nuts (8.5%), eggs (6.0%), grains (5.1%), and additives (4.7%). Ambiguous, generic concepts such as "nuts" and "seafood" accounted for 8.8% of the records. SNOMED-CT covered more concepts than UNII in terms of exact (81.7% vs 68.0%) and partial (14.3% vs 9.7%) matches. DISCUSSION: Adverse sensitivities to food are diverse, and existing standard terminologies have gaps in their coverage of the breadth of allergy concepts. CONCLUSION: New strategies are needed to represent and standardize food adverse sensitivity concepts, to improve documentation in EHRs.

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.

BACKGROUND: Maintaining continuity of care (CoC) in the inpatient setting is dependent on aligning goals and tasks with the plan of care (POC) during multidisciplinary rounds (MDRs). A number of locally developed rounding tools exist, yet there is a lack of standard content and functional specifications for electronic tools to support MDRs within and across settings. OBJECTIVE: To identify content and functional requirements for an MDR tool to support CoC. MATERIALS AND METHODS: We collected discrete clinical data elements (CDEs) discussed during rounds for 128 acute and critical care patients. To capture CDEs, we developed and validated an iPad-based observational tool based on informatics CoC standards. We observed 19 days of rounds and conducted eight group and individual interviews. Descriptive and bivariate statistics and network visualization were conducted to understand associations between CDEs discussed during rounds with a particular focus on the POC. Qualitative data were thematically analyzed. All analyses were triangulated. RESULTS: We identified the need for universal and configurable MDR tool views across settings and users and the provision of messaging capability. Eleven empirically derived universal CDEs were identified, including four POC CDEs: problems, plan, goals, and short-term concerns. Configurable POC CDEs were: rationale, tasks/'to dos', pending results and procedures, discharge planning, patient preferences, need for urgent review, prognosis, and advice/guidance. DISCUSSION: Some requirements differed between settings; yet, there was overlap between POC CDEs. CONCLUSIONS: We recommend an initial list of 11 universal CDEs for continuity in MDRs across settings and 27 CDEs that can be configured to meet setting-specific needs.