Joseph M. Plasek

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.

Mapping Partners Master Drug Dictionary to RxNorm using an NLP-based approach

OBJECTIVE To develop an automated method based on natural language processing (NLP) to facilitate the creation and maintenance of a mapping between RxNorm and a local medication terminology for interoperability and meaningful use purposes. METHODS We mapped 5961 terms from Partners Master Drug Dictionary (MDD) and 99 of the top prescribed medications to RxNorm. The mapping was conducted at both term and concept levels using an NLP tool, called MTERMS, followed by a manual review conducted by domain experts who created a gold standard mapping. The gold standard was used to assess the overall mapping between MDD and RxNorm and evaluate the performance of MTERMS. RESULTS Overall, 74.7% of MDD terms and 82.8% of the top 99 terms had an exact semantic match to RxNorm. Compared to the gold standard, MTERMS achieved a precision of 99.8% and a recall of 73.9% when mapping all MDD terms, and a precision of 100% and a recall of 72.6% when mapping the top prescribed medications. CONCLUSION The challenges and gaps in mapping MDD to RxNorm are mainly due to unique user or application requirements for representing drug concepts and the different modeling approaches inherent in the two terminologies. An automated approach based on NLP followed by human expert review is an efficient and feasible way for conducting dynamic mapping.

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.

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

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 patients 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 NLPs 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 patients estimated risk for specific adverse events at the time of medication prescription or review.

The Role of Nonverbal and Verbal Communication in a Multimedia Informed Consent Process

OBJECTIVE Nonverbal and verbal communication elements enhance and reinforce the consent form in the informed consent process and need to be transferred appropriately to multimedia formats using interaction design when re-designing the process. METHODS Observational, question asking behavior, and content analyses were used to analyze nonverbal and verbal elements of an informed consent process. RESULTS A variety of gestures, interruptions, and communication styles were observed. CONCLUSION In converting a verbal conversation about a textual document to multimedia formats, all aspects of the original process including verbal and nonverbal variation should be one part of an interaction community-centered design approach.

Rethink Data Sharing at the Dawn of a Health Data Economy (Preprint)

A health data economy has begun to form, but its rise has been tempered by the profound lack of sharing of both data and data products such as models, intermediate results, and annotated training corpora, and this severely limits the potential for triggering economic cluster effects. Economic cluster effects represent a means to elicit benefit from economies of scale from internal data innovations and are beneficial because they may mitigate challenges from external sources. Within institutions, data product sharing is needed to spark data entrepreneurship and data innovation, and cross-institutional sharing is also critical, especially for rare conditions.

Comment Topic Evolution on a Cancer Institution’s Facebook Page

OBJECTIVES Our goal was to identify and track the evolution of the topics discussed in free-text comments on a cancer institutions social media page. METHODS We utilized the Latent Dirichlet Allocation model to extract ten topics from free-text comments on a cancer research institutions Facebook™ page between January 1, 2009, and June 30, 2014. We calculated Pearson correlation coefficients between the comment categories to demonstrate topic intensity evolution. RESULTS A total of 4,335 comments were included in this study, from which ten topics were identified: greetings (17.3%), comments about the cancer institution (16.7%), blessings (10.9%), time (10.7%), treatment (9.3%), expressions of optimism (7.9%), tumor (7.5%), father figure (6.3%), and other family members & friends (8.2%), leaving 5.1% of comments unclassified. The comment distributions reveal an overall increasing trend during the study period. We discovered a strong positive correlation between greetings and other family members & friends (r=0.88; p<0.001), a positive correlation between blessings and the cancer institution (r=0.65; p<0.05), and a negative correlation between blessings and greetings (r=-0.70; p<0.05). CONCLUSIONS A cancer institutions social media platform can provide emotional support to patients and family members. Topic analysis may help institutions better identify and support the needs (emotional, instrumental, and social) of their community and influence their social media strategy.