Overcoming the linguistic divide: a barrier to consumer health information

Seeking health information online has become very popular. Despite this popularity, health consumers face many barriers to successfully retrieving good quality health information. This paper reviews the literature on the linguistic divide between health consumers and consumer health information. Consumer health vocabularies (CHV) and natural language processing (NLP) show potential for bridging the divide, thereby improving recall and precision from information retrieval systems. Developers of digital libraries can incorporate CHV and (or) NLP as help tools to facilitate health consumerd's search success. Deeper issues, such as health consumers's mental representation of medical domain, must also be addressed in future research for optimal benefit from such help tools

Normalizing acronyms and abbreviations to aid patient understanding of clinical texts: ShARe/CLEF eHealth Challenge 2013, Task 2

Background: The ShARe/CLEF eHealth challenge lab aims to stimulate development of natural language processing and information retrieval technologies to aid patients in understanding their clinical reports. In clinical text, acronyms and abbreviations, also referenced as short forms, can be difficult for patients to understand. For one of three shared tasks in 2013 (Task 2), we generated a reference standard of clinical short forms normalized to the Unified Medical Language System. This reference standard can be used to improve patient understanding by linking to web sources with lay descriptions of annotated short forms or by substituting short forms with a more simplified, lay term. Methods: In this study, we evaluate 1) accuracy of participating systems’ normalizing short forms compared to a majority sense baseline approach, 2) performance of participants’ systems for short forms with variable majority sense distributions, and 3) report the accuracy of participating systems’ normalizing shared normalized concepts between the test set and the Consumer Health Vocabulary, a vocabulary of lay medical terms. Results: The best systems submitted by the five participating teams performed with accuracies ranging from 43 to 72 %. A majority sense baseline approach achieved the second best performance. The performance of participating systems for normalizing short forms with two or more senses with low ambiguity (majority sense greater than 80 %) ranged from 52 to 78 % accuracy, with two or more senses with moderate ambiguity (majority sense between 50 and 80 %) ranged from 23 to 57 % accuracy, and with two or more senses with high ambiguity (majority sense less than 50 %) ranged from 2 to 45 % accuracy. With respect to the ShARe test set, 69 % of short form annotations contained common concept unique identifiers with the Consumer Health Vocabulary. For these 2594 possible annotations, the performance of participating systems ranged from 50 to 75 % accuracy. Conclusion: Short form normalization continues to be a challenging problem. Short form normalization systems perform with moderate to reasonable accuracies. The Consumer Health Vocabulary could enrich its knowledge base with missed concept unique identifiers from the ShARe test set to further support patient understanding of unfamiliar medical terms.</p

Designing a Digital Medical Interview Assistant for Radiology

Radiologists rarely interact with the patients whose radiological images they are reviewing due to time and resource constraints. However, relevant information about the patient’s medical history could improve reporting performance and quality. In this work, our objective was to collect requirements for a digital medical interview assistant (DMIA) that collects the medical history from patients by means of a conversational agent and structures as well as provides the collected data to radiologists. Requirements were gathered based on a narrative literature review, a patient questionnaire and input from a radiologist. Based on these results, a system architecture for the DMIA was developed. 37 functional and 17 non-functional requirements were identified. The resulting architecture comprises five components, namely Chatbot, Natural language processing (NLP), Administration, Content Definition and Workflow Engine. To be able to quickly adapt the chatbot content according to the information needs of a specific radiological examination, there is a need for developing a sustainable process for the content generation that considers standardized data modelling as well as rewording of clinical language into consumer health vocabulary understandable to a diverse patient user group

Designing a Digital Medical Interview Assistant for Radiology.

Radiologists rarely interact with the patients whose radiological images they are reviewing due to time and resource constraints. However, relevant information about the patient's medical history could improve reporting performance and quality. In this work, our objective was to collect requirements for a digital medical interview assistant (DMIA) that collects the medical history from patients by means of a conversational agent and structures as well as provides the collected data to radiologists. Requirements were gathered based on a narrative literature review, a patient questionnaire and input from a radiologist. Based on these results, a system architecture for the DMIA was developed. 37 functional and 17 non-functional requirements were identified. The resulting architecture comprises five components, namely Chatbot, Natural language processing (NLP), Administration, Content Definition and Workflow Engine. To be able to quickly adapt the chatbot content according to the information needs of a specific radiological examination, there is a need for developing a sustainable process for the content generation that considers standardized data modelling as well as rewording of clinical language into consumer health vocabulary understandable to a diverse patient user group

An Automated Method to Enrich and Expand Consumer Health Vocabularies Using GloVe Word Embeddings

Clear language makes communication easier between any two parties. However, a layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical jargon, which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this dissertation, we present an automatic method to enrich existing concepts in a medical ontology with additional laymen terms and also to expand the number of concepts in the ontology that do not have associated laymen terms. Our work has the benefit of being applicable to vocabularies in any domain. Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. We improve these vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. By performing iterative feedback using GloVe’s candidate terms, we can boost the number of word occurrences in the co-occurrence matrix allowing our approach to work with a smaller training corpus. Our novel algorithms and GloVe were evaluated using two laymen datasets from the National Library of Medicine (NLM), the Open-Access and Collaborative Consumer Health Vocabulary (OAC CHV) and the MedlinePlus Healthcare Vocabulary. For our first goal, enriching concepts, the results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Our best algorithm enhanced the corpus with synonyms from WordNet, outperformed GloVe with an F-score relative improvement of 25%. For our second goal, expanding the number of concepts with related laymen’s terms, our synonym-enhanced GloVe outperformed GloVe with a relative F-score relative improvement of 63%. The results of the system were in general promising and can be applied not only to enrich and expand laymen vocabularies for medicine but any ontology for a domain, given an appropriate corpus for the domain. Our approach is applicable to narrow domains that may not have the huge training corpora typically used with word embedding approaches. In essence, by incorporating an external source of linguistic information, WordNet, and expanding the training corpus, we are getting more out of our training corpus. Our system can help building an application for patients where they can read their physician\u27s letters more understandably and clearly. Moreover, the output of this system can be used to improve the results of healthcare search engines, entity recognition systems, and many others

A method to quantify residents\u27 jargon use during counseling of standardized patients about cancer screening

Background Jargon is a barrier to effective patient-physician communication, especially when health literacy is low or the topic is complicated. Jargon is addressed by medical schools and residency programs, but reducing jargon usage by the many physicians already in practice may require the population-scale methods used in Quality Improvement. Objective To assess the amount of jargon used and explained during discussions about prostate or breast cancer screening. Effective communication is recommended before screening for prostate or breast cancer because of the large number of false-positive results and the possible complications from evaluation or treatment. Participants Primary care internal medicine residents. Measurements Transcripts of 86 conversations between residents and standardized patients were abstracted using an explicit-criteria data dictionary. Time lag from jargon words to explanations was measured using “statements,” each of which contains one subject and one predicate. Results Duplicate abstraction revealed reliability κ = 0.92. The average number of unique jargon words per transcript was 19.6 (SD = 6.1); the total jargon count was 53.6 (SD = 27.2). There was an average of 4.5 jargon-explanations per transcript (SD = 2.3). The ratio of explained to total jargon was 0.15. When jargon was explained, the average time lag from the first usage to the explanation was 8.4 statements (SD = 13.4). Conclusions The large number of jargon words and low number of explanations suggest that many patients may not understand counseling about cancer screening tests. Educational programs and faculty development courses should continue to discourage jargon usage. The methods presented here may be useful for feedback and quality improvement efforts

Umls-based analysis of medical terminology coverage for tags in diabetes-related blogs

There is a well-known terminology disparity between laypeople and health professionals. Using the Unified Medical Language System (UMLS), this study explores an exploratory study on the terminology usages of laypeople, focusing on diabetes. We explain the analysis pipeline of extracting laypeople’s medical terms and matching them to the existing medical controlled vocabulary system. The preliminary result shows the promise of using the UMLS and Tumblr data for such analysis