Text Simplification Using Consumer Health Vocabulary to Generate Patient-Centered Radiology Reporting: Translation and Evaluation

Bibliometric analysis confirms expected trends in consumer health information publications

BACKGROUND

This study provides an overview of consumer health information (CHI) scholarly communication trends.

OBJECTIVES

To explore CHI publications trends, journal citations, prolific authors, countries of origin, and distribution of CHI publications.

METHODS

A bibliometric analysis was used; 8953 records from the Web of Science (WoS) and peer-reviewed journal articles from databases, including LISA, MEDLINE, ERIC, PREMEDLINE and EMBASE were analysed.

RESULTS

Publications on CHI rapidly increased from 1980 to 2019, especially during the 1990s and 2000s. Most journals that have published CHI research are based in North America and Europe.

CONCLUSION

The increase in the CHI literature corresponds with that in Internet usage in the 2000s, and explains the availability of CHI content online. This trend is associated with the widespread adoption of personal computers (PCs) and other Internet-enabled gadgets.

Generating colloquial radiology reports with large language models

OBJECTIVES

Patients are increasingly being given direct access to their medical records. However, radiology reports are written for clinicians and typically contain medical jargon, which can be confusing. One solution is for radiologists to provide a "colloquial" version that is accessible to the layperson. Because manually generating these colloquial translations would represent a significant burden for radiologists, a way to automatically produce accurate, accessible patient-facing reports is desired. We propose a novel method to produce colloquial translations of radiology reports by providing specialized prompts to a large language model (LLM).

MATERIALS AND METHODS

Our method automatically extracts and defines medical terms and includes their definitions in the LLM prompt. Using our method and a naive strategy, translations were generated at 4 different reading levels for 100 de-identified neuroradiology reports from an academic medical center. Translations were evaluated by a panel of radiologists for accuracy, likability, harm potential, and readability.

RESULTS

Our approach translated the Findings and Impression sections at the 8th-grade level with accuracies of 88% and 93%, respectively. Across all grade levels, our approach was 20% more accurate than the baseline method. Overall, translations were more readable than the original reports, as evaluated using standard readability indices.

CONCLUSION

We find that our translations at the eighth-grade level strike an optimal balance between accuracy and readability. Notably, this corresponds to nationally recognized recommendations for patient-facing health communication. We believe that using this approach to draft patient-accessible reports will benefit patients without significantly increasing the burden on radiologists.

ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports

OBJECTIVES

To assess the quality of simplified radiology reports generated with the large language model (LLM) ChatGPT and to discuss challenges and chances of ChatGPT-like LLMs for medical text simplification.

METHODS

In this exploratory case study, a radiologist created three fictitious radiology reports which we simplified by prompting ChatGPT with "Explain this medical report to a child using simple language." In a questionnaire, we tasked 15 radiologists to rate the quality of the simplified radiology reports with respect to their factual correctness, completeness, and potential harm for patients. We used Likert scale analysis and inductive free-text categorization to assess the quality of the simplified reports.

RESULTS

Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed relevant medical information, and potentially harmful passages were reported.

CONCLUSION

While we see a need for further adaption to the medical field, the initial insights of this study indicate a tremendous potential in using LLMs like ChatGPT to improve patient-centered care in radiology and other medical domains.

CLINICAL RELEVANCE STATEMENT

Patients have started to use ChatGPT to simplify and explain their medical reports, which is expected to affect patient-doctor interaction. This phenomenon raises several opportunities and challenges for clinical routine.

KEY POINTS

• Patients have started to use ChatGPT to simplify their medical reports, but their quality was unknown. • In a questionnaire, most participating radiologists overall asserted good quality to radiology reports simplified with ChatGPT. However, they also highlighted a notable presence of errors, potentially leading patients to draw harmful conclusions. • Large language models such as ChatGPT have vast potential to enhance patient-centered care in radiology and other medical domains. To realize this potential while minimizing harm, they need supervision by medical experts and adaption to the medical field.

Exploring therapeutic communication in managing chronic non-communicable diseases: a mixed-method study in Ghana

BACKGROUND

Globally, the burden of chronic non-communicable diseases is increasing rapidly and approximately one in three of all adults suffer from multiple chronic conditions. Therapeutic communication plays a crucial role in achieving curative, preventive, and promotive goals regarding chronic disease management. We examined therapeutic communication between health professionals and patients with chronic non-communicable diseases at the Ho Teaching Hospital.

METHODS

We adopted a concurrent mixed-methods approach. The quantitative aspect of the study was descriptive while the qualitative was explanatory. The quantitative study was conducted among 250 patients. The qualitative data was collected among eight health professionals. A stratified sampling and simple random sampling methods were used to recruit patients for the quantitative survey while purposive and convenient sampling was used for the qualitative aspect of the study. The quantitative data was collected using a semi-structured questionnaire while the qualitative data was collected using an in-depth interview guide. The quantitative data were analyzed using STATA v17 and the qualitative data were analyzed thematically using Atlas ti. The major themes that emerged were, therapeutic communication practices, barriers to therapeutic communication and possible solutions to good therapeutic communication.

RESULTS

We found that 37% patients were 60 years and above with 53.2% being females. It was noted that 36.4% of patients have had tertiary-level education. We found that 59.2% of patients reported having good therapeutic communication with health professionals. We, however, noted that male participants were 92% less likely to practice good therapeutic communication compared with females (aOR = 0.92,95% Cl = 0.46-1.84). Health professionals' activities to ensure good therapeutic practices included their capacity to listen, build rapport with their patients, and clarify information. We found that the major facilitators of good therapeutic communication included trust in the health professionals (90.4%), conducive environment (93.2%), using simple and plain language by health professionals (92.0%) We found that there are myriad of barriers that impede communication process. This included language, health professionals' inability to break terminologies, and the unconscious state of patients.

CONCLUSIONS

The study revealed that there was good therapeutic communication between health professionals and patients with CNCDs. Nevertheless, it was also identified that ineffective therapeutic communication between health professionals and their patients due to barriers like language could lead to dissatisfaction with care, misdiagnosis, and noncompliance to treatment regimen. For Ghana as a country to achieve SDG target 3.4 by reducing mortality due to NCDs and improve wellbeing of patients by 2030, it will be imperative on Ghana Health Service to design communication strategy training for health professionals that could help improve therapeutic communication between patients and health professionals.

Retrieval augmentation of large language models for lay language generation

The complex linguistic structures and specialized terminology of expert-authored content limit the accessibility of biomedical literature to the general public. Automated methods have the potential to render this literature more interpretable to readers with different educational backgrounds. Prior work has framed such lay language generation as a summarization or simplification task. However, adapting biomedical text for the lay public includes the additional and distinct task of background explanation: adding external content in the form of definitions, motivation, or examples to enhance comprehensibility. This task is especially challenging because the source document may not include the required background knowledge. Furthermore, background explanation capabilities have yet to be formally evaluated, and little is known about how best to enhance them. To address this problem, we introduce Retrieval-Augmented Lay Language (RALL) generation, which intuitively fits the need for external knowledge beyond that in expert-authored source documents. In addition, we introduce CELLS, the largest (63k pairs) and broadest-ranging (12 journals) parallel corpus for lay language generation. To evaluate RALL, we augmented state-of-the-art text generation models with information retrieval of either term definitions from the UMLS and Wikipedia, or embeddings of explanations from Wikipedia documents. Of these, embedding-based RALL models improved summary quality and simplicity while maintaining factual correctness, suggesting that Wikipedia is a helpful source for background explanation in this context. We also evaluated the ability of both an open-source Large Language Model (Llama 2) and a closed-source Large Language Model (GPT-4) in background explanation, with and without retrieval augmentation. Results indicate that these LLMs can generate simplified content, but that the summary quality is not ideal. Taken together, this work presents the first comprehensive study of background explanation for lay language generation, paving the path for disseminating scientific knowledge to a broader audience. Our code and data are publicly available at: https://github.com/LinguisticAnomalies/pls_retrieval.

Natural language processing in radiology: Clinical applications and future directions

Natural language processing (NLP) is a wide range of techniques that allows computers to interact with human text. Applications of NLP in everyday life include language translation aids, chat bots, and text prediction. It has been increasingly utilized in the medical field with increased reliance on electronic health records. As findings in radiology are primarily communicated via text, the field is particularly suited to benefit from NLP based applications. Furthermore, rapidly increasing imaging volume will continue to increase burden on clinicians, emphasizing the need for improvements in workflow. In this article, we highlight the numerous non-clinical, provider focused, and patient focused applications of NLP in radiology. We also comment on challenges associated with development and incorporation of NLP based applications in radiology as well as potential future directions.

Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model

Radiology reports contain a diverse and rich set of clinical abnormalities documented by radiologists during their interpretation of the images. Comprehensive semantic representations of radiological findings would enable a wide range of secondary use applications to support diagnosis, triage, outcomes prediction, and clinical research. In this paper, we present a new corpus of radiology reports annotated with clinical findings. Our annotation schema captures detailed representations of pathologic findings that are observable on imaging ("lesions") and other types of clinical problems ("medical problems"). The schema used an event-based representation to capture fine-grained details, including assertion, anatomy, characteristics, size, and count. Our gold standard corpus contained a total of 500 annotated computed tomography (CT) reports. We extracted triggers and argument entities using two state-of-the-art deep learning architectures, including BERT. We then predicted the linkages between trigger and argument entities (referred to as argument roles) using a BERT-based relation extraction model. We achieved the best extraction performance using a BERT model pre-trained on 3 million radiology reports from our institution: 90.9-93.4% F1 for finding triggers and 72.0-85.6% F1 for argument roles. To assess model generalizability, we used an external validation set randomly sampled from the MIMIC Chest X-ray (MIMIC-CXR) database. The extraction performance on this validation set was 95.6% for finding triggers and 79.1-89.7% for argument roles, demonstrating that the model generalized well to the cross-institutional data with a different imaging modality. We extracted the finding events from all the radiology reports in the MIMIC-CXR database and provided the extractions to the research community.

A survey of automated methods for biomedical text simplification

OBJECTIVE

Plain language in medicine has long been advocated as a way to improve patient understanding and engagement. As the field of Natural Language Processing has progressed, increasingly sophisticated methods have been explored for the automatic simplification of existing biomedical text for consumers. We survey the literature in this area with the goals of characterizing approaches and applications, summarizing existing resources, and identifying remaining challenges.

MATERIALS AND METHODS

We search English language literature using lists of synonyms for both the task (eg, "text simplification") and the domain (eg, "biomedical"), and searching for all pairs of these synonyms using Google Scholar, Semantic Scholar, PubMed, ACL Anthology, and DBLP. We expand search terms based on results and further include any pertinent papers not in the search results but cited by those that are.

RESULTS

We find 45 papers that we deem relevant to the automatic simplification of biomedical text, with data spanning 7 natural languages. Of these (nonexclusively), 32 describe tools or methods, 13 present data sets or resources, and 9 describe impacts on human comprehension. Of the tools or methods, 22 are chiefly procedural and 10 are chiefly neural.

CONCLUSIONS

Though neural methods hold promise for this task, scarcity of parallel data has led to continued development of procedural methods. Various low-resource mitigations have been proposed to advance neural methods, including paragraph-level and unsupervised models and augmentation of neural models with procedural elements drawing from knowledge bases. However, high-quality parallel data will likely be crucial for developing fully automated biomedical text simplification.

Communicating with patients in the age of online portals-challenges and opportunities on the horizon for radiologists

The deployment of electronic patient portals increasingly allows patients throughout Europe to consult and share their radiology reports and images securely and timely online. Technical solutions and rules for releasing reports and images on patient portals may differ among institutions, regions and countries, and radiologists should therefore be familiar with the criteria by which reports and images are made available to their patients. Radiologists may also be solicited by patients who wish to discuss complex or critical imaging findings directly with the imaging expert who is responsible for the diagnosis. This emphasises the importance of radiologists’ communication skills as well as appropriate and efficient communication pathways and methods including electronic tools. Radiologists may also have to think about adapting reports as their final product in order to enable both referrers and patients to understand imaging findings. Actionable reports for a medical audience require structured, organ-specific terms and quantitative information, whereas patient-friendly summaries should preferably be based on consumer health language and include explanatory multimedia support or hyperlinks. Owing to the cultural and linguistic diversity in Europe dedicated solutions will require close collaboration between radiologists, patient representatives and software developers; software tools using artificial intelligence and natural language processing could potentially be useful in this context. By engaging actively in the challenges that are associated with increased communication with their patients, radiologists will not only have the opportunity to contribute to patient-centred care, but also to enhance the clinical relevance and the visibility of their profession.

The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis

BACKGROUND

The Unified Medical Language System (UMLS) has been a critical tool in biomedical and health informatics, and the year 2021 marks its 30th anniversary. The UMLS brings together many broadly used vocabularies and standards in the biomedical field to facilitate interoperability among different computer systems and applications.

OBJECTIVE

Despite its longevity, there is no comprehensive publication analysis of the use of the UMLS. Thus, this review and analysis is conducted to provide an overview of the UMLS and its use in English-language peer-reviewed publications, with the objective of providing a comprehensive understanding of how the UMLS has been used in English-language peer-reviewed publications over the last 30 years.

METHODS

PubMed, ACM Digital Library, and the Nursing & Allied Health Database were used to search for studies. The primary search strategy was as follows: UMLS was used as a Medical Subject Headings term or a keyword or appeared in the title or abstract. Only English-language publications were considered. The publications were screened first, then coded and categorized iteratively, following the grounded theory. The review process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

RESULTS

A total of 943 publications were included in the final analysis. Moreover, 32 publications were categorized into 2 categories; hence the total number of publications before duplicates are removed is 975. After analysis and categorization of the publications, UMLS was found to be used in the following emerging themes or areas (the number of publications and their respective percentages are given in parentheses): natural language processing (230/975, 23.6%), information retrieval (125/975, 12.8%), terminology study (90/975, 9.2%), ontology and modeling (80/975, 8.2%), medical subdomains (76/975, 7.8%), other language studies (53/975, 5.4%), artificial intelligence tools and applications (46/975, 4.7%), patient care (35/975, 3.6%), data mining and knowledge discovery (25/975, 2.6%), medical education (20/975, 2.1%), degree-related theses (13/975, 1.3%), digital library (5/975, 0.5%), and the UMLS itself (150/975, 15.4%), as well as the UMLS for other purposes (27/975, 2.8%).

CONCLUSIONS

The UMLS has been used successfully in patient care, medical education, digital libraries, and software development, as originally planned, as well as in degree-related theses, the building of artificial intelligence tools, data mining and knowledge discovery, foundational work in methodology, and middle layers that may lead to advanced products. Natural language processing, the UMLS itself, and information retrieval are the 3 most common themes that emerged among the included publications. The results, although largely related to academia, demonstrate that UMLS achieves its intended uses successfully, in addition to achieving uses broadly beyond its original intentions.

A systematic review of natural language processing applied to radiology reports

BACKGROUND

Natural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses and quantifies recent literature in NLP applied to radiology reports.

METHODS

We conduct an automated literature search yielding 4836 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics.

RESULTS

We present a comprehensive analysis of the 164 publications retrieved with publications in 2019 almost triple those in 2015. Each publication is categorised into one of 6 clinical application categories. Deep learning use increases in the period but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results.

CONCLUSIONS

Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process and we show that research in this field continues to grow. Reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers in the field providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication.

Human versus machine editing of electronic prescription directions

BACKGROUND

Pharmacy staff are responsible for editing poor-quality and difficult-to-read electronic prescription (e-prescription) directions. Machine translation (MT) models are capable of translating free text from 1 sequence into another. However, the quality of MTs of e-prescriptions into pharmacy label directions is unknown.

OBJECTIVE

To determine the types and frequencies of e-prescription direction component errors made by an MT model, pharmacy staff, and prescribers.

METHODS

A prospective evaluation was conducted on a random sample of 300 patient directions in a test set of e-prescriptions from a mail-order pharmacy. Each row included directions produced by (1) prescribers on e-prescriptions, (2) pharmacy staff on prescription labels, and (3) an open neural MT model. Annotators labeled direction sets for missing direction components, use of abbreviations and medical jargon, and incorrect information (e.g., changing the number of tablets to be taken). The longest common subsequence (LCS) compared the amount of pharmacy staff editing with and without MT.

RESULTS

Out of 279 direction sets labeled, the MT model directions contained no quality issues in 196 (70.3%) samples compared with 187 (67.0%) and 83 (29.8%) samples for pharmacy staff directions and prescriber directions, respectively. The MT model directions contained more incorrect components (n = 23). Median LCS was greater without MT (30.0 vs. 18.5, P < 0.01, Wilcoxon signed-rank test), indicating more editing was needed.

CONCLUSION

MT could be used to improve the quality of e-prescription directions; however, MT makes high-risk mistakes such as incorrectly predicting the tapering regimen for prednisone. The use of semiautomated MT, where pharmacy staff can review model predictions to detect and resolve quality issues, should be considered to improve safety and decrease total work time compared with current practice. MT has strengths and weaknesses for improving the editing process of the patient directions compared with pharmacy staff alone.

Why do Thoracic Radiologists Need to Know About Cultural Competence (and What Is it Anyway)?

Thoracic radiologists meet patients when performing procedures such as transthoracic computed tomography-guided biopsy and during shared decision-making required for lung cancer screening. Both patients and thoracic radiologists are influenced by their cultures, which affect their health care interactions. While culture may relate to religion or ethnicity of individuals, it also includes multiple additional characteristics such as gender, socioeconomic status, sexual orientation, education, occupation, age, disability, and more. Patients from different cultures undergo similar medical procedures; however, care must be tailored according to the cultural and linguistic needs of each patient. Cultural competence allows all patients, from the same or different culture as the thoracic radiologist, to receive care that is tailored to the patient's cultural and linguistic needs. Cultural competence includes concepts such as cultural awareness, linguistic competence, and health literacy as well as avoiding bias and stereotyping. Culturally competent care requires appropriate services for interpretation, relating to spoken language, and translation, related to written reports and documents. The implications of not providing adequate interpretation and translation services include the inability to take an accurate health history and patient inability to understand the informed consent forms. Thoracic radiologic services should have culturally competent practices in place at every step of the care, starting from the first phone call when patients are making an appointment. This will allow patients to receive care that is culturally and linguistically appropriate and lead to better satisfaction and outcomes.

Artificial Intelligence Solutions for Analysis of X-ray Images

Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient care and population health. The potential opportunity of AI to aid in triage and interpretation of conventional radiographs (X-ray images) is particularly significant, as radiographs are the most common imaging examinations performed in most radiology departments. Substantial progress has been made in the past few years in the development of AI algorithms for analysis of chest and musculoskeletal (MSK) radiographs, with deep learning now the dominant approach for image analysis. Large public and proprietary image data sets have been compiled and have aided the development of AI algorithms for analysis of radiographs, many of which demonstrate accuracy equivalent to radiologists for specific, focused tasks. This article describes (1) the basis for the development of AI solutions for radiograph analysis, (2) current AI solutions to aid in the triage and interpretation of chest radiographs and MSK radiographs, (3) opportunities for AI to aid in noninterpretive tasks related to radiographs, and (4) considerations for radiology practices selecting AI solutions for radiograph analysis and integrating them into existing IT systems. Although comprehensive AI solutions across modalities have yet to be developed, institutions can begin to select and integrate focused solutions which increase efficiency, increase quality and patient safety, and add value for their patients.

Full Radiology Report through Patient Web Portal: A Literature Review

The aim of this study discusses the gap between the patient web portal and providing a full radiology report. A literature review was conducted to examine radiologists, physicians, and patients’ opinions and preferences of providing patients with online access radiology reports. The databases searched were Pubmed and Google Scholar and the initial search included 927 studies. After review, 47 studies were included in the study. We identified several themes, including patients’ understanding of radiology reports and radiological images, as well as the need for decreasing the turnaround time for reports availability. The existing radiology reports written for physicians are not suited for patients. Further studies are needed to guide and inform the design of patient friendly radiology reports. One of the ways that can be used to fill the gap between patients and radiology reports is using social media sites.

Factors Influencing Motivation and Engagement in Mobile Health Among Patients With Sickle Cell Disease in Low-Prevalence, High-Income Countries: Qualitative Exploration of Patient Requirements

Background

Sickle cell disease (SCD) is a hematological genetic disease affecting over 25 million people worldwide. The main clinical manifestations of SCD, hemolytic anemia and vaso-occlusion, lead to chronic pain and organ damages. With recent advances in childhood care, high-income countries have seen SCD drift from a disease of early childhood mortality to a neglected chronic disease of adulthood. In particular, coordinated, preventive, and comprehensive care for adults with SCD is largely underresourced. Consequently, patients are left to self-manage. Mobile health (mHealth) apps for chronic disease self-management are now flooding app stores. However, evidence remains unclear about their effectiveness, and the literature indicates low user engagement and poor adoption rates. Finally, few apps have been developed for people with SCD and none encompasses their numerous and complex self-care management needs.

Objective

This study aimed to identify factors that may influence the long-term engagement and user adoption of mHealth among the particularly isolated community of adult patients with SCD living in low-prevalence, high-income countries.

Methods

Semistructured interviews were conducted. Interviews were audiotaped, transcribed verbatim, and analyzed using thematic analysis. Analysis was informed by the Braun and Clarke framework and mapped to the COM-B model (capability, opportunity, motivation, and behavior). Results were classified into high-level functional requirements (FRs) and nonfunctional requirements (NFRs) to guide the development of future mHealth interventions.

Results

Overall, 6 males and 4 females were interviewed (aged between 21 and 55 years). Thirty FRs and 31 NFRs were extracted from the analysis. Most participants (8/10) were concerned about increasing their physical capabilities being able to stop pain symptoms quickly. Regarding the psychological capability aspects, all interviewees desired to receive trustworthy feedback on their self-care management practices. About their physical opportunities, most (7/10) expressed a strong desire to receive alerts when they would reach their own physiological limitations (ie, during physical activity). Concerning social opportunity, most (9/10) reported wanting to learn about the self-care practices of other patients. Relating to motivational aspects, many interviewees (6/10) stressed their need to learn how to avoid the symptoms and live as normal a life as possible. Finally, NFRs included inconspicuousness and customizability of user experience, automatic data collection, data shareability, and data privacy.

Conclusions

Our findings suggest that motivation and engagement with mHealth technologies among the studied population could be increased by providing features that clearly benefit them. Self-management support and self-care decision aid are patients’ major demands. As the complexity of SCD self-management requires a high cognitive load, pervasive health technologies such as wearable sensors, implantable devices, or inconspicuous conversational user interfaces should be explored to ease it. Some of the required technologies already exist but must be integrated, bundled, adapted, or improved to meet the specific needs of people with SCD.

Patient-centered web-based information on oral lichen planus: Quality and readability

Background

To assess the readability and quality of web-based information available for patients about oral lichen planus (OLP).

Material and Methods

Three major search engines (Google, Bing and Yahoo!) were used to identify websites of particular interest to the study using the search term ‘oral lichen planus’. The first 100 sites of each search engine were considered for the study. The quality of the contents was evaluated using the DISCERN instrument. The Flesch-Kinkaid Reading Grade Level (FKRGL) and the Flesh Reading Ease Score (FRES) were used to assess readability. The presence of the Health on the Net (HON) seal was also evaluated.

Results

Following the application of the study’s exclusion criteria, only 28 websites were compiled for further analysis. The median of the DISCERN instrument was 3 [2-3] which means serious or potentially important shortcoming in the quality of the information. Readability indexes pointed to a high reading difficulty (FRES: 48.14±11.22; FKRGL:11.13±2.90).

Conclusions

The information provided by the Internet to the general public regarding OLP has major deficits in terms of quality, and at the same time is difficult for a comprehensive reading. Further studies are warranted to test well-produced patient-centered information on OLP.

Key words:Oral lichen planus, oral diseases, health literacy, health information.

Mining and standardizing chinese consumer health terms

Background

Health professionals and consumers use different terms to express medical events or concerns, which makes the communication barriers between the professionals and consumers. This may lead to bias in the diagnosis or treatment due to the misunderstanding or incomplete understanding. To solve the issue, a consumer health vocabulary was developed to map the consumer-used health terms to professional-used medical terms.

Methods

In this study, we extracted Chinese consumer health terms from both online health forum and patient education monographs, and manually mapped them to medical terms used by professionals (terms in medical thesauri or in medical books). To ensure the above annotation quality, we developed annotation guidelines.

Results

We applied our method to extract consumer-used disease terms in endocrinology, cardiology, gastroenterology and dermatology. In this study, we identified 1349 medical mentions from 8436 questions posted in an online health forum and 1428 articles for patient education monographs. After manual annotation and review, we released 1036 Chinese consumer health terms with mapping to 480 medical terms. Four annotators worked on the manual annotation work following the Chinese consumer health term annotation guidelines. Their average inter-annotator agreement (IAA) score was 93.91% ensuring high consistency of the released terms.

Conclusions

We extracted Chinese consumer health terms from online forum and patient education monographs, and mapped them to medical terms used by professionals. Manual annotation efforts have been made for term annotating and mapping. Our study may contribute to the Chinese consumer health vocabulary construction. In addition, our annotated corpus, both the contexts of consumer health terms and consumer-professional term mapping, would be a useful resource for automatic methodology development. The dataset of the Chinese consumer health terms (CHT) is publicly available at http://www.phoc.org.cn/cht/.

Improving Consumer Understanding of Medical Text: Development and Validation of a New SubSimplify Algorithm to Automatically Generate Term Explanations in English and Spanish

Background

While health literacy is important for people to maintain good health and manage diseases, medical educational texts are often written beyond the reading level of the average individual. To mitigate this disconnect, text simplification research provides methods to increase readability and, therefore, comprehension. One method of text simplification is to isolate particularly difficult terms within a document and replace them with easier synonyms (lexical simplification) or an explanation in plain language (semantic simplification). Unfortunately, existing dictionaries are seldom complete, and consequently, resources for many difficult terms are unavailable. This is the case for English and Spanish resources.

Objective

Our objective was to automatically generate explanations for difficult terms in both English and Spanish when they are not covered by existing resources. The system we present combines existing resources for explanation generation using a novel algorithm (SubSimplify) to create additional explanations.

Methods

SubSimplify uses word-level parsing techniques and specialized medical affix dictionaries to identify the morphological units of a term and then source their definitions. While the underlying resources are different, SubSimplify applies the same principles in both languages. To evaluate our approach, we used term familiarity to identify difficult terms in English and Spanish and then generated explanations for them. For each language, we extracted 400 difficult terms from two different article types (General and Medical topics) balanced for frequency. For English terms, we compared SubSimplify’s explanation with the explanations from the Consumer Health Vocabulary, WordNet Synonyms and Summaries, as well as Word Embedding Vector (WEV) synonyms. For Spanish terms, we compared the explanation to WordNet Summaries and WEV Embedding synonyms. We evaluated quality, coverage, and usefulness for the simplification provided for each term. Quality is the average score from two subject experts on a 1-4 Likert scale (two per language) for the synonyms or explanations provided by the source. Coverage is the number of terms for which a source could provide an explanation. Usefulness is the same expert score, however, with a 0 assigned when no explanations or synonyms were available for a term.

Results

SubSimplify resulted in quality scores of 1.64 for English (P<.001) and 1.49 for Spanish (P<.001), which were lower than those of existing resources (Consumer Health Vocabulary [CHV]=2.81). However, in coverage, SubSimplify outperforms all existing written resources, increasing the coverage from 53.0% to 80.5% in English and from 20.8% to 90.8% in Spanish (P<.001). This result means that the usefulness score of SubSimplify (1.32; P<.001) is greater than that of most existing resources (eg, CHV=0.169).

Conclusions

Our approach is intended as an additional resource to existing, manually created resources. It greatly increases the number of difficult terms for which an easier alternative can be made available, resulting in greater actual usefulness.