Current approaches to identify sections within clinical narratives from electronic health records: a systematic review

BACKGROUND

The identification of sections in narrative content of Electronic Health Records (EHR) has demonstrated to improve the performance of clinical extraction tasks; however, there is not yet a shared understanding of the concept and its existing methods. The objective is to report the results of a systematic review concerning approaches aimed at identifying sections in narrative content of EHR, using both automatic or semi-automatic methods.

METHODS

This review includes articles from the databases: SCOPUS, Web of Science and PubMed (from January 1994 to September 2018). The selection of studies was done using predefined eligibility criteria and applying the PRISMA recommendations. Search criteria were elaborated by using an iterative and collaborative keyword enrichment.

RESULTS

Following the eligibility criteria, 39 studies were selected for analysis. The section identification approaches proposed by these studies vary greatly depending on the kind of narrative, the type of section, and the application. We observed that 57% of them proposed formal methods for identifying sections and 43% adapted a previously created method. Seventy-eight percent were intended for English texts and 41% for discharge summaries. Studies that are able to identify explicit (with headings) and implicit sections correspond to 46%. Regarding the level of granularity, 54% of the studies are able to identify sections, but not subsections. From the technical point of view, the methods can be classified into rule-based methods (59%), machine learning methods (22%) and a combination of both (19%). Hybrid methods showed better results than those relying on pure machine learning approaches, but lower than rule-based methods; however, their scope was more ambitious than the latter ones. Despite all the promising performance results, very few studies reported tests under a formal setup. Almost all the studies relied on custom dictionaries; however, they used them in conjunction with a controlled terminology, most commonly the UMLSⓇ metathesaurus.

CONCLUSIONS

Identification of sections in EHR narratives is gaining popularity for improving clinical extraction projects. This study enabled the community working on clinical NLP to gain a formal analysis of this task, including the most successful ways to perform it.

Development and Evaluation of a Serious Game for Teaching ICD-10 Diagnosis Coding to Medical Students

Objective: Assessment of the pedagogical effect and technological acceptance of the serious game, CODIFICO, which has been designed to train medical students in ICD-10 diagnosis coding. Materials and Methods: We designed the serious game, CODIFICO, as an alternative way to teach ICD-10 diagnosis coding to undergraduate medical students. To assess the pedagogical effect of the game, we used the quasiexperimental pretest-posttest design. The participants began by completing a knowledge pretest on Blackboard. After the pretest, the teacher presented the game to the students and invited them to play it for 1 week. Then, the students completed the posttest on Blackboard. We applied the Wilcoxon test to establish the difference between the pretest and posttest. We designed a questionnaire to evaluate the participants' technology acceptance toward the game. Results: Sixty-one undergraduate medical students from a large Colombian private university took part. There was no statistically significant difference between the pretest and the posttest. However, the game had some positive effects on knowledge. The game was well accepted among the participants. Conclusion: The game, CODIFICO, was useful to teach diagnosis determination, not diagnostic coding. Some of the reasons that caused this situation were insufficient attention to the pedagogical theory, excessive reliance on clinical aspects of the medical training, limited resources, and lack of experience at the medical school to design gamification strategies.

Transfer Learning for Classifying Spanish and English Text by Clinical Specialties

Transfer learning has demonstrated its potential in natural language processing tasks, where models have been pre-trained on large corpora and then tuned to specific tasks. We applied pre-trained transfer models to a Spanish biomedical document classification task. The main goal is to analyze the performance of text classification by clinical specialties using state-of-the-art language models for Spanish, and compared them with the results using corresponding models in English and with the most important pre-trained model for the biomedical domain. The outcomes present interesting perspectives on the performance of language models that are pre-trained for a particular domain. In particular, we found that BioBERT achieved better results on Spanish texts translated into English than the general domain model in Spanish and the state-of-the-art multilingual model.

Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish

BACKGROUND

Controlled vocabularies are fundamental resources for information extraction from clinical texts using natural language processing (NLP). Standard language resources available in the healthcare domain such as the UMLS metathesaurus or SNOMED CT are widely used for this purpose, but with limitations such as lexical ambiguity of clinical terms. However, most of them are unambiguous within text limited to a given clinical specialty. This is one rationale besides others to classify clinical text by the clinical specialty to which they belong.

RESULTS

This paper addresses this limitation by proposing and applying a method that automatically extracts Spanish medical terms classified and weighted per sub-domain, using Spanish MEDLINE titles and abstracts as input. The hypothesis is biomedical NLP tasks benefit from collections of domain terms that are specific to clinical subdomains. We use PubMed queries that generate sub-domain specific corpora from Spanish titles and abstracts, from which token n-grams are collected and metrics of relevance, discriminatory power, and broadness per sub-domain are computed. The generated term set, called Spanish core vocabulary about clinical specialties (SCOVACLIS), was made available to the scientific community and used in a text classification problem obtaining improvements of 6 percentage points in the F-measure compared to the baseline using Multilayer Perceptron, thus demonstrating the hypothesis that a specialized term set improves NLP tasks.

CONCLUSION

The creation and validation of SCOVACLIS support the hypothesis that specific term sets reduce the level of ambiguity when compared to a specialty-independent and broad-scope vocabulary.

Co-Designing, Developing, and Testing a Mental Health Platform for Young People Using a Participatory Design Methodology in Colombia: Mixed Methods Study

BACKGROUND

Globally, mental health (MH) problems increasingly affect young people, contributing significantly to disability and disease. In low- and middle-income countries, such as Colombia, barriers to accessing care exacerbate the treatment gap. In addition, the lack of widespread digital interventions further deepens the digital health divide between the Global North and Global South, limiting equitable access to innovative MH solutions.

OBJECTIVE

This study aims to co-design and develop an MH platform using participatory design methodologies and conduct a 15-month naturalistic observational trial to assess its feasibility among Colombian youth.

METHODS

This study used a mixed methods approach within a structured research and development cycle. To ensure a user-centered design, we began with a series of co-design workshops, where stakeholders collaboratively identified key user needs. Following this, usability testing was conducted in 2 stages, alpha and beta, using the System Usability Scale (SUS) to assess functionality and user experience. To capture real-world interactions, a naturalistic observational trial ran from July 2022 to October 2023, collecting data on user engagement and system performance. This study integrated quantitative and qualitative analyses.

RESULTS

A total of 146 individuals participated in the co-design process, with 110 (75.3%) contributing to the development of platform components and 36 (24.7%) participating in usability testing. The co-designed platform integrated several key features, including social media and advertising, an MH screening tool, registration, targeted psychoeducational resources, automated tailored recommendations, and a "track-as-you-go" feature for continuous MH monitoring. Additional elements included user-friendly follow-up graphs, telecounseling integration, customizable well-being nudges, an emergency button, and gamification components to enhance engagement. During usability testing, the beta prototype received a median SUS score of 85.0 (IQR 80-92.5), indicating high usability. In the subsequent observational trial, which ran from July 2022 to October 2023, a total of 435 users interacted with the platform-314 (72.2%) as registered users and 121 (27.8%) anonymously. Emotional distress was prevalent, with 63.7% (200/314) of the registered users and 61.2% (74/121) of the anonymous users reporting distress, as measured by the 6-item Kessler Psychological Distress Scale. Despite 102 users requesting telecounseling, only 26.5% (27/102) completed a session. While usability scores remained high, engagement challenges emerged, with only 18.8% (59/314) of the users continuing platform use beyond the first day.

CONCLUSIONS

This study explored the development and user experience of a youth MH platform in Colombia, demonstrating that a cocreation approach is both feasible and effective. By actively involving users throughout the design process, the platform achieved high usability and incorporated features that resonated with its target audience. However, sustaining long-term engagement remains a challenge, as does addressing privacy concerns, particularly for younger users. These findings highlight the importance of continuous user-centered refinement to enhance both accessibility and retention in digital MH interventions.

Celebrity profiling through linguistic analysis of digital social networks

Digital social networks have become an essential source of information because celebrities use them to share their opinions, ideas, thoughts, and feelings. This makes digital social networks one of the preferred means for celebrities to promote themselves and attract new followers. This paper proposes a model of feature selection for the classification of celebrities profiles based on their use of a digital social network Twitter. The model includes the analysis of lexical, syntactic, symbolic, participation, and complementary information features of the posts of celebrities to estimate, based on these, their demographic and influence characteristics. The classification with these new features has an F1-score of 0.65 in Fame, 0.88 in Gender, 0.37 in Birth year, and 0.57 in Occupation. With these new features, the average accuracy improve up to 0.14 more. As a result, extracted features from linguistic cues improved the performance of predictive models of Fame and Gender and facilitate explanations of the model results. Particularly, the use of the third person singular was highly predictive in the model of Fame.

Leveraging PubMed to Create a Specialty-Based Sense Inventory for Spanish Acronym Resolution

Acronyms frequently occur in clinical text, which makes their identification, disambiguation and resolution an important task in clinical natural language processing. This paper contributes to acronym resolution in Spanish through the creation of a set of sense inventories organized by clinical specialty containing acronyms, their expansions, and corpus-driven features. The new acronym resource is composed of 51 clinical specialties with 3,603 acronyms in total, from which we identified 228 language independent acronyms and 391 language dependent expansions. We further analyzed the sense inventory across specialties and present novel insights of acronym usage in biomedical Spanish texts.

Providing digital mental health support and guidance across Colombia: An observational study

Objective

Colombia's mental health system is plagued by significant shortages in services and health professionals. Digital health technologies enable access to information and care, overcoming barriers related to systemic limitations, geographic location, cost and stigma. This paper aims to characterise the sample of Colombians who sought telecounselling and support through Mentes Colectivas, a web-based mental health counselling platform.

Methods

Participants provided basic demographics and completed the Kessler 6 to track psychological distress. Counsellors collected information about participants' level of functional impairment, presenting problems, mental health warning signs and session attendance. Descriptive statistics were used to characterise the sample. A range of inferential statistics were used to analyse group differences based on age and session, explore associations within clinical presentations, examine predictors of session attendance and analyse clinical differences between episodes of care.

Results

A total of 6442 participants (mean age = 33.6 years; 78.5% female) attended an initial session, with 35.7% returning for at least one follow-up session. Participants on average reported moderate levels of psychological distress, with young people reporting significantly higher distress relative to adults and older adults. Symptoms of anxiety and depression and sleep disturbances were most common.

Conclusions

This research confirms the feasibility of Mentes Colectivas to promote help-seeking and support self-management of mental health across the lifespan in Colombia. Digital health technologies have the potential to play a vital role in increasing equity of access to care for the Colombian population, improving mental health and functioning as well as potentially strengthening the health of families and communities.