AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献

This scoping review evaluated the efficacy and potential of web-based interventions for substance use disorders and mental health conditions. The studies comprise randomized controlled trials, pilot trials, and effectiveness trials. Web-based interventions consistently demonstrated significant reductions in substance use, improvements in mental health outcomes (e.g., PTSD, depression, anxiety), and enhancements in emotion regulation, help-seeking, and quality of life. Several studies found web-based interventions to be non-inferior or superior to traditional face-to-face treatments. Despite limitations in the current evidence base, such as methodological issues and lack of long-term follow-up, the findings highlight the promise of web-based interventions in expanding access to evidence-based care, particularly for underserved populations. Future research should focus on refining interventions, exploring novel technologies, and evaluating long-term effectiveness and cost-effectiveness. The integration of web-based interventions into healthcare systems has the potential to significantly impact public health by increasing treatment accessibility and improving outcomes for individuals with substance use disorders and mental health conditions.

This study examines the integration of OpenEMR, a Meaningful Use-certified open-source electronic health record (EHR) system, into a Health Informatics curriculum. The primary objective was to address the disparity between theoretical knowledge and practical application in health informatics education. The implementation process revealed several significant challenges, including unintended system modifications that compromised functionality, data entry errors that impacted usability, and technical issues that impeded accessibility. To mitigate these challenges, a series of interventions were implemented. These included backend modifications to enhance data entry accuracy, usability improvements such as limiting open tabs to facilitate navigation, and the implementation ofproactive measures to expedite the resolution of technical issues. The experiences gained from this integration process highlight three critical aspects of health informatics education: the significance of practical proficiency in EHR systems, the necessity for user-centric interface design, and the importance of adaptability and problem-solving skills. The study proposes several future directions for research and practice. These include fostering global collaboration, developing standardized curricula for EHR education, and establishing robust mechanisms for continuous assessment and improvement. The findings underscore the pivotal role of integrating hands-on EHR experience into health informatics education, emphasizing its potential to equip students with the essential competencies required to navigate the complex and dynamic healthcare landscape.

Clinical question answering systems have the potential to provide clinicians with relevant and timely answers to their questions. Nonetheless, despite the advances that have been made, adoption of these systems in clinical settings has been slow. One issue is a lack of question-answering datasets which reflect the real-world needs of health professionals. In this work, we present RealMedQA, a dataset of realistic clinical questions generated by humans and an LLM. We describe the process for generating and verifying the QA pairs and assess several QA models on BioASQ and RealMedQA to assess the relative difficulty of matching answers to questions. We show that the LLM is more cost-efficient for generating "ideal" QA pairs. Additionally, we achieve a lower lexical similarity between questions and answers than BioASQ which provides an additional challenge to the top two QA models, as per the results. We release our code and our dataset publicly to encourage further research.

This study explores the potential of utilizing administrative claims data, combined with advanced machine learning and deep learning techniques, to predict the progression of Chronic Kidney Disease (CKD) to End-Stage Renal Disease (ESRD). We analyze a comprehensive, 10-year dataset provided by a major health insurance organization to develop prediction models for multiple observation windows using traditional machine learning methods such as Random Forest and XGBoost as well as deep learning approaches such as Long Short-Term Memory (LSTM) networks. Our findings demonstrate that the LSTM model, particularly with a 24-month observation window, exhibits superior performance in predicting ESRD progression, outperforming existing models in the literature. We further apply SHap-ley Additive exPlanations (SHAP) analysis to enhance interpretability, providing insights into the impact of individual features on predictions at the individual patient level. This study underscores the value of leveraging administrative claims data for CKD management and predicting ESRD progression.

In the evolving landscape of clinical Natural Language Generation (NLG), assessing abstractive text quality remains challenging, as existing methods often overlook generative task complexities. This work aimed to examine the current state of automated evaluation metrics in NLG in healthcare. To have a robust and well-validated baseline with which to examine the alignment of these metrics, we created a comprehensive human evaluation framework. Employing ChatGPT-3.5-turbo generative output, we correlated human judgments with each metric. None of the metrics demonstrated high alignment; however, the SapBERT score-a Unified Medical Language System (UMLS)- showed the best results. This underscores the importance of incorporating domain-specific knowledge into evaluation efforts. Our work reveals the deficiency in quality evaluations for generated text and introduces our comprehensive human evaluation framework as a baseline. Future efforts should prioritize integrating medical knowledge databases to enhance the alignment of automated metrics, particularly focusing on refining the SapBERT score for improved assessments.

A critical strategy in limiting breast cancer (BC) mortality is the early identification of high-risk patients and implementation of risk-reducing measures. RealRisks, an online decision aid constructed by our team to provide education on BC risk and personalized risk assessment, allows users the option to connect to their electronic health record (EHR) to extract requisite data to calculate BC risk via Fast Healthcare Interoperability Resources (FHIR). Using data from RealRisks user profiles, baseline questionnaires, and interview transcripts, we sought to understand the differences between the groups of patients who opted to download their data via the EHR vs. those who did not. A higher percentage of those who downloaded data (53.8% vs. 42.3%) identified as Hispanic/Latino compared to those who did not download. Thematic analysis suggested that while data security and privacy concerns may lead to hesitation, it is perhaps technological barriers that most significantly limit EHR download.

Large Language Models (LLMs) have revolutionized various sectors, including healthcare where they are employed in diverse applications. Their utility is particularly significant in the context of rare diseases, where data scarcity, complexity, and specificity pose considerable challenges. In the clinical domain, Named Entity Recognition (NER) stands out as an essential task and it plays a crucial role in extracting relevant information from clinical texts. Despite the promise of LLMs, current research mostly concentrates on document-level NER, identifying entities in a more general context across entire documents, without extracting their precise location. Additionally, efforts have been directed towards adapting ChatGPTfor token-level NER. However, there is a significant research gap when it comes to employing token-level NER for clinical texts, especially with the use of local open-source LLMs. This study aims to bridge this gap by investigating the effectiveness of both proprietary and local LLMs in token-level clinical NER. Essentially, we delve into the capabilities of these models through a series of experiments involving zero-shot prompting, few-shot prompting, retrieval-augmented generation (RAG), and instruction-fine-tuning. Our exploration reveals the inherent challenges LLMs face in token-level NER, particularly in the context of rare diseases, and suggests possible improvements for their application in healthcare. This research contributes to narrowing a significant gap in healthcare informatics and offers insights that could lead to a more refined application of LLMs in the healthcare sector.

The use of artificial intelligence (AI) in medicine has shown promise to improve the quality of healthcare decisions. However, AI can be biased in a manner that produces unfair predictions for certain demographic subgroups. In MIMIC-CXR, a publicly available dataset of over 300,000 chest X-ray images, diagnostic AI has been shown to have a higher false negative rate for racial minorities. We evaluated the capacity of synthetic data augmentation, oversampling, and demographic-based corrections to enhance the fairness of AI predictions. We show that adjusting unfair predictions for demographic attributes, such as race, is ineffective at improving fairness or predictive performance. However, using oversampling and synthetic data augmentation to modify disease prevalence reduced such disparities by 74.7% and 10.6%, respectively. Moreover, such fairness gains were accomplished without reduction in performance (95% CI AUC: [0.816, 0.820] versus [0.810, 0.819] versus [0.817, 0.821] for baseline, oversampling, and augmentation, respectively).

Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving cancer treatment outcomes and safety. This study examined machine learning (ML) models to identify cancer patients at risk of HF using electronic health records (EHRs), including traditional ML, Time-Aware long short-term memory (T-LSTM), and large language models (LLMs) using novel narrative features derived from the structured medical codes. We identified a cancer cohort of 12,806 patients from the University of Florida Health, diagnosed with lung, breast, and colorectal cancers, among which 1,602 individuals developed HF after cancer. The LLM, GatorTron-3.9B, achieved the best F1 scores, outperforming the traditional support vector machines by 39%, the T-LSTM deep learning model by 7%, and a widely used transformer model, BERT, by 5.6%. The analysis shows that the proposed narrative features remarkably increased feature density and improved performance.

As part of a randomized controlled trial on the use of pictographs (images used in lieu of a patient photo) embedded in the electronic health record to reduce wrong-patient errors in the neonatal intensive care unit (NICU), we conducted a series of surveys of parents, providers and nurses in the NICU. Data from survey responses were thematically analyzed and categorized. We found that in all groups, there was very high awareness of the intended purpose of the pictographs; however, the perception of providers and nurses about the effectiveness of pictographs was not as strong. While several providers and nurses acknowledged that pictographs can or have helped them avoid wrong-patient errors when caring for multiple birth infants (such as twins), many nurses believed that their current practice of the use of two patient identifiers was sufficient, and pictographs were not useful. Parents reported that pictographs improved their experience of care.