He, Zhe

Abstract Objectives To explore patients’ use of patient portals to access lab test results, their comprehension of lab test data, and factors associated with these. Materials and Methods An online survey was administered to 276 adults 18+ years. Multivariate logistic regression was used to determine factors associated with patient portal use to view lab test results and lab test comprehension. Results The sample was predominantly White (72.5%), female (55.4%), with mean age 50.7 ± 15.5 years. Overall, participants had low numeracy (10.79 ± 2.71) and eHealth literacy skills (23.91 ± 5.29), and moderate lab test comprehension scores (18 ± 2.3). White participants with greater eHealth literacy were more likely to access lab test results via patient portals, whereas those with a college education were less likely to do so (χ2 [3]=31.23, P < .001). The regression showed that older age, fewer chronic conditions, and use of patient portals were significantly related to higher lab test comprehension scores (F [22, 250]=8.73, P <.001). Older adults performed better on comprehension tests but reported having difficulty understanding lab tests, expressing a preference for their doctors to explain them. Discussion The findings shed light on the experiences and needs of different user groups that must be addressed to enhance their ability to effectively use patient portals for obtaining lab test results. Conclusion More research is needed to determine patient barriers to comprehending lab test results online and to develop tailored strategies to improve patients’ self-efficacy to meaningfully use medical information in patient portals.

Abstract Objectives Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. Materials and Methods A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. Results Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). Discussion These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. Conclusions Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.

AbstractObjectiveThe study sought to describe the literature related to the development of methods for auditing the Unified Medical Language System (UMLS), with particular attention to identifying errors and inconsistencies of attributes of the concepts in the UMLS Metathesaurus.Materials and MethodsWe applied the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach by searching the MEDLINE database and Google Scholar for studies referencing the UMLS and any of several terms related to auditing, error detection, and quality assurance. A qualitative analysis and summarization of articles that met inclusion criteria were performed.ResultsEighty-three studies were reviewed in detail. We first categorized techniques based on various aspects including concepts, concept names, and synonymy (n = 37), semantic type assignments (n = 36), hierarchical relationships (n = 24), lateral relationships (n = 12), ontology enrichment (n = 8), and ontology alignment (n = 18). We also categorized the methods according to their level of automation (ie, automated systematic, automated heuristic, or manual) and the type of knowledge used (ie, intrinsic or extrinsic knowledge).ConclusionsThis study is a comprehensive review of the published methods for auditing the various conceptual aspects of the UMLS. Categorizing the auditing techniques according to the various aspects will enable the curators of the UMLS as well as researchers comprehensive easy access to this wealth of knowledge (eg, for auditing lateral relationships in the UMLS). We also reviewed ontology enrichment and alignment techniques due to their critical use of and impact on the UMLS.