Readability Formulas and User Perceptions of Electronic Health Records Difficulty: A Corpus Study

BACKGROUND: Electronic health records (EHRs) are a rich resource for developing applications to engage patients and foster patient activation, thus holding a strong potential to enhance patient-centered care. Studies have shown that providing patients with access to their own EHR notes may improve the understanding of their own clinical conditions and treatments, leading to improved health care outcomes. However, the highly technical language in EHR notes impedes patients\u27 comprehension. Numerous studies have evaluated the difficulty of health-related text using readability formulas such as Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning-Fog Index (GFI). They conclude that the materials are often written at a grade level higher than common recommendations. OBJECTIVE: The objective of our study was to explore the relationship between the aforementioned readability formulas and the laypeople\u27s perceived difficulty on 2 genres of text: general health information and EHR notes. We also validated the formulas\u27 appropriateness and generalizability on predicting difficulty levels of highly complex technical documents. METHODS: We collected 140 Wikipedia articles on diabetes and 242 EHR notes with diabetes International Classification of Diseases, Ninth Revision code. We recruited 15 Amazon Mechanical Turk (AMT) users to rate difficulty levels of the documents. Correlations between laypeople\u27s perceived difficulty levels and readability formula scores were measured, and their difference was tested. We also compared word usage and the impact of medical concepts of the 2 genres of text. RESULTS: The distributions of both readability formulas\u27 scores (P \u3c .001) and laypeople\u27s perceptions (P=.002) on the 2 genres were different. Correlations of readability predictions and laypeople\u27s perceptions were weak. Furthermore, despite being graded at similar levels, documents of different genres were still perceived with different difficulty (P \u3c .001). Word usage in the 2 related genres still differed significantly (P \u3c .001). CONCLUSIONS: Our findings suggested that the readability formulas\u27 predictions did not align with perceived difficulty in either text genre. The widely used readability formulas were highly correlated with each other but did not show adequate correlation with readers\u27 perceived difficulty. Therefore, they were not appropriate to assess the readability of EHR notes

Assessing the Readability of Medical Documents: A Ranking Approach

BACKGROUND: The use of electronic health record (EHR) systems with patient engagement capabilities, including viewing, downloading, and transmitting health information, has recently grown tremendously. However, using these resources to engage patients in managing their own health remains challenging due to the complex and technical nature of the EHR narratives. OBJECTIVE: Our objective was to develop a machine learning-based system to assess readability levels of complex documents such as EHR notes. METHODS: We collected difficulty ratings of EHR notes and Wikipedia articles using crowdsourcing from 90 readers. We built a supervised model to assess readability based on relative orders of text difficulty using both surface text features and word embeddings. We evaluated system performance using the Kendall coefficient of concordance against human ratings. RESULTS: Our system achieved significantly higher concordance (.734) with human annotators than did a baseline using the Flesch-Kincaid Grade Level, a widely adopted readability formula (.531). The improvement was also consistent across different disease topics. This method\u27s concordance with an individual human user\u27s ratings was also higher than the concordance between different human annotators (.658). CONCLUSIONS: We explored methods to automatically assess the readability levels of clinical narratives. Our ranking-based system using simple textual features and easy-to-learn word embeddings outperformed a widely used readability formula. Our ranking-based method can predict relative difficulties of medical documents. It is not constrained to a predefined set of readability levels, a common design in many machine learning-based systems. Furthermore, the feature set does not rely on complex processing of the documents. One potential application of our readability ranking is personalization, allowing patients to better accommodate their own background knowledge

FIELD LOADING-TEST BASED SHM SYSTEM SAFETY STANDARD DETERMINATION

Structural health monitoring (SHM) allows for real-time structural response monitoring and online data acquisition of bridge structures. This data reflects the operational and environmental conditions of the bridge, which is important in identification of possible anomalous changes. In order to effortlessly determine the safety condition of the bridge directly through the transferred data without data analysis, a five-level safety standard system will be established for real-time safety warning in this paper. The threshold of each safety levels will be determined through field loading tests results on an external prestressing rehabilitated continuous rigid frame bridge, of which permanent structural health monitoring system was instrumented. After overall evaluation, we come to the conclusion that the rehabilitation is successful and that the bridge is under safe operating condition. A novel, simplified safety standard thresholding technique is proposed based on static loading test results as well as ultimate limit state of the bridge. This technique is simple yet very practical in daily bridge monitoring

Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations

BACKGROUND: Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients\u27 notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. OBJECTIVE: We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. METHODS: First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians\u27 agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. RESULTS: Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen\u27s kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FOCUS for identifying important terms from EHR notes was 0.866 AUC-ROC. Both performance scores significantly exceeded the corresponding baseline system scores (P \u3c .001). Rich learning features contributed to FOCUS\u27s performance substantially. CONCLUSIONS: FOCUS can automatically rank terms from EHR notes based on their importance to patients. It may help develop future interventions that improve quality of care

FIELD TEST ON THE COOPERATION OF NSM STRENGTHENING AND EXTERNAL TENDON RETROFITTING TECHNIQUE

In this paper, post-tension and steel plate near-surface mounted (NSM) strengthening systems are proposed to strengthen deteriorated and cracked large box girder rigid frame bridge without altering appearance and dimension of the bridge. The reinforcement method mainly improves the bearing capacity through external prestressed tendons, and bonding steel plate can enhance the shear resistance of the bridge. The main purpose is to study the structural mechanical properties before and after the reinforcement of rigid frame bridges. Take a 540m rigid frame box girder bridge as an example. The static load test of the bridge before and after reinforcement is carried out. The deflection and strain of the middle cross section of the span are measured in the static test. A finite element analysis model was also developed and verified static loading test data. The results show that structural bearing capacity and performance of the bridge were enhanced with the post-tension and NSM strengthening systems cooperatively

Inhibition of AMPA receptor trafficking at hippocampal synapses by β-amyloid oligomers: the mitochondrial contribution

<p>Abstract</p> <p>Background</p> <p>Synaptic defects represent a major mechanism underlying altered brain functions of patients suffering Alzheimer's disease (AD) <abbrgrp><abbr bid="B1">1</abbr><abbr bid="B2">2</abbr><abbr bid="B3">3</abbr></abbrgrp>. An increasing body of work indicates that the oligomeric forms of β-amyloid (Aβ) molecules exert profound inhibition on synaptic functions and can cause a significant loss of neurotransmitter receptors from the postsynaptic surface, but the underlying mechanisms remain poorly understood. In this study, we investigated a potential contribution of mitochondria to Aβ inhibition of AMPA receptor (AMPAR) trafficking.</p> <p>Results</p> <p>We found that a brief exposure of hippocampal neurons to Aβ oligomers not only led to marked removal of AMPARs from postsynaptic surface but also impaired rapid AMPAR insertion during chemically-induced synaptic potentiation. We also found that Aβ oligomers exerted acute impairment of fast mitochondrial transport, as well as mitochondrial translocation into dendritic spines in response to repetitive membrane depolarization. Quantitative analyses at the single spine level showed a positive correlation between spine-mitochondria association and the surface accumulation of AMPARs. In particular, we found that spines associated with mitochondria tended to be more resistant to Aβ inhibition on AMPAR trafficking. Finally, we showed that inhibition of GSK3β alleviated Aβ impairment of mitochondrial transport, and effectively abolished Aβ-induced AMPAR loss and inhibition of AMPAR insertion at spines during cLTP.</p> <p>Conclusions</p> <p>Our findings indicate that mitochondrial association with dendritic spines may play an important role in supporting AMPAR presence on or trafficking to the postsynaptic membrane. Aβ disruption of mitochondrial trafficking could contribute to AMPAR removal and trafficking defects leading to synaptic inhibition.</p

Selectively enhanced expression of prophenoloxidase activating enzyme 1 (PPAE1) at a bacteria clearance site in the white shrimp, Litopenaeus vannamei

<p>Abstract</p> <p>Background</p> <p>The prophenoloxidase-activating (PO activating) system plays an important role in the crustacean innate immunity, particularly in wound healing and pathogen defense. A key member of this system is prophenoloxidase-activating enzyme (PPAE), which is the direct activator of prophenoloxidase (proPO). Despite their importance in crustacean PO activating system, the studies on them remain limited.</p> <p>Results</p> <p>Here we report on a PPAE of white shrimp, <it>Litopenaeus vannamei </it>(lvPPAE1), which showed 94% similarity to PPAE1 of <it>Penaeus monodon</it>. We found that lvPPAE1 in fluid hemocytes was down regulated after challenge by <it>Vibrio harveyi </it>but was enhanced when shrimps were exposed to a bacteria-rich environment for long-term. In <it>vivo </it>gene silence of lvPPAE1 by RNAi can significantly reduce the phenoloxidase activity (PO) and increase the susceptibility of shrimps to <it>V. harveyi</it>. Although lvPPAE1 was down-regulated in fluid hemocytes by <it>Vibrio </it>challenge, its expression increased significantly in gill after bacteria injection, which is the primary bacteria-clearance tissue.</p> <p>Conclusion</p> <p>Suppressed expression in fluid hemocytes and enhanced expression in gill indicates selectively enhanced expression at the bacterial clearance site. This is a novel feature for PPAE expression. The results will contribute to our understanding of the PO activating system in crustaceans.</p