scholarly journals Consolidated EHR Workflow for Endoscopy Quality Reporting

2021 ◽  
Author(s):  
Shorabuddin Syed ◽  
Benjamin Tharian ◽  
Hafsa Bareen Syeda ◽  
Meredith Zozus ◽  
Melody L. Greer ◽  
...  
Keyword(s):  
Language Processing ◽  
Data Extraction ◽  
University Of Arkansas ◽  
Quality Metric ◽  
Quality Reporting ◽  
Standardized Reporting ◽  
Colonoscopy Quality ◽  
The University ◽  
Extraction Error

Although colonoscopy is the most frequently performed endoscopic procedure, the lack of standardized reporting is impeding clinical and translational research. Inadequacies in data extraction from the raw, unstructured text in electronic health records (EHR) pose an additional challenge to procedure quality metric reporting, as vital details related to the procedure are stored in disparate documents. Currently, there is no EHR workflow that links these documents to the specific colonoscopy procedure, making the process of data extraction error prone. We hypothesize that extracting comprehensive colonoscopy quality metrics from consolidated procedure documents using computational linguistic techniques, and integrating it with discrete EHR data can improve quality of screening and cancer detection rate. As a first step, we developed an algorithm that links colonoscopy, pathology and imaging documents by analyzing the chronology of various orders placed relative to the colonoscopy procedure. The algorithm was installed and validated at the University of Arkansas for Medical Sciences (UAMS). The proposed algorithm in conjunction with Natural Language Processing (NLP) techniques can overcome current limitations of manual data abstraction.

A natural language processing algorithm to measure quality prostate cancer care.

2017 ◽  
Vol 35 (8_suppl) ◽  
pp. 232-232 ◽  
Author(s):  
Tina Hernandez-Boussard ◽  
Panagiotis Kourdis ◽  
Rajendra Dulal ◽  
Michelle Ferrari ◽  
Solomon Henry ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Specific Antigen ◽  
Quality Metrics ◽  
Free Text ◽  
Data Mining Algorithm ◽  
Quality Metric ◽  
Quality Reporting

232 Background: Electronic health records (EHRs) are a widely adopted but underutilized source of data for systematic assessment of healthcare quality. Barriers for use of this data source include its vast complexity, lack of structure, and the lack of use of standardized vocabulary and terminology by clinicians. This project aims to develop generalizable algorithms to extract useful knowledge regarding prostate cancer quality metrics from EHRs. Methods: We used EHR ICD-9/10 codes to identify prostate cancer patients receiving care at our academic medical center. Patients were confirmed in the California Cancer Registry (CCR), which provided data on tumor characteristics, treatment data, treatment outcomes and survival. We focused on three potential pretreatment process quality measures, which included documentation within 6 months prior to initial treatment of prostate-specific antigen (PSA), digital rectal exam (DRE) performance, and Gleason score. Each quality metric was defined using target terms and concepts to extract from the EHRs. Terms were mapped to a standardized medical vocabulary or ontology, enabling us to represent the metric elements by a concept domain and its permissible values. The structured representation of the quality metric included rules that accounted for the temporal order of the metric components. Our algorithms used natural language processing for free text annotation and negation, to ensure terms such as ‘DRE deferred’ are appropriately categorized. Results: We identified 2,123 patients receiving prostate cancer treatment between 2008-2016, of whom 1413 (67%) were matched in the CCR. We compared accuracy of our data mining algorithm, a random sample of manual chart review, and the CCR. (See Table.) Conclusions: EHR systems can be used to assess and report quality metrics systematically, efficiently, and with high accuracy. The development of such systems can improve and reduce the burden of quality reporting and potentially reduce costs of measuring quality metrics through automation. [Table: see text]

scholarly journals Quality metrics of generative models

2021 ◽  
Author(s):  
Kirill Igorevich Abrosimov ◽  
Tatiana Vladimirovna Lvutina ◽  
Anna Sergeyevna Surkova
Keyword(s):  
Language Processing ◽  
Deep Neural Networks ◽  
Pairwise Comparison ◽  
Generative Models ◽  
Baseline Model ◽  
Quality Metric ◽  
Subjective Assessments ◽  
Music Generation ◽  
Subjective Estimates

Within the framework of this article, modern metrics for evaluating generative models are considered. Particular attention is paid to metrics that are used in the field of natural language processing - BLUE (evaluates quality based on a comparison of the result obtained by a model and a person), NIST (based on the BLUE metric), METEOR (based on the harmonic mean of unigrams of accuracy and completeness), ROUGE (. The article presents a new metric, which is based on subjective assessments. The subjective estimates used in the considered metric are collected using pairwise comparison in the form of evaluation scales. The article also proposes an algorithm for generating music based on automatic models of working with ABC notation, models of distributive semantics and generative models of deep neural networks - Transformers. The new quality metric (SS-metric) presented in the article is used to assess the quality of the proposed algorithm for generating music in comparison with the solutions offered by humans and baseline models. Music generation based on the baseline model builds a continuation of a musical fragment by randomly selecting bars from the first half of the musical fragment. During the experiments, it was found out that the SS-metric allows you to formalize and generalize subjective assessments, this can be used to assess the quality of various objects.

scholarly journals Editor's Note

2018 ◽  
Vol 1 (3) ◽  
Author(s):  
Alan Johns
Keyword(s):  
Lessons Learned ◽  
Washington Dc ◽  
University Of Arkansas ◽  
Medical Sciences ◽  
Regional Campuses ◽  
Regional Medical ◽  
Regional Campus ◽  
The University

We are happy to publish our third issue of the Journal of Regional Medical Campuses. The response we have received has been excellent, both in numbers and quality of submissions. Our editorial board continues to meet regularly to discuss suggestions from our readers and future plans. Please continue to pass the word of our journal to your colleagues on our regional campuses.   I would like to acknowledge the article “Lessons learned through a partnership with Marshallese faith-based organizations to screen for hypertension and diabetes” by Dr. Pearl McElfish from the University of Arkansas for Medical Sciences Northwest Regional Campus. The program she describes was the winner of the 2017 AAMC Regional Medical Campus Star of Community Achievement Award. This award was presented at the GRMC Spring meeting in Washington, DC.   Alan Johns, MD, MEd Co-Editor, Journal of Regional Medical Campuses

scholarly journals The Quality of Colonoscopy Reporting in Usual Practice: Are Endoscopists Reporting Key Data Elements?

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
S. D. Hadlock ◽  
N. Liu ◽  
M. Bernstein ◽  
M. Gould ◽  
L. Rabeneck ◽  
...  
Keyword(s):  
Open Access ◽  
Poor Quality ◽  
High Quality ◽  
Usual Practice ◽  
Quality Reporting ◽  
Targeted Interventions ◽  
Factors Associated ◽  
Data Elements ◽  
Colonoscopy Quality

Background. High quality reporting of endoscopic procedures is critical to the implementation of colonoscopy quality assurance programs.Objective. The aim of our research was to (1) determine the quality of colonoscopy (CS) reporting in “usual practice,” (2) identify factors associated with good quality reporting, and (3) compare CS reporting in open-access and non-open-access procedures.Methods. 557 CS reports were randomly selected and assigned a score based on the number of mandatory data elements included in the report. Reports documenting greater than 70% of the mandatory data elements were considered to be of good quality. Physician and procedure factors associated with good quality CS reporting were identified.Results. Variables that were consistently well documented included date of the procedure (99.6%), procedure indication (88.9%), a description of the most proximal anatomical segment reached (98.6%), and documentation of polyp location (97.8%). Approximately 79.4% of the reports were considered to be of good quality. Gastroenterology specialty, lower annual CS volume, and fewer years in practice were associated with good quality reporting.Discussion. CS reporting in usual practice in Ontario lacks quality in several areas. Almost 1 in 5 reports was of poor quality in our study.Conclusions. Targeted interventions and/or use of mandatory fields in synoptic reports should be considered to improve CS reporting.

Rice yield and quality as affected by cultivar and red rice (Oryza sativa) density

Weed Science ◽  
2005 ◽  
Vol 53 (4) ◽  
pp. 499-504 ◽  
Author(s):  
Brian V. Ottis ◽  
Kenneth L. Smith ◽  
Robert C. Scott ◽  
Ronald E. Talbert
Keyword(s):  
Rice Yield ◽  
Field Studies ◽  
Rice Cultivars ◽  
University Of Arkansas ◽  
Red Rice ◽  
Cultivated Rice ◽  
Yield And Quality ◽  
The University ◽  
Extension Center

Previous research has examined the extent to which red rice affects both yield and grain quality of cultivated rice. However, this research was conducted over 15 yr ago. Modern long-grain rice cultivars have the potential to produce yields above 10,000 kg ha−1; however, it is unknown whether modern rice cultivars sacrifice competitiveness to achieve higher yields, or if, in fact, they are more competitive. Field studies were conducted in 2002 and 2003 at the Southeast Research and Extension Center near Rohwer, AR, and at the University of Arkansas Pine Bluff Research Farm near Lonoke, AR, to investigate the effect of red rice density on interference between red rice and five rice cultivars (‘CL161’, ‘Cocodrie’, ‘LaGrue’, ‘Lemont’, and ‘XL8’). White rice yield reductions were between 100 and 755 kg ha−1for every red rice plant m−2. The hybrid rice, XL8, had higher yields than the conventional cultivars. Red rice contamination in milling samples increased linearly as a function of red rice density at Lonoke and Rohwer in 2003. Dockage for each cultivar was calculated on the basis of the relationship between red rice density and red rice contamination. Semidwarf Lemont was the most contaminated and hybrid XL8 the least contaminated by the various densities of red rice.

scholarly journals Enhance Portable Radiograph for Fast and High Accurate COVID-19 Monitoring

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1080
Author(s):  
Ngan Le ◽  
James Sorensen ◽  
Toan Bui ◽  
Arabinda Choudhary ◽  
Khoa Luu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Treatment Process ◽  
University Of Arkansas ◽  
Medical Sciences ◽  
The Subject ◽  
Subject Pair ◽  
The University ◽  
Learning Frameworks ◽  
Speed And Accuracy

This work aimed to assist physicians by improving their speed and diagnostic accuracy when interpreting portable CXRs as well as monitoring the treatment process to see whether a patient is improving or deteriorating with treatment. These objectives are in especially high demand in the setting of the ongoing COVID-19 pandemic. With the recent progress in the development of artificial intelligence (AI), we introduce new deep learning frameworks to align and enhance the quality of portable CXRs to be more consistent, and to more closely match higher quality conventional CXRs. These enhanced portable CXRs can then help the doctors provide faster and more accurate diagnosis and treatment planning. The contributions of this work are four-fold. Firstly, a new database collection of subject-pair radiographs is introduced. For each subject, we collected a pair of samples from both portable and conventional machines. Secondly, a new deep learning approach is presented to align the subject-pairs dataset to obtain a pixel-pairs dataset. Thirdly, a new PairFlow approach is presented, an end-to-end invertible transfer deep learning method, to enhance the degraded quality of portable CXRs. Finally, the performance of the proposed system is evaluated by UAMS doctors in terms of both image quality and topological properties. This work was undertaken in collaboration with the Department of Radiology at the University of Arkansas for Medical Sciences (UAMS) to enhance portable/mobile COVID-19 CXRs, to improve the speed and accuracy of portable CXR images and aid in urgent COVID-19 diagnosis, monitoring and treatment.

Epistemic and relational conflicts in sharing identical vs. complementary information during cooperative learning

2002 ◽  
Vol 61 (3) ◽  
pp. 139-151 ◽  
Author(s):  
Céline Darnon ◽  
Céline Buchs ◽  
Fabrizio Butera
Keyword(s):  
Cooperative Learning ◽  
Social Comparison ◽  
Positive Relationship ◽  
Learning Task ◽  
Control Group ◽  
Complementary Information ◽  
Relational Conflict ◽  
The Social ◽  
The University

When interacting on a learning task, which is typical of several academic situations, individuals may experience two different motives: Understanding the problem, or showing their competences. When a conflict (confrontation of divergent propositions) emerges from this interaction, it can be solved either in an epistemic way (focused on the task) or in a relational way (focused on the social comparison of competences). The latter is believed to be detrimental for learning. Moreover, research on cooperative learning shows that when they share identical information, partners are led to compare to each other, and are less encouraged to cooperate than when they share complementary information. An epistemic vs. relational conflict vs. no conflict was provoked in dyads composed by a participant and a confederate, working either on identical or on complementary information (N = 122). Results showed that, if relational and epistemic conflicts both entailed more perceived interactions and divergence than the control group, only relational conflict entailed more perceived comparison activities and a less positive relationship than the control group. Epistemic conflict resulted in a more positive perceived relationship than the control group. As far as performance is concerned, relational conflict led to a worse learning than epistemic conflict, and - after a delay - than the control group. An interaction between the two variables on delayed performance showed that epistemic and relational conflicts were different only when working with complementary information. This study shows the importance of the quality of relationship when sharing information during cooperative learning, a crucial factor to be taken into account when planning educational settings at the university.

Evaluation of the Academic Setting in Spain 1This study was financed by the University of Oviedo.

1995 ◽  
Vol 11 (2) ◽  
pp. 133-137 ◽  
Author(s):  
Juan Fernández ◽  
Miguel A. Mateo ◽  
José Muñiz
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Social Climate ◽  
Three Dimensions ◽  
Academic Setting ◽  
Teaching And Research ◽  
Evaluation Questionnaire ◽  
University Teachers ◽  
The University

The conditions are investigated in which Spanish university teachers carry out their teaching and research functions. 655 teachers from the University of Oviedo took part in this study by completing the Academic Setting Evaluation Questionnaire (ASEQ). Of the three dimensions assessed in the ASEQ, Satisfaction received the lowest ratings, Social Climate was rated higher, and Relations with students was rated the highest. These results are similar to those found in two studies carried out in the academic years 1986/87 and 1989/90. Their relevance for higher education is twofold because these data can be used as a complement of those obtained by means of students' opinions, and the crossing of both types of data can facilitate decision making in order to improve the quality of the work (teaching and research) of the university institutions.

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